Applied Science, Faculty of
Mining Engineering, Keevil Institute of
DSpace
UBCV
Kuyuk, Ali Fahrettin
Doctor of Philosophy - PhD
University of British Columbia
Depletion of shallower resources is challenging modern mining industry to reach deeper deposits to maintain global mineral and raw material demand. As mines grow deeper and get more complex, heat loads associated with production, air compression and geology become an important issue for the health and safety of underground workers. For this purpose, mining industry uses water-based bulk-air spray cooling systems. However, design of these systems is reliant on semi-empirical models that were developed based on restricted empirical data collected from limited chamber size and geometry. Therefore, they often fail to respond when the granularity of the operating parameters such as droplet size, air velocity or chamber geometry is challenged by the application. To mitigate this issue, numerical models can be used effectively. Nevertheless, mining literature is missing an extensive study of these cooling chambers with numerical methods. Moreover, mine air conditioning systems are energy intensive, and their energy performance is highly related to their design. To fill this gap, first, the energy problem associated with conventional mine cooling systems were raised and examined with different case studies. These case studies have shown that, there are alternative cooling solutions for deep-mines, and they could offer similar cooling performances with relatively less capital investment. Then, a fully coupled numerical model that can replace conventional design methods was introduced and validated with series of lab experiments. Literature survey done on similar concepts with applicable scales has shown that earlier studies are mainly investigating evaporative cooling of spray cooling systems without offering a relevant condensation criterion to capture the two-way multiphase physics seen in bulk-air cooling applications. In these terms, this study provided a more inclusive approach by introducing a saturation criterion to Lee’s mass transfer model used in multiphase modelling. Finally, once validated with experimentation, the lab-scale solution was scaled up to an industry compliant, full-scale, multistage bulk air cooler model to benchmark the conventional methods. The studies have shown that; numerical models presented here agreed with the experimental work and semi-empirical models within a reasonable error and promised higher granularity when it comes to understanding the system merit and effectiveness.
NUMERICAL AND EXPERIMENTAL STUDY OF THE PERFORMANCE OF BULK-AIR SPRAY COOLERS AND RENEWABLE COOLING SYSTEMS FOR APPLICATION IN MINE VENTILATION by Ali Fahrettin Kuyuk B.Sc., Middle East Technical University, 2012 M.Eng., The University of British Columbia, 2014 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Mining Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2022 © Ali Fahrettin Kuyuk, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Numerical and Experimental Study of the Performance of Bulk-Air Spray Coolers and Renewable Cooling Systems for Application in Mine Ventilation submitted by Ali Fahrettin Kuyuk in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mining Engineering Examining Committee: Seyed Ali Ghoreishi-Madiseh, Assistant Professor, Mining Engineering, UBC Supervisor Ilija Miskovic, Assistant Professor, Mining Engineering, UBC Supervisory Committee Member Nadja Kunz, Assistant Professor, School of Public Policy and Global Affairs, UBC Supervisory Committee Member Xiaotao Bi, Professor, Chemical and Biological Engineering, UBC University Examiner Wenying Liu, Associate Professor, Department of Materials Engineering, UBC University Examiner iii ABSTRACT Depletion of shallower resources is challenging modern mining industry to reach deeper deposits to maintain global mineral and raw material demand. As mines grow deeper and get more complex, heat loads associated with production, air compression and geology become an important issue for the health and safety of underground workers. For this purpose, mining industry uses water-based bulk-air spray cooling systems. However, design of these systems is reliant on semi-empirical models that were developed based on restricted empirical data collected from limited chamber size and geometry. Therefore, they often fail to respond when the granularity of the operating parameters such as droplet size, air velocity or chamber geometry is challenged by the application. To mitigate this issue, numerical models can be used effectively. Nevertheless, mining literature is missing an extensive study of these cooling chambers with numerical methods. Moreover, mine air conditioning systems are energy intensive, and their energy performance is highly related to their design. To fill this gap, first, the energy problem associated with conventional mine cooling systems were raised and examined with different case studies. These case studies have shown that, there are alternative cooling solutions for deep-mines, and they could offer similar cooling performances with relatively less capital investment. Then, a fully coupled numerical model that can replace conventional design methods was introduced and validated with series of lab experiments. Literature survey done on similar concepts with applicable scales has shown that earlier studies are mainly investigating evaporative cooling of spray cooling systems without offering a relevant condensation criterion to capture the two-way multiphase physics seen in bulk-air cooling applications. In these terms, this study provided a more inclusive approach by introducing a saturation criterion to Lee’s mass iv transfer model used in multiphase modelling. Finally, once validated with experimentation, the lab-scale solution was scaled up to an industry compliant, full-scale, multistage bulk air cooler model to benchmark the conventional methods. The studies have shown that; numerical models presented here agreed with the experimental work and semi-empirical models within a reasonable error and promised higher granularity when it comes to understanding the system merit and effectiveness. v LAY SUMMARY Mining operations are energy intensive and some specific operations like deep underground mines may require costly systems to maintain proper health and safety standards. Mine cooling is one of these concepts and often employed to mitigate heat loads in subsurface mines to ensure comfortable ambient conditions for workers. However, design of mine cooling systems is complex due to the nature of multiphase physics in cooling chambers. Earlier models found in mining literature offer relatively high-level design guidelines and often fail to respond changes in granular details. This study not only provides detailed case studies exploring alternative mine cooling techniques that might ease the financial burden of mine cooling systems, but it also provides an experimentally validated, industry compliant multiphase fluid dynamics and heat transfer model to better capture the performance of these systems. vi PREFACE Chapter 3 will be covering published case studies based on a real-life, anonymous Canadian mine. Some of the identifying information is removed from the context intentionally as the relevant project details and associated results are protected with a ‘Non-Disclosure Agreement’ signed between involved parties. Chapter is written by me and following details apply as indicated: Section 3.2 is based on publications (1) and (2) below. These studies were exploring the performance assessment and feasibility of integration of a lake sourced cooling system for a deep mine cooling project. In these studies, I conducted the cooling system design, engineering work of cooling plants and the heat load analysis of the mine. All the figures and tables in this section were created by me. Ghoreishi-Madiseh SA, Sasmito AP were the main supervisors of the project and they involved with conceptualization and supervision of all engineering designs. Amiri L and Templeton J were collaborating researchers and they contributed the project with studies of hydraulic energy recovery, pipeline specifications and lake temperature probing. Hassani FP was the head of the research group and provided high level supervision to the project. (1) A.F. Kuyuk, S.A. Ghoreishi-Madiseh, A.P. Sasmito & F.P. Hassani. (2019). Designing A Large-scale Lake Cooling System for an Ultra-deep mine: A Canadian case Study. Energies. (2) A.F. Kuyuk, S.A. Ghoreishi-Madiseh, F.P. Hassani, A.P. Sasmito, L. Amiri and J. Templeton. (2019). Performance and economic assessment of large-scale deep-lake cooling systems: A Canadian Example. Energy Procedia. ICAE. August 2018. Hong Kong. Section 3.3 is based on publication (3) below. This study was a part of the studies (1) and (2). The work was conceptualized by me and Ghoreishi-Madiseh SA. I created all the figures and tables of vii this section. Also, all the engineering analysis and calculations are done by me and supervised by Ghoreishi-Madiseh SA. Hassani FP provided high level supervision to this publication. (3) A.F. Kuyuk, S.A. Ghoreishi-Madiseh and F.P. Hassani, Closed-loop bulk air conditioning: A renewable energy-based system for deep mines in arctic regions. International Journal of Mining Science and Technology. July 2020. Section 3.4 is based on publication (4) below. This study was conceptualized by me, Ghoreishi-Madiseh SA, Sasmito AP and Kalantari H. In this study, I conducted the solar irradiation studies and generic design of the renewable system integration. Kalantari H was a collaborating researcher and he contributed with research and financial analysis of the renewable systems. Ghoreishi-Madiseh and Sasmito AP were the main supervisors of the project. (4) S.A. Ghoreishi-Madiseh, A.F. Kuyuk, H. Kalantari and A.P. Sasmito. (2019). Ice versus battery storage; a case for integration of renewable energy in refrigeration systems of remote sites. Energy Procedia. REM. September 2018. Rhodes, Greece. Although, mentioned sections of Chapter 3 was based on the indicated publications, none of the text of the present dissertation is directly taken from the mentioned sources and figures and tables are cited wherever needed. viii TABLE OF CONTENTS ABSTRACT… ..................................................................................................................................... iii LAY SUMMARY ................................................................................................................................. v PREFACE….. ..................................................................................................................................... vi TABLE OF CONTENTS..................................................................................................................... viii LIST OF TABLES ............................................................................................................................... xii LIST OF FIGURES ............................................................................................................................ xiv ACKNOWLEDGEMENTS ............................................................................................................... xviii DEDICATION ................................................................................................................................... xx 1. Introduction .................................................................................................................. 1 1.1. Problem Statement ............................................................................................... 5 1.2. Thesis Objectives ................................................................................................. 10 1.3. Methodology ...................................................................................................... 12 1.3.1. Rationale of the Methods ........................................................................... 13 1.4. Limitations of the Thesis .................................................................................... 21 1.5. General Organization of the Thesis .................................................................... 23 2. Literature Review ....................................................................................................... 24 2.1. Mine Heating, Ventilation and Air Conditioning (HVAC) ................................... 24 2.2. Challenges Faced in Mine HVAC Industry .......................................................... 24 2.3. Impact of Increasing Depth on Mine Ventilation and Refrigeration .................. 26 2.4. Underground Mine Cooling Techniques ............................................................ 28 2.4.1. Conventional Spray Cooling Systems .......................................................... 29 2.4.2. Closed-Circuit (Indirect Coolers) ................................................................. 36 ix 2.4.3. Ice Cooling Plants ......................................................................................... 37 2.4.4. Alternative / Novel Cooling Techniques ....................................................... 40 2.4.4.1. Micro-Climate Options ......................................................................... 40 2.4.4.2. Shaft or Stope Freezing ......................................................................... 40 2.4.4.3. Natural Cooling ..................................................................................... 42 2.4.4.4. Lake Cooling .......................................................................................... 43 2.5. Introduction to Conventional Spray Cooling Plant Design ................................. 43 2.5.1. Semi-Empirical Model for Designing Full-Scale Bulk Air Coolers ................. 46 2.5.2. Full-Scale Bulk-Air-Cooler Design Recommendations .................................. 52 2.5.2.1. Distribution of Air and Water in the Chamber Cavity .......................... 52 2.5.2.2. Distribution of Nozzles .......................................................................... 53 2.5.2.3. Staging within the Chamber ................................................................. 55 2.5.2.4. Droplet Size ........................................................................................... 56 2.5.2.5. Summary of the Conventional BAC Design Methods ........................... 56 3. Introduction to Case Studies and Originality of the Work ......................................... 58 3.1. Case Studies: A Canadian Example ..................................................................... 58 3.1.1. General Motivation ...................................................................................... 59 3.2. 1st Case Study: Lake Cooling ............................................................................... 60 3.2.1. Working Principles of a Lake Cooling System .............................................. 61 3.2.2. Mode of Coolth Delivery from Lake to Mine ............................................... 62 3.2.3. Mine Properties and Calculating the Cooling Load ...................................... 64 3.2.4. Lake Water Thermal Study ........................................................................... 69 3.2.5. Important Highlights Regarding Conventional Designs ............................... 73 3.2.6. Plant Sizing and Design Process ................................................................... 74 x 3.2.6.1. Surface Plant Design and Relevant Sensitivity Studies ......................... 74 3.2.6.2. Underground Plant Design and Relevant Sensitivity Studies ............... 78 3.2.7. Hydraulic Energy Recovery System .............................................................. 84 3.2.8. Financial Breakdown of Conv. Refrigeration vs. Lake Cooling System ........ 85 3.3. 2nd Case Study: Reutilization of the Ret. Water of Underg. Cooling Plant ......... 87 3.3.1. Mine Bulk Air Heating System (or Reverse BAC) .......................................... 88 3.4. 3rd Case Study: Plant Downsizing with Solar Panels (Ice Storage for Cooling) .. 95 4. Lab-Scale and Full-scale Experimental and Numerical Studies ................................ 103 4.1. Introduction to Experiments and the Lab-Scale Numerical Model.................. 103 4.2. Components of the Experimental Setup .......................................................... 108 4.3. Rationale of the Experiments and the Difference from Other Lab Studies ..... 111 4.4. Test Preparation ............................................................................................... 112 4.5. Instrumentation and Experimentation ............................................................ 114 4.5.1. Air Provision to the Unit and Temperature Control ................................... 114 4.5.2. Injections and Nozzle Arrangements ......................................................... 114 4.6. Developing the Numerical Model: Multiphase Modeling Approach ............... 117 4.6.1. Mass Transfer Mechanisms and the Continuity of the Model .................. 117 4.6.2. Boundary Conditions .................................................................................. 119 4.6.3. Phases in the Numerical Domain ............................................................... 120 4.6.4. Governing Equations for the Mixture Model ............................................. 121 4.6.5. Mass Transfer Criterion and Lee’s Model .................................................. 123 4.6.6. Droplet and Specie Settings ....................................................................... 125 4.7. Experiments ...................................................................................................... 129 4.7.1. Procedure ................................................................................................... 130 xi 4.7.2. Data ............................................................................................................ 133 4.8. Lab-Scale Model Validation .............................................................................. 138 4.8.1. Summary of Numerical Simulations and Example Cases ........................... 139 4.8.2. Lab-Scale Numerical Results and Discussion.............................................. 150 4.8.3. Lab-Scale Model Grid Sensitivity Study ...................................................... 155 4.9. Introduction to Full-scale Model ...................................................................... 156 4.9.1. Model Geometry ........................................................................................ 157 4.10. Changes Introduced to the Model for Upscaling ........................................... 159 4.11. Boundary Conditions of Full-Scale Model ...................................................... 162 4.12. Full-Scale Cooling Case Study ......................................................................... 162 4.13. Full-Scale Model Validation: Results and Discussion ..................................... 164 4.13.1. Comparison of all Solutions...................................................................... 165 4.13.2. Grid Sensitivity Study for Full-Scale Solution ........................................... 173 4.14. Implications of Changing Droplet Size on Merit Factor ................................. 174 5. Summary, Recommendations and Future Work ...................................................... 176 5.1. Summary........................................................................................................... 176 5.2. Future Work and Recommendations ............................................................... 178 BIBLIOGRAPHY ............................................................................................................................ 180 xii LIST OF TABLES Table 1 - Heat Sources in an Operating Underground Mine .......................................................... 6 Table 2 - Recommended Underground Working Temperatures Suggested by ASHRAE ............... 7 Table 3 - Under-utilization of Conventional Cooling Plants in Some Anonymous Examples ....... 10 Table 4 - Physical Parameters That Impact Spray Cooling Performance ..................................... 18 Table 5 - General Organization of the Thesis ................................................................................ 23 Table 6 - Cooling Plant Selection Matrix ....................................................................................... 35 Table 7 - Standardized Relationship Between Number of Stages and Merit for a BAC System .. 51 Table 8 - Mine operational parameters used in the ventilation network simulation ................. 67 Table 9 - Summary of the Heat Added to Network by the Mine Fleet and Auxiliary Equipment 67 Table 10 - Optimum Operational Conditions for Selected BAC Plants ......................................... 68 Table 11 - Pipeline Specifications and Heat Loss/Gain Study ....................................................... 72 Table 12 - Typical Merit Factors for Different Applications (After McPherson) ........................... 75 Table 13 - Operational Parameters of the Selected Surface Plant ............................................... 76 Table 14 - Operational Parameters of the Underground Plant .................................................... 79 Table 15 - CAPEX Breakdown of the Lake Cooling Project vs. Conventional Plant Installation ... 86 Table 16 - A Summary of the Op. Parameters Set for the Bulk-Air-Heating System Design ........ 91 Table 17 - Volumetric Calculations for the Natural Gas Backed-up System ................................. 93 Table 18 - Volumetric Calculations for the Conventional Gas Burner System (only) ................... 93 Table 19 - Local Solar Irradiation Data Collected from NRCAN .................................................. 100 Table 20 - Parameters Used in the Developed Cost Model ........................................................ 101 Table 21 – Finan. Matrix Showing Each Cap Line and Res. CAPEX Invest. and Payback Per. ..... 102 xiii Table 22 - Technical Details of the Nozzle Used in the Experiments .......................................... 115 Table 23 - Mass Flow and Pressure Classification ...................................................................... 116 Table 24 – Experimental Data ..................................................................................................... 133 Table 25 - RUN#4 and RUN#7 Cooling Performance Comparison.............................................. 145 Table 26 - Numerical Results ...................................................................................................... 150 Table 27 - Injection Settings Used During the Full-Scale Solution .............................................. 161 Table 28 - Rosin-Rammler Distribution (RRD) Summary ............................................................ 161 Table 29 - Inlet Conditions of the Case Study ............................................................................. 163 Table 30 - Bluhm et al.'s Solution Results ................................................................................... 164 Table 31 - Sigma Heat Comparison of All Cases and Inlet Outlet Summary of All Solutions ..... 173 Table 32 - Mesh Statistics Used in the Mesh Sensitivity Study .................................................. 173 xiv LIST OF FIGURES Figure 1 - Modern Mine Cooling Techniques Applied Based on Depth of Mines ........................ 27 Figure 2 - Thermodynamic Process of Refrigeration Cycle Used in Conventional Cooling Plants 29 Figure 3 - A Typical Multistage Cooling Chamber ......................................................................... 30 Figure 4 - Underground BAC at La Ronde Mine ............................................................................ 31 Figure 5 - A Surface BAC with Cooling Towers and Water Treatment Facility ............................. 32 Figure 6 - Hydro-powered, Closed-circuit Spot Coolers ............................................................... 37 Figure 7 - Examples of Ice Types Used in Mine Cooling ............................................................... 39 Figure 8 - Working Principles of Ice Stope .................................................................................... 41 Figure 9 - An Image from the Ice Stopes Used in Kidd Mine ........................................................ 42 Figure 10 - Sketch Showing the Natural Cooling / Heating System Used in Creighton Mine ...... 43 Figure 11 - A Typical Nozzle Arrangement and Water Distribution as Explained ........................ 53 Figure 12 - A Simp. Graphical Rep. of Multi-Staging for a 2.5 Stage Cooling Chamber ............... 55 Figure 13 - Thermal Stratification in a Lake .................................................................................. 61 Figure 14 - Different Combinations of Water Looping Scenarios Applicable to Mining Settings 63 Figure 15 - Life of Mine Production Schedule of the Mine ........................................................... 65 Figure 16 - Annual Temperature Distribution at the Mine Site .................................................... 66 Figure 17 - Effect of Auto-Compression ........................................................................................ 69 Figure 18 - Lake Temperature Study Data Obtained from 9 Locations ........................................ 70 Figure 19 - Probe Locations and Their Linear Distances from the Shoreline ............................... 71 Figure 20 - Topographical Profile Cut out From Satellite Image .................................................. 72 xv Figure 21 - Sensitivity Study Over the Water Delivery Temperature Showing Maximum Attainable Cooling Power and Maximum Tolerable Ambient Temperature at the Mine Site for the Given Design ........................................................................................................................................... 77 Figure 22 - Sensitivity Study Showing Cooling Power and Lake Flow Rate .................................. 78 Figure 23 - Sensitivity Study Over the Water Delivery Temperature Showing Maximum Attainable Cooling Power and Maximum Tolerable Ambient Temperature for the Underground BAC ...... 81 Figure 24 - Sensitivity Study Over the Water Mass Flow Rate Showing Maximum Attainabe Cooling, Power and Maximum Outlet Temperature for the UG BAC ........................................... 82 Figure 25 - Overall Water-Flow vs. Water Temperature Curve for the Given Design Criteria ..... 84 Figure 26 - Heat Exchanging Loop between the Underground Cooling Plant and the Surface Bulk Air Heater During the Mine Heating Days .................................................................................... 90 Figure 27 - Parametric Sensitivity Study on Operational Parameters .......................................... 94 Figure 28 - Cap Shaving Lines Bringing Plant Design Criteria to a Lower Temperature ............... 96 Figure 29 - Detailed Temperature Analysis of the Month of July ................................................. 97 Figure 30 - Proposed System Flow Diagram Including Two Storage Options .............................. 98 Figure 31 - Cap Lines and Corresponding Plant Size and Ice Generation Requirement ............. 100 Figure 32 - A representation of conventional bulk-air-spray cooling ......................................... 104 Figure 33 - 3-D Sketch of the Lab Experimental Setup ............................................................... 105 Figure 34 - Lab Experimental Setup Dimensions (1) ................................................................... 106 Figure 35 - Lab Experimental Setup Dimensions (2) ................................................................... 107 Figure 36 - Lab Experimental Setup (Actual) .............................................................................. 110 Figure 37 - Human-Machine Interface of the Lab Equipment .................................................... 110 xvi Figure 38 - Outlet Thermocouple Locations (T2-T10) ................................................................. 111 Figure 39 - Test Preparation ....................................................................................................... 113 Figure 40 - Model Coupling in Fluent .......................................................................................... 118 Figure 41 - Injection Positioning and the Domain for Type A Injections .................................... 126 Figure 42 - Droplet Size Distributions in Accordance with Injection Settings ............................ 127 Figure 43 - Ice Storage for the Experimental Process ................................................................. 130 Figure 44 – Sensor Locations and Parts of the Experimental Unit ............................................. 132 Figure 45 – Example RUN #10 Data Plots ................................................................................... 135 Figure 46 – Example RUN #11 Data Plots ................................................................................... 136 Figure 47 – Example RUN #16 Data Plots ................................................................................... 137 Figure 48 - Horizontal Central Plane Temperature Contour Plot of RUN#10 at Steady State ... 139 Figure 49 - Vertical Central Plane Temperature Contour Plot of RUN#10 at Steady State ........ 140 Figure 50 - Outlet Temperature Contour Plot of RUN#10 at Steady State ................................ 140 Figure 51 - Droplet Temperature Distribution of RUN #10 at Steady State ............................... 141 Figure 52 - RUN #10 Experimentation ........................................................................................ 141 Figure 53 - Horizontal Central Plane Temperature Contour Plot of RUN#3 at Steady State ..... 142 Figure 54 - Vertical Central Plane Temperature Contour Plot of RUN#3 at Steady State .......... 143 Figure 55 - Outlet Temperature Contour Plot of RUN#10 at Steady State ................................ 143 Figure 56 - Droplet Temperature Distribution of RUN #3 at Steady State ................................. 144 Figure 57 - Images of Different Injection Settings for Type A injection ..................................... 144 Figure 58 - Vapor Fraction Distribution within the Chamber ..................................................... 146 Figure 59 - Horizontal Central Plane Temperature Contour Plot of RUN#8 at Steady state...... 148 xvii Figure 60 - Vertical Central Plane Temperature Contour Plot of RUN#8 at Steady State .......... 149 Figure 61 - Droplet Temperature Distribution of RUN #8 at Steady State (Type B Injection).... 149 Figure 62 - Outlet Average Static Temperature Comparison ..................................................... 151 Figure 63 - Outlet Average Relative Humidity Comparison ........................................................ 152 Figure 64 - Outlet Average Enthalpy Comparison ...................................................................... 153 Figure 65 - Outlet Average Water Temperature Comparison .................................................... 154 Figure 66 - Grid Sensitivity Study and Relative Error Curve ....................................................... 155 Figure 67 - Geometry of the Computational Domain Developed for Full-Scale Model ............. 158 Figure 68 - Fine (0.05 m) Mesh of the Domain ........................................................................... 158 Figure 69 - A Representative 3D View of the Solution Domain and Injection Points ................. 160 Figure 70 - Static Temp. Comp. of the Numerical Model with the Semi-empirical Solution ..... 166 Figure 71 - Static Temperature Distribution within the BAC ...................................................... 166 Figure 72 - Relative Humidity Comparison for All Cases ............................................................ 168 Figure 73 – Average Relative Humidity Change across the Chamber Length ............................ 169 Figure 74 - Transient Behavior of Vapor Mass Fraction During Bulk-Air-Cooling ...................... 170 Figure 75 - Vapor Mass Fraction Distribution within Chamber .................................................. 170 Figure 76 - Water Phase Forming around the Injection Points and the Outlet Where the Coldest Temperatures are Attained ........................................................................................................ 171 Figure 77 - Injection Points and Droplet Temperatures across the Cooling Chamber ............... 172 Figure 78 - Grid Sensitivity Study for Full-Scale Model Comparing Static Temperature and Vapor Fraction of Different Mesh Sizes ................................................................................................. 174 Figure 79 - Merit Factor Sensitivity Based on Different Droplet Size Settings ........................... 175 xviii ACKNOWLEDGEMENTS Firstly, I would like to thank my supervisor Dr. Ali Madiseh for his continuous support and patience in advising me through my studies. His curious perspective and calm guidance helped me immensely in every direction when finding my way. Without his kind support, this journey would have been much more challenging. His tireless efforts in lifting his team gave me the confidence to thrive and move forward. Besides my supervisor, I would like to express my sincere thanks to Dr. Agus Sasmito. He has been very instrumental during my research, especially with his sound expertise in computational fluid dynamics and numerical simulations. He added an indispensable momentum to this work in reaching the finish line. I also would like to express my sincere gratitude to Dr. Ferri Hassani. His insightful comments and years of academic excellence was very much encouraging in facing the challenges during the realization of this work, and many others. Several times during my PhD, he also helped me finding my way with his wisdom. Here, I also would like to thank to my thesis committee. Their constructive methods and intuitive guidance certainly shaped this study to its final form. I also appreciate how approachable they were, whenever I needed them. I also thank my fellow researchers in helping with the lab work while bringing idea stimulating discussions to the table. Their contribution to this work and several others is very important to me and I appreciate their efforts. xix Last but not the least, I would like to thank my dearest wife, Sema and the rest of my family. They have been the source of unconditional support and joy when I needed them the most. I cannot express myself enough to appreciate their presence in my life. Thank you all. xx DEDICATION I would like to dedicate this dissertation to my family and my friends as an appreciation to all the good memories we shared together. I know none will recur and all will matter… 1 CHAPTER 1 – Introduction of the Thesis, Problem Statement, Limitations and General Information 1. Introduction A typical North American open pit mine could require some 60-kWh energy per tonne of ore mined and processed [1]. For an underground mine, this figure goes up to 100-kWh [1] as underground mining has some additional challenges in comparison to open pit mining (i.e., air conditioning, ventilation, and illumination). Although underground mining is usually more selective and offers higher access to ore bodies with relatively lower waste generation, due to mentioned requirements it is usually much more energy intensive in comparison to open pit mines. Further looking into the breakdown of energy distribution within a typical underground mine energy grid, one would notice that mine HVAC (i.e., heating, ventilation, and air conditioning) systems take up a substantially big portion of the overall need. In his studies, Sbarba highlighted that HVAC energy requirements of an underground mining operation could total up to ~40% of the overall energy demand of the mine [2]. Additionally, this power load must be provided continuously for the safety of operations and might peak to higher values during peak seasons (i.e., heating in peak wintertime or cooling in peak summertime etc.). On the other hand, it is reasonable to foresee that mining operations are growing deeper due to continuous depletion of shallower deposits. This renders engineering and operation of such complex industrial establishments to become more difficult and expensive. For example, according to Ranjith et al., some South African mines has already reached beyond 4.5 km depth, whereas; China has 47 coal mines reaching beyond 1-km underground [3]. Similarly, according to some sources in the field (Hardcastle and Langevin) Canada has several operations attaining 3-km underground and it is growing [4], [5]. Lastly, Brake studies cooling of the Australia’s Mount Isa 2 Mine with ~2km depth [6] highlighting the challenges of building such massive scale bulk coolers. Moreover, in his studies, Kuyuk lays out some industrial cost figures around installation of a modern, conventional chilling system in a mine located in central Canada. According to his studies [7], [8] installing a conventional bulk air cooling system with a proper refrigeration plantroom could total more than $ 50 Million, and as other sources confirm these systems might need $ 5 – 7 Million per annum to operate in full performance [7]-[10]. Growing depth of mines would not only bring challenges to distribution of air, but also it would bring other physical strains on the operation, such as heat. Mitigation of heat related safety issues become a priority for ultra-deep mines as their significance arise with the growing depth. Under normal conditions, increasing depth triggers longer distances to travel, more engine hours, deeper strata exposure and greater compression of sinking air. All these are significant contributors of mine heat [11] and create difficult working conditions for workers. For instance, in deep South African mines, mine ambient temperature could rise up to 50 °C as Man-chao reports [12] or if one examines Canadian mines as Kenny did, it would be seen that mine ambient temperatures reach to 40 °C levels at depths around 2 km [13]. However, deepening of mines is not the only challenge in mine HVAC industry. As one might reach the conclusion with mere logic, minable deposits are not always located in favorable locations and grid access for building these facilities are sometimes not at the disposal of the operating companies. In these circumstances mining companies are either required to build power gridlines to the nearest substation or simply deploy energy generation facilities to generate their own power. Assuming deployment of nuclear energy [14] is still immature and not yet ready to be considered as a viable option, it is safe to say that much of these remote facilities 3 are still powered with fossil fuels, mainly diesel [15]. A benchmark study done by Wojtaszek et al., analyzing 32 varying scale, off-grid mining facilities in Canada, has reported that overall, 658 MWe is required for operating such remote establishments [16]. To sum up, current position of the industry indicates that underground operations are growing deeper with more demand on cooling. As a result, mine heating, ventilation, and air-conditioning (HVAC) applications are becoming more substantial for underground mining. This means, mining companies will be looking for more effective cooling solutions with more reasonable energy profiles. However, these systems are still highly expensive and require loads of energy. Plus, mine cooling systems have not been studied much for the past few decades and design models proposed so far are lacking granularity. In accordance with this motive, this dissertation aims to investigate current practices used in the industry, realize the gaps in the existing mine cooling literature and propose a comprehensive study of numerical and experimental approaches to better understand such systems for design optimization. To better categorize the related subjects, this thesis work is divided into chapters. For example, the first chapter, including what is presented so far, (Chapter 1: Introduction of the thesis, problem statement, and general information about the concept.) will discuss the introduction of the thesis concept by laying out some basic facts around the mine HVAC industry. This chapter also highlights arising problems in subsurface mining and provides general information about the content of this work. Following up, the second chapter (Chapter 2: Literature review on mine ventilation, global challenges on mine ventilation and cooling, review, and background information.) will deeply delve into the literature review of the techniques used in modern mining industry so far. This section will also provide in-depth knowledge about earlier studies and 4 models used for underground cooling system design. Second chapter is important and one of the core chapters of this work. Firstly, previous mathematical approaches that was developed by Bluhm et al. will be introduced to the audience in this chapter. Secondly, main methods to design conventional bulk air cooling (BAC) systems will also be shown and explained here. Also, this section will discuss the empirical nature of these models and emphasize design guidelines and associated limitations. Understanding this section is of a prime importance for the thesis audience as the mathematical methods discussed in this section will help them to fully appreciate the modern design methods employed in ‘lake cooling’ study. With this, it is aimed to form a transitional basis while migrating to numerical methods and techniques used in the oncoming chapters. This section will also create an important foundation for the large-scale and small-scale numerical modelling of spray cooling applications and the reasoning behind these studies. To investigate more specific examples and to demonstrate the level of current knowledge in the industry, the third chapter (Chapter 3: Renewable mine air conditioning systems: case studies and techno-economic evaluations.) will discuss alternative methods used in mine air conditioning industry and it will also bring extensive case studies done on a real-life Canadian ultra-deep mine. These studies will present details of a cooling system design done for an anonymous, real-life, full-scale mine conditioning project and will be based on published material of the author. To compliment, in the fourth chapter (Chapter 4: Numerical methods in designing BAC Full-scale and Lab-scale, experiments, validation.), numerical methods developed for designing spray cooling chambers will be introduced. In this chapter, multiphase numerical modelling of BAC systems will be detailed in multiple scales (i.e., full-scale and lab-scale). Also, a comprehensive presentation of the experimental setup, details of experiments, data collection and targets of experiments will 5 be explored in this chapter. Moreover, offered numerical solutions and model construction including user defined codes and all other techniques will be shown in this chapter with close detail. Finally, the results of the numerical and experimental studies will be evaluated and presented to the audience. Following this chapter, a conclusive chapter (Chapter 5: Summary, recommendations, future work.) will summarize the dissertation and leave some remarks for the future work. 1.1. Problem Statement As briefly introduced earlier, mining applications are growing deeper and becoming more challenging. The fact that; the industry might be dealing deeper mines in the coming ages would potentially raise several issues such as extreme working conditions in mines. Maintaining safety of operations is the upmost priority of mining companies. However, technological challenges and the capital requirement of employing such systems could be challenging for decision makers. One of the most important challenges of ultra-deep mining is provision of proper ventilation and conditioning to the workings of the mine. Nonetheless, as Vergne stressed earlier, cooling effect of ventilation air becomes usually insufficient for operations attaining ~2 km depths [1]. This means after such depths, other factors (i.e., auto-compression, strata emissions and underground mobile machine traffic) would dominate the thermal conditions and a mechanical refrigeration would become a necessity. Note that, heat sources acting on mine ambient air is various, and their distribution might change from mine to mine. Yet, it is relatively easier to identify that; there are several direct and indirect sources / causes of heat that could impact the mine ambient conditions. Some of these heat sources could be shown as in Table 1 below [1]. 6 As one can predict, not all these sources are prime contributors to the mine heat, and their distribution can change based on the needs of the operation of concern. However, according to Hartman and Vergne, with a generic approach, strata heat, engine and equipment emissions and auto-compression form up to ~80% of the overall mine heat [1], [11]. Table 1 - Heat Sources in an Operating Underground Mine Source of Heat In-situ or broken rock Auto-compression of sinking fresh air Vent-air-compression due to shock losses in the air circuit Diesel/Electric engines Electric sources Chemical sources (Explosive, backfill etc.) Personnel Ground water To further explore, one would notice that; as being one of the biggest contributors of heat emissions in subsurface mines, a specific rate of strata heat emission is not universal for all mines and can change in between different lithological formations. This creates a challenge in estimating the strata emissions without a set of field data. According to Rothstein, geothermal gradient associated with a geological formation could range between 15 °C/km to 50 °C/km [17]. On the other hand, auto-compression is technically not a source of heat but rather a mechanism that results in increase of temperature of the mine ventilation air. This happens due to gravitational compression acting on the body of air that moves into mine network and heat added with this phenomenon is also dependent on the mine depth, mostly. Similarly, the heat added by the mine machinery is also very much operation dependent and its impact may be significantly diminished in the future with more employment of electric fleets in 7 mines. Nonetheless, nowadays, subsurface mining operations could go as hot as 50 °C as highlighted by Man-chao earlier [12] and mines are strictly regulated in these terms, especially in North America. Note that, such temperatures would create significant health impacts on the workers and Table 2 summarizes the temperature ranges accepted in North American mines. The table is presented by American Society of Heating, Refrigerating and Air-Conditioning Engineers, (a.k.a. ASHRAE) and these recommendations are highly accepted and applied in North American subsurface mining operations [18]. Table 2 - Recommended Underground Working Temperatures Suggested by ASHRAE Underground Temperature (x) and Associated Ranges x ≤ 27 °C - 100% Worker Efficiency 27 °C ≤ x ≤ 29 °C - Economic Range of Working 29 °C ≤ x ≤ 33 °C - Action Needed to be Taken 33 °C ≤ x - Short-duration of Work is Allowed Accordingly, deep underground mining operations that reach beyond the “economic range of working” as stated in Table 2, would seek for some ambient air chilling solutions to provide the most effective operational health and safety standards for their workers. When it comes to mine chilling there are several methods that can be employed and much of theses modern techniques will be further elaborated in the context of this study. However, to stay within the scope of the problem statement, it could be said that these methods are generally very expensive and require a very precise engineering for maintaining a profitable operation. To lay down a general outlook of these systems, one could say; much of these systems require massive refrigeration plantrooms that relies on expensive machinery that uses energy intensive, heavy compression cycles [19]. Here, it is important to see that these compression cycles are 8 usually very large in size as they are designed to deal with liters of water to cool tons of flowing air. According to Jaarsveld, only mine refrigeration itself could require 25% of the overall electricity demand of the mine [20]. As a reminder of figures laid out earlier and to show a generic breakdown of costs of similar systems one can refer to Ghoreishi-Madiseh et al. [10], [21] and other cited sources here [7]-[9], [22] to see that these systems might require up to $ 4.5 Million/year for ventilation and up to $ 3 Million/year for refrigeration, reaching more than $ 7 Million per annum just to operate. Certainly, these costs are excluding mine heating during the winter season for harsh climate locations such as Canada, Sweden, Norway and Finland which could add another $ 3 Million per annum to the mine operating expenses (OPEX) according to Ghoreishi-Madiseh et al. [23]. In fact, in some cases auto compression is so impactful that the mines might require a booster station installed in the underground location as seen in an example studied by Kuyuk et al. and Templeton et al. [7], [24]. As seen in this example, underground cooling plants, although they are relatively smaller in sizes, they might require very high capital expenditures as often times these plants require heavy pipelining, high maintenance and ground excavation for installation [7], [24]. In summary, as explained with examples to the thesis audience so far, current mining industry is in a move to attain deeper deposits with utilization of subsurface mining techniques and there are several physical challenges ahead that would require precise engineering and high volume of investments. Mine HVAC applications is one of those areas that would face these challenges sharply. However, with a quick look at the mine HVAC literature, it would be noticed that mining industry is still relying on older approaches that was developed in late 1970’s and early 1980’s which uses limited knowledge in the field. Certainly, more details will be laid out in the 9 forthcoming relevant chapters, however, at this point it could be said that these approaches might not satisfy the requirements of current motive seen in the industry in terms of operational granularity. In a more modern sense, mine HVAC systems could now be designed based on numerical approaches developed in the past decade and as the computational technologies allow faster results these approaches might shed more light in the way of developing more autonomous and intelligent cooling systems. Although robust, design methods developed some 40 years ago are sometimes not capable enough to provide sufficient parametric inventory for faster decision making in preventing under-utilization of such expensive systems. Plus, for underground systems, the problem is more complicated due to limited excavation and installation options. These cases might require out of range solutions than that was studied in earlier practices. With the approaches provided here, it is aimed to generate a more precise system response in mine HVAC applications to prevent over-design of these systems through deeper understanding of involved parameters. Accordingly, this research aims to pave the way in finding more economic, more effective, and environmentally more friendly design strategies. By doing so, more localized, high proximity and high effectiveness cooling solutions can be proposed for ultra-deep mining of the future. Noting the highlights provided in van Jaarsveld’s work [20] (Table 3), some of the cooling systems designed with older methods are occasionally over designed (or under-utilized). This shows that there is a good potential to understand on-demand usage of these systems and transient numerical models could tell a lot about cutting down costs associated with this kind of mis-design. 10 Table 3 - Under-utilization of Conventional Cooling Plants in Some Anonymous Examples (Jaarsveld) Mine ID Installed Capacity (MW) Average Usage (MW) % Under-utilization 1 6.9 2.7 61 2 7.2 3.1 57 3 6.8 1.8 74 4 11.1 6.9 38 5 18.6 8.2 56 1.2. Thesis Objectives As briefly mentioned in problem definition, design of mine cooling systems is currently reliant on non-granular, steady-state, semi-empirical solutions that are developed based on restricted empirical data collected from a limited number of cases. However, due to the dynamic nature of modern underground mining, studied solutions are often challenged with granular modifications in operating conditions and model geometry. Yet, these solutions are not sufficient to explain the performance impact of these granular changes in operating parameters (i.e., droplet size or chamber geometry, air velocity etc.). This problem can be mitigated with numerical methods; however, current mining literature is missing a detailed study of multiphase physics of spray cooling chambers to respond this issue. Also, as these systems are designed using peak operational parameters (i.e., peak seasonal conditions, peak operating conditions with maximum underground crew and fleet etc.) to represent the ‘worst-case’ scenarios, they often end up being underutilized. Moreover, literature survey has shown that research done in other disciplines concerning compatible scales that of mining, are mostly focusing on high enthalpy water spraying for cooling with evaporation. Nevertheless, it is widely known that mining applications utilize very cold spray injection where the impact of evaporation is replaced with higher mass flows with much colder water temperatures limiting the impact of evaporation during cooling as high 11 vaporization might damage the downstream operation. Again, as a response to this issue, mining literature was missing lab scale experimentation of cold spray injection and its impact on the cooler performance. Finally, no other studies in the mining literature have been found to represent industrial scale modelling of mine bulk air coolers with coupled multiphase solutions. Note that; multiphase solution of these systems require coupling of DPM phase (also referred as droplets) to an air-vapor mixture with a mass transfer mechanism to model the two-way phase change behavior. This requires development and integration of a user-defined function as a code and no relevant cases has been found in the mining literature investigating this coupling. In the light of this discussion, following items are summarized as the objectives of this thesis: 1- Provide an in-depth analysis of the current state of the industrial models and present a comprehensive set of industrial case studies to show the usage of earlier approaches and demonstrate their capabilities along with their limitations. 2- Remark the gaps that could be filled in the current literature regarding the model granularity and underutilization issue that arises with older methods. 3- Design a lab-scale test to mimic mine cooling applications in the lab with cold water injections as opposed what was studied in civil engineering field. 4- Develop a numerical model that is capable to capture the multiphase physics in the lab-scale spray cooling unit with reasonable error. 5- Improve the lab-scale model to a full-scale, industry compliant model and provide a robust, multiphase modelling for multi-stage bulk air cooling system. 6- Conduct a sensitivity analysis on operational parameters to prove the importance of model granularity and impact on decision making. 12 Note that there needs to be some clarifications on some of the items above. These can be discussed in bullet points as below: - First objective will be achieved with detailed literature review of current practices and by provision of real-life case studies. - In the third objective it is stated that cold water injections are aimed to be used in the lab experiments. This is important, as the literature review in the field has shown that some earlier studies (to be mentioned when relevant), are concerned about evaporative cooling with spraying. Although it may resemble the case here, this application does not reflect the reality of underground cooling. In underground cooling applications, water injection temperatures are comparably lower (i.e., 5-9 °C) than ranges studies in these studies. At the mentioned temperature ranges used in underground cooling, water evaporation is very limited on the droplets and the overall air cooling is mostly achieved via sensible cooling through the contact of droplets and air. This will be further discussed in more detail in the relevant chapters. - Secondly, model granularity can be introduced in many angles to the existing models. However, for this work droplet size sensitivity in the full-scale solution is put forward as it is one of the most impactful parameters affecting the system performance of spray coolers. 1.3. Methodology Firstly, to achieve the stated objectives, a detailed plan for lab experiments will be prepared. This is important to design a capable equipment to test the conditions similar to the ones faced in 13 underground mines. Achieving this objective in lab conditions requires cold water tanks, air heating unit, a chamber with nozzle installations and so on. Once this is ensured, the fabrication of the equipment will be initiated. Once the lab equipment is fabricated, the machine will be installed, and a series of systematically prepared cooling tests will be conducted at the lab and the raw data for numerical simulations will be collected. Concurrently, a comprehensive, multiphase numerical model will be developed to represent the cases covered in lab experiments for simulation purposes. Once the lab experiments are validated with numerical solutions, the lab-scale solution provided earlier will be upscaled to an industrial size model (also referred as full-scale) and will be validated with industry accepted full-scale models. Once a valid base is established for the full-scale model, a sensitivity study will be conducted over spray droplet sizes to better emphasize the granularity problem and give the audience a perspective on merit factor manipulations that could be established for future studies. Additionally, as objectives state, some case studies using the current approaches will also be provided with detailed design criteria and methods. This will be achieved with real life, ultra-deep cooling examples studied by the author during this work. This will help the audience to identify what sort of enhancements are introduced in the scope of this work. 1.3.1. Rationale of the Methods and Justification of Spray Cooling Experiments, Numerical Solutions and Case Studies Mining operations are often considered as long-term projects with up to 20 years of lifespan. Therefore, they often require very detailed financial analysis of each stage and department that take part in the overall operation. As stated by Hamrin, the industry is always expected to thrive 14 in economically challenging environments and, that is why; its financial survival is very much reliant on the revenue margins that brings profit after paying the cost of prospecting, development, exploitation and reclamation of each unit of rock mass [25]. This shortly explains that any component or part of an engineering project offered to the industry is expected to make its own financial sense to the investors. Therefore, it is an indispensable task of this thesis to seek for optimizations in financial and energy profiles of proposed mine conditioning systems or design methods. Consequently, potential for financial optimization relating to the discussed concepts is aimed to be presented at all relevant chapters whenever the opportunity will arise. This will be done by not only investigating alternative cooling techniques but also by offering better models to design these cooling units. For instance, the opportunity to improve financials of mine air conditioning sought in this work could be a discussion around advantages of using surface water bodies over conventional systems or as well as a detailed numerical explanation of utilization of different drop sizes to generate a range of operating conditions inside the cooling chamber for better performance. Therefore, the methods of this study are selected in a way that they are not only offering meaningful solutions, but they are also providing examples with different aspects. Underground mine refrigeration is a challenging concept considering its design and operation. Coolth required to chill a given mine is usually dependent on the operational characteristics and may differ from mine to mine. In some cases, availability of nearby sources can also change the design characteristics of a mine cooling system. Although, there has been several air-cooling strategies employed in the history of mine cooling (e.g., indirect cooling, fan-coil cooling etc.), spray cooling with water injection has been one of the most prominent ones [2]. According to 15 Kim, spray cooling is a cooling technique that stands out among others in terms of its high heat flux capacity [26]. This very reason makes spray cooling highly attractive for mining industry in comparison to other large-scale cooling systems, as rapid heat transfer offered by spray cooling can provide very effective results for flows that could maintain a full-grown underground mine which could go as high as 1.7 million cfm (or 800 m3/s) (see La Ronde Mine, QC, Canada) [23]. As Glassman states, spray cooling techniques can be seen in many industries ranging from medical services to mine HVAC systems or even space industry [27]. In fact, spray cooling systems have always been considered as one of the most effective cooling techniques due to its technological simplicity, scalability, relatively lower cost, and operational sustenance [28]. Interestingly, spray cooling itself has always been an attractive source of research as well. Seemingly, this sort of cooling systems have always provided some room for improvement at every scale from micro-chip cooling to building refrigeration as seen in here [26], [27], [29]-[35] and many more. However, to the best of the author’s knowledge, most of the modern research done in this field goes up to the scale of buildings and civil applications. Although these applications are similar in scale and form a good base for up-scaling of numerical studies, they are still relatively small when comparing to what is usually found in underground mine cooling and many of them rely on evaporative cooling. Therefore, to reach one of the main objectives of this thesis, it is aimed to build a lab scale equipment specifically designed for spray cooling research at desired operational ranges. This will also help finding similarities between the research conducted for civil applications and seek for an opportunity to up-scale that knowledge to scales that could be easily applied to mines as well. 16 On the other hand, it is known that mine refrigeration is not an untouched field through the course of mine refrigeration history. Especially the period between 1950’s and 1980’s yielded an increasing demand of cooling towers, industrialization and growing number of underground mines [36]-[39]. Apparently, this period laid a good foundation for this kind of research in the mining industry and large-scale civil applications. Last parts of the Chapter 2 of this study will be covering these methods and their capabilities in designing industrial scale spray cooling chambers. However, at this point it could be said that; besides being robust and providing good, generic operational characteristics, these models were not capable of capturing implicit details of spray cooling. The finalized model as published by McPherson in 1993, was sufficient for designing cooling towers and bulk-air coolers without revealing any interior mechanics of these systems [39]. This was mainly due to the empirical nature of the developed model which will be expanded in Chapter 2 in more detail. However shortly, the model was able to capture inlet and outlet conditions of these systems successfully without expanding on any operational parameters i.e., droplet characteristics, turbulence conditions, fluid-fluid interactions etc. Impact of these parameters have remained implicit to the range of empirical observations and touched upon in a very limited manner. That is why a method to delve into these parameters is yet to be found in the current mining literature. This gap can be filled with numerical models. Note that, older methods are matured in their development, and they are not much prone to change without increasing the granularity of the problem with numerical approaches. Although they are still highly applicable and recommended by the relevant authorities [18] (or can be even found as default solution in ventilation simulation software packages [40]) they are still incapable of capturing the parameters raised by Montazeri et al. (Table 4) As they explain in their work, 17 modelling of two-phase interaction of fluids in direct spray heat exchanger applications is very complex [41]. Consequently, to unify the current knowledge in the field, the problem shall be approached from different angles. According to Montazeri, this could be done with a three-way approach as listed below [41]. These set of steps also form a strong base in establishing the reasoning behind the numerical methods of this study: Full-scale Measurements: For this research, earlier studies put forward by Whillier, Bluhm and McPherson is used as the benchmark in validating the full-scale models. Their studies collected in [39] lays an indispensable foundation for up scaling the numerical model developed here to the full scale. Their field measurements will be used as validation point for large-scale numerical modelling. Wind tunnel measurements: A lab scale testing system will be developed and used for experimentation in lab-scale to justify the small-scale numerical models developed over the course of this study. Again, in upscaling, experimental validation plays a very important role to prove the model in capturing the phenomenon around spray cooling from single spray scale to multiple stage, multiple nozzle industrial systems. Computational Fluid Dynamics (CFD): All the knowledge acquired so far will be transferred to a CFD environment and will be validated in both lab and full-scale. This final section aims to finally unify the knowledge around the topic by offering a working numerical model that could be later used for further investigations of involved parameters (See Table 4). 18 Table 4 - Physical Parameters That Impact Spray Cooling Performance (Montazeri et al.) Physical Properties CFD Properties Model Constraints Continuous phase velocity Wall conditions Grid size optimization Continuous phase temperature Roughness Boundary and contact settings Continuous phase relative humidity Temperature Validation and scaling Discrete phase size distribution Wettability Mathematical limitations Continuous-discrete phase / discrete-discrete phase interaction Droplet conditions (Discrete) Temperature and velocity of discrete phase Surface Tension Nozzle gage pressure Viscosity & Density Nozzle discharge diameter Temperature Particle Size Air conditions (Continuous) Flow rate and speed Temperature Turbulence Table 4 here shows the physical parameters that impact spray cooling performance explained by Montazeri et al. [41], [42]. CFD methods in this sense, offer an expansive inventory in further developing parametric understanding of these systems with high flexibility. Although, CFD methods might require expertise and computational resources, they could offer a deep understanding of the governing parameters of such complex systems by providing granular information around the model. As Table 4 attempts to summarize above, CFD methods when validated against field data can offer a range of valuable information that could be hardly explained by mere empirical measurements. Another method of delivery that will take place in this work is the studies carried on a real-life Canadian case, which involves several energy efficient, alternative approaches as opposed to the conventional systems used in the industry. Accordingly, there will be a detailed case study presentation for the audience showcasing there could be alternative mindsets around the common energy problem. 19 The main motivation driving this chapter of the thesis will be two-fold. In the first perspective, alternative methods shown will be aimed to facilitate the idea that refrigeration problem could be tackled with non-standard methods successfully. On the other hand, the other perspective would be bringing the results that some of these alternatives not only might make more financial sense, but they are also much more energy effective. Due to extreme demand of chiller plants and mine refrigeration systems, finding some alternative, mostly renewable based, solutions have been a very relatable concern for mine ventilation engineers. In this sense, this thesis aims to cover some alternative mine air conditioning methods to provide an accurate, real-life perspective to the audience by showing there is not only one type of solution to this problem by bringing different angles to look at the same problem more objectively. This not only serves to the purpose of this thesis in unifying the industrial and academic knowledge into one piece of work, but also it creates a portal for connecting the mine HVAC industries to modern, energy efficient and smart mining solutions as well. Note that; all the case studies discussed here will be delivered in modules. The modules will be concerning different aspects of the same mine e.g., cooling, energy management and intake air heating. Much of this information will be laid out in Chapter 3 and all the results included in here will be based on author’s published material. The method of delivery will include a full-scale, detailed introduction of the mine and its operating parameters followed by the modules of interest as seen in more details below: 20 Deep-lake cooling system: This part will be covering end-to-end design of a lake-based cooling of an ultra-deep mine. Mine cooling system designed here is inspired on district / building cooling systems using deep surface water as heat sink. The section will compare the conventional system against the deep-lake cooling system and highlight potential savings of deep lake cooling system over conventional installation. See some widely acclaimed industrial scale examples of deep lake cooling in other industries: Enwave’s Toronto based district cooling system [43] or Swiss Lake-cooled Data Centre [44]. Ice storage system for cap-shaving: In this section, the conventional plant addressed in the first chapter will be aimed to downsized through cap shaving efforts using a coupled solar system powered ice generation and storage. The idea here will present photovoltaic availability of the studied location and seek for financial savings through employment of solar power in the cooling circuitry of the mine. Closed-loop bulk air conditioning: In this last case study, the same location will be analyzed in terms of winter conditions and mine air preconditioning needs of the operation. Then a novel closed loop system will be proposed that can be coupled to the system introduced earlier. This study will seek the financial and carbon savings in the pre-conditioning efforts of the operation through re-utilization of the return water of the underground cooling chamber. These alternative systems, with proper water treatment plans, can be much more environmentally friendly comparing to the conventional refrigeration plants as they do not need to employ toxic ammonia or other sorts of refrigerant agents. Some of the large-scale examples of surface water cooling used for different applications as seen here [43]-[45] will be further 21 examined in these case studies. These studies are placed here as they will establish a reasonable series of examples in utilizing current models and provide some cost figures for full-scale alternative mine cooling scenarios. This will not only help audience to see how the current models are being used in an industrial design, but also help them to notice that these models are providing limited answers for further questioning the provided designs. Also knowing that cooling systems are very expensive to install, it is believed that providing some alternative cases to the mine cooling problem could also help audience to be aware of the relative cost profiles of different solutions in case they would like to invest in mine cooling systems that are outside the conventional domain. With these methods, this study will not only be looking the cooler from outside (i.e., source of cooling, cooling methods, different types of designs and their cost) but also will investigate them from the inside (i.e., numerical methods for better results and optimization). 1.4. Limitations of the Thesis Through employment of computational-fluid-dynamics simulations and experimental work conducted in the lab, this thesis aims to create a new ground in designing BAC systems with higher precision and parametric flexibility. However, spray cooling systems and their physical interpretation is very complex, and it is difficult to incorporate all the relevant physical phenomena into the model. Accordingly, some assumptions had to be made to maintain a healthy and robust simulation. These assumptions are mostly related with limiting physical phenomenon in the simulation conditions, such as impact of droplet collusion, droplet break-up 22 or water film forming on the walls. These studies are scoped as future work and has not been studied during the timeline of this dissertation. Moreover, experimental validation only covers the lab scale model and its offerings. The lab scale model studied here mostly focuses on single spray arrangement and close-up spray behavior in terms of heat exchange and outlet conditions. Learnings acquired in small scale modelling are then applied to full-scale model design and then validated against a model used in the industry. Yet, this full-scale model has not been compared with an actual working mine. Although, it was found sufficient to use a well-established and recognized experimental model for upscale model validation, a full-scale operating mine could also be studied, modeled, and validated in the future. Again, this was not possible during the thesis timeline. As being one of the central case studies shown here, lake cooling systems could be very promising in cutting down the energy costs or avoiding from multi-million plantroom installations. On the other hand, using surface water bodies in an industrial setting could be environmentally risky and requires to be well studied. The lake cooling project was an extended work studied with other researchers and some level of water treatment work also put together. However, this content is considered outside the scope of this work and not presented. Environmental aspects of water displacement from lake, utilization, and recycling should be studied further for more detailed and concrete justification of these systems. Lastly, the ice-storage system mentioned in this study is a proof-of-concept level study and no experiments were conducted on validating the idea physically. However, all the data used in the paper was based on real-life conditions and solar irradiation and temperature values are taken 23 from publicly available sources. For further development of the idea, detailed testing and experimentation is required. 1.5. General Organization of the Thesis General organization of the thesis is summarized in the table below. Following list gives out a detailed outline of the thesis and the coverage of the corresponding chapters. Table 5 - General Organization of the Thesis Chapter Details 1 Introduction of the thesis, problem statement, limitations, general information 2 Literature review on mine ventilation and cooling, global challenges reviews and background studies 3 Renewable mine air conditioning systems: case studies and techno-economic evaluations 4 Numerical methods in designing BAC in Full-scale and Lab-scale, experiments, and validations 5 Summary, recommendations, future work** *Bibliography and appendices are not included in the list. **The last chapter of this thesis is dedicated for thesis conclusions, recommendations, and some insights on future work. The list shown here only covers the main divisions within the thesis and provides a summary of the content that they include. However, note that the content covered in the prescribed chapter may not be limited to what is stated in Table 5. Also, some other titles or subsections are not included to the list. Each chapter covered throughout the thesis will be a standalone chapter covering the subjects stated in Table 5. The chapters are presented in a way that the knowledge accumulated by the reader on the subject is consistent and logically relatable. -End of this Chapter-24 CHAPTER 2 – Literature Review on Mine Ventilation and Cooling, Global Challenges, Reviews and Background Studies 2. Literature Review 2.1. Mine Heating, Ventilation and Air Conditioning (HVAC) This section provides a general background about the subject of the research. Since the background of the subject is multi-disciplinary, several sub-sections are curated for the literature review section for easier organization. 2.2. Challenges Faced in Mine HVAC Industry Mining activities are almost as old as human history and ever since mines are getting deeper. However, it dates back only to 17th century that miners began to understand about the ventilation needs associated with the subsurface exposure [39]. That was the era that humanity was rapidly learning, developing, and improving. However, the mining conditions, especially coal mines, were very bad for human health [39]. Until 1854, miners were mostly interested in mitigation of ventilation problems. Therefore, they were mostly interested in finding solutions for a handful selected cases [39]. Nonetheless, after 1854, the date that J.J. Atkinson published his first paper on mine ventilation theory, miners started to learn about mathematics of the mine ventilation and about the fact that it can be designed, managed, and improved [39]. Since then, the knowledge on fluid dynamics science has improved so significantly that ventilation engineers can now deal with ventilating mines that has almost 4 km of depth. However, the challenge is ever growing due to the natural process of mining and its irreversibility. 25 As defined by Hardcastle et al. [46], Canadian mines are undergoing three important trends. To be more specific; one can say that following changes are challenging mining industry in terms of several aspects including mine ventilation, heating, and air conditioning: - Mines are growing deeper and deeper. This makes sending the minimum airflow to the underground for proper ventilation is now more challenging due to physical factors acting on the body of air. - Technological improvements are resulting in more sophisticated machinery and more mechanization in mines. Here, it is worthwhile to mention that although electrification of mine machinery is a very big change and there are several good examples of this shift across the globe. Diesel-run mine machinery are still cheaper to operate [47] and, therefore, still more preferred for extraction and haulage of the broken rock. - Lastly, standards governing health and environment around mine sites are becoming rapidly more conservative and protective resulting in higher detail adjustments, improvements and, therefore, integration of more sensitive and expensive instrumentation. None of these three factors are less important than the other, however, it is clear to see that the challenge faced due to deepening of the subsurface mining operations is in a constant shift to negative. This is mostly because; while the technological advances are help soothing the second and third factor, the first factor is still the main drive and keeps moving in the same direction. 26 2.3. Impact of Increasing Depth on Mine Ventilation and Refrigeration Due to rapid consumption of earth’s irreversible resources, many sectors involved in the extractive industries are facing big challenges due to depth they need to reach in. Referring to Ranjith et al.’s study, it is easy to conclude that mines are growing deeper [3]. Most certainly, as a counterargument it could be said that increasing automation of the mine machinery and improvement of communication and remote controlling would make mines quite possibly less occupied by personnel in the coming years. However, as cited in [48] according to the results of five surveys conducted by the Ontario Ministry of Labor, it is shown that the personnel population in underground has constantly increased relative to earlier years between 1977 and 2003 (Ontario Ministry of Labor, 1996-2003). Since then, several other projections are made, according to Mining Industry Human Resources Council [49], over 97,000 workers will required to be hired by the mining industry of Canada in the next decade (2019-2029) and this could go up to 135,000 under a potential growth scenario. Therefore, it is to conclude that considering the pace of research and development, technology integration, market penetration rates and regulatory adaptation, a complete transformation could still take up a few decades. Accordingly, miners and engineers are more likely to improve the existing techniques as it is more accepted to find solutions around the currently existing problems to keep mitigating the problems around the regulatory requirements due to slow change in the market. Like it was mentioned before, tackling with the physical challenges associated with deepening of the mines can be considered as one of the most difficult and costly issues. As Hardcastle highlights, increasing the depth of a 2km deep Canadian mine by 300 m would cost extra 20 % 27 increase in the total airflow quantity to be provided by pushing the whole operating expense of the attached ventilation system up to 60-70 % more than what it was [4], [46]. On the other hand, as mentioned earlier in the introduction, underground chilling becomes a mandatory act after 2km of depth is reached due to several heat sources listed in Table 1. In modern mining industry, there are several air conditioning techniques that could be employed to mitigate the heat related problems. Following figure, Figure 1 - Modern Mine Cooling Techniques Applied Based on Depth of Mines (after MacKay), shows not a definite but certainly relatable relationship between the depth of mines and potential heat mitigation techniques visually (after MacKay et al. [50]). Figure 1 - Modern Mine Cooling Techniques Applied Based on Depth of Mines (after MacKay) Although, these systems may differ from each other by the techniques acting behind them, they are usually classified as their interaction with the mine ambient. When approached in that sense, these systems can be investigated under two different titles: • VENTILATION SYSTEM ONLY (100m – 750m Depth) • SURFACE BULK-AIR COOLING (CONVENTIONAL) (600m – 1300m Depth) • SURFACE BULK AIR COOLING (ULTRA-COLD) (100m – 1500m Depth) • DEDICATED FRIDGE SHAFTS (ULTRA-COLD, HIGH SPEED) (1400m – 1900m Depth) • COLD-WATER-FROM-SURFACE (SURFACE PLANTS) (1600m – 2250m Depth) • UNDERGROUND AIR-COOLING SYSTEMS (2000m – 2500m Depth) • Secondary air cooling (intake and reuse) • Controlled recirculation (vent districts) • Tertiary or in-stope cooling • UNDERGROUND REFRIGERATION PLANT (2000m – 2500m Depth) Increasing Depth 28 - Direct cooling systems: Water and ice-based cooling systems where the coolant gets into physical contact with the mine intake air or ambient. Mostly used versions are spray chambers (or also known as bulk-air-coolers). In these systems, generally a conventional cooling system is attached to a spray chamber through which a body of fresh mine air is force fed. The system is mostly well established and very abundantly used in the current industry today. However, there are other versions of direct cooling as known as ice stopes where a large body of ice is formed during the favorable times of the year and let to be melted when the hot season arrives. - Indirect cooling systems: In these type of cooling systems, coolth bearing media and the mine air will not get into contact. Indirect cooling systems are comparably less preferred than the direct cooling systems due to their relatively lower effectiveness in large sizes and due to inefficiencies associated with the heat transfer mechanism involved. These type of cooling strategies are required when the cooling effect of the ventilation air is not enough to sustain minimum working conditions underground. According to a study done an ultra-deep platinum mine by MacKay et al. has shown that the break-even depth for water chilled solutions to be deployed starts at depths around 2.2 km [50] agreeing with Vergne’s ~ 2 km estimation mentioned earlier [1] and Bluhm et al.’s list of typical wet-bulb temperatures for deep mines [51]. 2.4. Underground Mine Cooling Techniques As introduced above, there are several underground mine cooling techniques seen in the industry. This section provides background information on these systems as they are found in the modern mining industry. 29 2.4.1. Conventional Spray Cooling Systems In these type of cooling applications classical refrigeration cycle is used through employment of external work on the refrigerant by making it to go through a phase change via change in its pressure. Figure 2 shows the classical refrigeration cycle showing step by step thermodynamic process of the cooling. Figure adapted from Radermacher and Hwang [52] also as cited in [2]. Figure 2 - Thermodynamic Process of Refrigeration Cycle Used in Conventional Cooling Plants (a- refrigerant cycle b- pressure enthalpy curve of the coolant) (after Radermacher) Conventional cooling plants are mostly operated with chilled water where the water is pressurized with pumping systems. Once the water is chilled at the refrigeration plant, it is pumped to lines tipped with nozzle, injecting water into a cooling chamber. This cooling process is very rapid and injected water has very limited time during its trajectory. Therefore, industrial size cooling systems are usually in multi-stages. This means that sprayed water is recycled in between stages to maintain a continuous cooling for higher effectiveness. 30 A typical mine bulk air cooling system as first sketched by Bluhm et al. [51] and adapted here can be seen in Figure 3 below. The sketch represents a multi-stage cooling chamber, where chambers are compartments of cooling that uses the return water from prior stages for creating a counterflow effect within the chamber [51]. This will be discussed in following chapters with more depth. Figure 3 - A Typical Multistage Cooling Chamber (After Bluhm) First examples of nozzle assisted spray-to-air applications are not quite new and dates to early 1900s, where Carrier used such systems for air washing which helped dehumidification and air cleaning [53]. Afterwards, some early mining examples had started to emerge around 1930s for cooling purposes in hot South African mines [54]. Although these examples has been around, there was not much of a written literature on the subject until 1960s and early 1970s, after which an increasing demand on cooling towers and spray systems in heavy industries like mining was seen creating a demand regarding optimization of these installations [39]. As highlighted earlier, (see Figure 1), except ice-cooled extreme examples of ultra-deep mines, where uncooled mine ambient temperature could rise up to 65 °C, water based systems are highly preferred and 31 effectively used in mine cooling due to its high flux, easiness in treatment, and opportunity for recirculation [55]. Nowadays, one of the most established examples of mine bulk air cooling applications can be seen in Agnico Eagle’s La Ronde mine. This operation has announced a feasible depth 3.7 km making it the deepest mine in the Northern Hemisphere [56]. Following picture, Figure 4, (credit to Agnico Eagle as taken from Hardcastle [57]) shows an angle from La Ronde’s underground bulk air cooling (BAC) plant. Figure 4 - Underground BAC at La Ronde Mine (After Hardcastle) To elaborate on the example, La Ronde mine needs an overall ~1.7 million cfm of fresh air provision to the mine and this flow is divided in between two intake shafts (i.e., ~1.2 mcfm at the main intake shaft and ~0.5 mcfm at the Penna shaft) [5], [58]. According to Langevin, La Ronde’s cooling efforts are distributed across the mine with 12 MW cooling plant at the surface (handling 1.2 mcfm), 6.8 MW UG plant at the 1,700m level (handling 0.7 mcfm), another 3.4 MW UG plant 32 at the 1,500 m level (handling 0.25 mcfm) and one final 5-6 MW plant commissioned to be deployed at the 2,600 m level (to provide cooling for deeper parts of the mine) [5]. This totals up approximately ~30 MW worth of cooling for just one example shown here [5], [58]. Most certainly, technology is pushing the benchmarks of mechanical refrigeration to a better state at every instant, however, even with the most advanced mechanical refrigeration systems, a mechanical chilling plant requires 1 MW of electricity for generation of 5 MW of cooling, as Hardcastle states [57]. As a result, establishing these systems to multiple locations at a mine site results in multi-million-dollar projects with expensive machinery, sophisticated installations, and sizable footprint. Figure 5 below represents a real-life, surface bulk air cooling facility to visualize the discussion here. Figure 5 - A Surface BAC with Cooling Towers and Water Treatment Facility (After [59]) As it might be obvious so far, underground mines might choose to design and use different locations, different sizes or different capacities for their bulk air-cooling employment, depending 33 on their cooling needs. However, locating the right spot for the cooling plant is not an easy process and requires detailed understanding of the dynamic heat flow inside the mine for higher effectiveness of the cooling operation. As discussed by Sbarba, most of the surface cooling plants are getting less effective with the increasing mine depth [2] and this is mainly due to auto-compression and strata exposure. For example, going back to La Ronde example, it is discovered that the cooling impact of the surface plant at the main intake shaft decreases significantly as the chilled air gets exposed to host rock increasing its temperature as development and production of the mine continues (as cited in [2], see Lafontaine, 2010) creating demand for further cooling for the safety of the operation. Another example from Victoria, Australia demonstrates the significance of the issue. As discussed by Sbarba and Tuck, Stawell Gold Mine loses ~50% of the refrigeration power by the time the fresh air reaches to deep mine workings [2], [60]. This creates a demand for underground cooling for maintaining a stable cooling performance, however, underground plants could be even more challenging due to space limitations in underground and additional expenses associated with UG BACs such as piping or excavation etc. To generalize, selection of the cooling plant location is very specific to mine design, depth of mine workings and airflow required, however, as Mackay et al. clarifies in their study, it should be concluded that before attaining any BAC design, a break-even depth for cooling plants needs to be settled to make sure the effectiveness of cooling [50]. (Remark Figure 1 for typical depths and associated cooling efforts to mitigate mine heat.) Underground plants are generally stationary and required to deal with high amount of flows for ultra-deep mines. However, in some cases, mobility of the underground cooling could be discussed too. Here, again depending on the design requirements, the size of cooling needs to be 34 calculated and shall be used to determine whether a stationary plant or mobile plant would mitigate the heat problems for the given workings. As Mackay et al. discusses, usually 1.5 MW rule is looked for spot cooling versus stationary cooling plant installation [50]. In principle, underground cooling plants works very similar to surface cooling plants, however, they require more piping for water circulation, more pumps, and different water handling techniques comparing to surface plants [46]. Also, as they mostly dump the heat of the recirculating water at the cooling towers coupled to the return air shaft of the mine, hence; their COPs are relatively lower when compared to surface cooling plants [61]. Additionally, for underground cooling plants, water management and excavation of the chamber could create more cost and operational burden comparing to surface plants due to accessibility and space problems. Due to these issues a detailed comparison for each case must be employed and a final decision matrix should be created for the decision-making process. Table 6 summarizes some practical questions and offers a decision-making matrix for comparison of surface versus underground refrigeration plants. 35 Table 6 - Cooling Plant Selection Matrix Duty Surface Plant Underground Plant Maintenance and Repairing Easier access, easier maintenance, availability of using larger equipment due to availability of larger spaces. More difficulties in accessing the mechanical equipment, parts, and plantrooms. Not spacious due to restrictions and costs. Seasonal Operations (For most cases) not needed during the winter season. Requires start-up and cool-down periods for 2-3 weeks. Can be used year-round, not necessarily requires start-up and cool-down procedure if used year-round. Positioning Almost no limitations. Only restricted by the mine outline and footprint. Location of the plant is highly limited with geotechnical requirements, depth, ventilation network, underground traffic, and infrastructure. Scheduling and Employment Commissioning can be done in accordance with civil works does not require heavy earthworks. Requires earth moving, rock supporting, pipelining etc. Detailed planning is highly recommended. Availability for Expansion Plant expansion can be commenced easily due to low limitations. Plant expansion is highly burdensome. Establishing of another plant might be required in occasion of expansion. Heat Transfer Capacity High-capacity heat transfer capability due to higher COP and more effective cooling towers. Cooling towers should have access to return airways. Due to higher heat at the exhaust lower COP and performance. 36 Risk of Dust and Contaminant Exposure Almost no exposure. Might be exposed to contaminated air intake due to leakages in the air network. Positional Efficiency / Localness Fair. They are required to be installed at the surface. Although their capacities could go up to 40 MW, the coolth provided by these plants might be dissipated. Very good. They can be planned in locations where the mine temperature increases the thresholds. They can provide immediate cooling with minimum loss caused by intrinsic heat emissions. Size Limited by the footprint. Limited by the mine design, geotechnics, and cost. Refrigerant Can use ammonia. Refrigerant leakage is easier to tolerate. No ammonia can be used due to its toxicity. Refrigerant leakage cannot be tolerated. As seen, most of the factors shown in Table 6 are in favor of surface cooling plants. However, due to high positional efficiency of underground cooling plants, they are highly considered in the industry, especially for ultra-deep mine cooling. 2.4.2. Closed-Circuit (Indirect Coolers) Indirect coolers are mostly used for local cooling as spot coolers or regional coolers in the mine air circuit as their heat transfer performance is comparably lower than the spray cooling systems or direct cooling. They are mostly coupled to local booster fans to provide additional cooling on top of the cooling provided by ventilation air, and they operate as closed circuit in the loop (i.e., 37 air and chilled water does not come into contact). Their size varies and their cooling capacity varies from 500 kW to 1.5 MW [50], [62]. Following pictures shown in Figure 6 shows some examples of hydro-powered spot coolers used in subsurface mining operations. These systems are generally coupled to mine service water or extra chilled water streams (if available) from the surface [62]. Figure 6 - Hydro-powered, Closed-circuit Spot Coolers (After Mackay) 2.4.3. Ice Cooling Plants Ice cooling can be very effective way of mine cooling due to high thermal capacity of ice. Additionally, ice cooling systems are rapidly getting attention because of increasing depth of mines. Due to extreme depths and temperatures in underground working spaces, ambient cooling with other conventional methods are seemingly reaching their limit due to massive amounts of water circulation and highly costly pumping requirements [63]. Shortly, ice cooling plants are gaining attention as they can: 38 - Eliminate extreme temperatures with high effectiveness - Significantly reduce pump-back water - Can provide megawatts of refrigeration with easier transportation and minimum loss [63]. Here it is important to see whether for chilled water transfer, insulation of the delivery system is very critical for maintaining a high-performance cooling. According to Mackay et al., improper insulation on delivery lines for BAC systems could cost up to 200 kW of cooling per 100 m of pipe length [50]. Also, another study done for the Vaal Reefs mine shows that a rise 2.34 °C temperature rise was observed in the temperature of water per 1,000 m of pipe run [64]. However, this insulation problem could be easily mitigated in ice cooling as the transfer of ice is relatively easier (i.e., free, or controlled fall from shaft or ice conveyors etc.) with minimum coolth loss. As Brisset highlights, ice cooling system is becoming the conventional way of cooling ultra-deep South African mines comparing to refrigerated water systems due to its higher cooling capacity over cost of transportation [65], [66]. Ice plants are relatively newer in comparison to water cooling in mining industry. First application of ice cooling was employed in 1982 at the ERP Mine in South Africa, (as cited in [39]). Manufacturing of ice and delivery of ice to the underground can be done in various ways and generally depends on the operation and available infrastructure. However, to generalize, ice is supplied to underground either in particulate form (i.e., solid pieces or particles) or mixed with water forming an ice slurry or as referred in the industry ‘brine’ [39]. 39 Figure 7 - Examples of Ice Types Used in Mine Cooling (Credit: [67]) Ice plants can generate ice by using conventional ice generation techniques and these units could be at the surface or in underground as described by Bellas et al. [68]. However, due to size of refrigeration systems and capacity of vapor compressors for high tonnages of water freezing, these systems might result in elevated costs and considered to be deployed only if no other ways of cooling is successful in bringing the mine temperature to reasonable levels. A study done by Schutte et al. shows financial simulations of three different scenarios including surface plant only, surface and underground refrigeration system coupled, and surface ice generation system based on the conditions of deep South African Mines [69]. In their study, they recognize that although pumping requirements for ice system is comparably less than other systems, ice generation energy requires more power drawn from the grid adding up more than 25 % electricity cost on the mine’s grid [69]. On the other hand, MacKay et al. finds out that the break-even depth for ice cooling versus chilled water cooling is around 2,900 m, according to their study done on an ultra-deep platinum mine [50]. As seen, it is clear to see that; successful deployment of such systems requires a detailed study of performances offered by different options. Flake Plate Block 40 2.4.4. Alternative / Novel Cooling Techniques In the context of mine refrigeration, there are several other novel examples can be referred. These methods, in comparison with general understanding, are designed in a way that they take advantage of the existing coolth energy or using novel coolth delivery / distribution systems. These systems are briefly identified in this section. 2.4.4.1. Micro-Climate Options Micro-climate solutions are more focused on chilling of the personnel rather than the cooling of the mine ambient [70]. These systems mostly employ cabin cooling in the machinery or wearable cooling garments [70]. A study done in 2016 has shown that, with the available technology, none of the current wearable technology would protect the miner from exposure but only can mitigate the impacts [71]. According to Greth, the research regarding this type of wearable systems dates to 1970s [70]. 2.4.4.2. Shaft or Stope Freezing Shaft or stope freezing are types of mine cooling techniques that uses seasonal storage of coolth in a stope or ventilation shaft. In these type of cooling, climatic conditions offered by the low temperature season is used to freeze a part of the fresh air ventilation network and let that opening to cool the intake air for a given period. Freezing an opening help cooling the passing ventilation air through contact. McPherson discusses an example where a spray system is used to build a pile of ice under the stope floor during the wintertime and circulation of air during the cooling season is shown as seen in Figure 8 [39]. 41 Figure 8 - Working Principles of Ice Stope (After McPherson) Two of the Canadian mines, Glencore’s Kidd Creek Mine and Vale’s Frood – Stobie Mine, can be shown as examples that deployed such cooling systems to save from their refrigeration costs. Although, Frood – Stobie Mine has been shut down, it used to employ 10.8 MW worth of cooling from its ice stope formed during the wintertime [72] (also see [73] for more details and computational fluid dynamics models discussed around this application). On the other hand, as being another ultra-deep mine that uses ice stope, Kidd Creek Mine is still in operation and considered as the deepest base metal mine with shaft bottom reaching more than 3 km [74]. According to Counter, the ice stope in Kidd Mine can provide 5-9 MW of cooling until it completely melts around late May or early June, depending on the average cold season temperature of that year [75]. Following image, Figure 9 shows how the ice stope in Kidd Creek Mine looks like. Note that the following photo was captured during early March. 42 Figure 9 - An Image from the Ice Stopes Used in Kidd Mine (1,200 Level, March 2014) (After Counter) 2.4.4.3. Natural Cooling Natural cooling is a type of cooling, where (if applicable) caved overburden mass is used as coolth storage medium during the wintertime and used as a cold body allowing natural convection through the mine workings for the cooling season. According to Sylvestre, Vale owned Creighton Mine uses tis system to provide a stable temperature flow into the mine at 3 °C year-round [76]. See, this system not only provides coolth during the summertime, but it also protects the mine working from harsh climates during the wintertime too, as it stores warmth during the warmer season [76], [77]. Figure 10 below shows a simplified sketch of the natural cooling / heating system used in Creighton Mine. 43 Figure 10 - Sketch Showing the Natural Cooling / Heating System Used in Creighton Mine (After Sylvestre and Sbarba) 2.4.4.4. Lake Cooling Lake cooling or in general using surface water bodies as for cooling of the mine air is a type of natural cooling. In these type of cooling systems, deep surface water is accessed either via pump systems or closed-loop water circulation paths to use the stored, seasonal coolth in deep waters. Thermal stratifications found in deep waters suggest a stable year-round temperature and could help saving operational cost of conventional refrigeration plants [45]. Following chapter is dedicated to techno-economic case studies elaborating on similar systems and savings that could be acquired through employment of these systems. 2.5. Introduction to Conventional Spray Cooling Plant Design In conventional mine bulk air cooling, the coolth available at the plant (i.e., ice, deep lake water, mechanically refrigerated etc.) is transferred to coolant, for injection in the cooling chambers. The coolant material is usually water and sprayed with nozzle systems installed in a chamber. By 44 this injection the air cooling is achieved where the return water cycles back to the source of coolth present at the plant. As briefly introduced earlier, nozzle based air cooling systems incorporating a chamber of nozzles had first emerged as air washing and dehumidification systems and developed by Carrier in early 1900s [53]. Therefore, it is fair to say that their first use is not new. On the other hand, first mining-based examples of spray systems dates back to 1920s; nevertheless, their full implementation as air conditioning units has not been seen until 1960s [39]. After 1960s an escalation in applications such as cooling towers and spray based conditioning systems was seen [39]. This higher demand for these systems must have created an urge to develop a systematic understanding of such systems, as first comprehensive studies about similar water based, direct heat exchangers fall to the same timeline of late 1960s and early 1970s. In this timeline, Hemp worked on spray tower performances and collected significant data about performance of spray based direct heat exchangers and these early studies conducted by Hemp was followed up by Whillier in developing the tower factor relationship that is used in the conventional models [39]. Again, earliest, structured design recommendations on the topic were put forward by Whillier exploring underground cooling towers where he developed a systematic approach in estimating water inlet and outlet temperatures along with tower performance in 1971 [38]. Then, Whillier followed up on his earlier studies on cooling towers in 1977 introducing estimations on the performance of force-draught cooling towers [37]. Not long after, Bluhm and Whillier collaborated on publishing their first work regarding bulk-air-cooler design in 1978 [51]. This was one of the most comprehensive studies regarding bulk air coolers incorporating Whillier’s 45 extensive observations about tower performances and correlating it with some analytic deductions based on energy balance between air and water [51]. Finally, underground bulk-air-cooler design methodology developed earlier was fully matured in 1980s with Bluhm’s follow up work on multi-stage coolers and overall system merit factor [78]. This allowed predicting the stage number of a system through utilization of merit factors and tower capacities [39], [78]. Lastly, it shall be mentioned that, Malcolm McPherson had collected all the accumulated knowledge around predicting the performance of cooling towers and bulk-air-cooler design (see Subsurface Ventilation and Environmental Engineering, Chapter 6) in a unifying way in 1993 [39]. This solidified the model’s importance for future implementations. Since than the design steps suggested by these earlier models have been very successful in describing such spray-based, direct heat exchangers. They have been standardized by the industry, and recommended by ASHRAE or even used by ventilation simulation software packages [18], [40]. The model introduced here was successful in defining full definition of inlet and outlet conditions of a typical direct, heat exchanger used in mines (i.e., cooling towers and BACs), however, since then not much of an additional work had been done in explaining the chamber physics. To better explain the case let us discuss what Bluhm et al. have suggested in terms of designing bulk air coolers, and at what conditions they have considered for their design recommendations for further industrial use. Later, in the next chapter, a real-life Canadian case will be introduced, and its cooling design will be conducted with the semi-empirical model laid out in this section. 46 Also note that, this model, as an industry-accepted method, will then be used to validate full-scale numerical model in Chapter 4. 2.5.1. Semi-Empirical Model for Designing Full-Scale Bulk Air Coolers Firstly, the model shown in this section is several times referred as ‘semi-empirical model’ in this chapter. It is referred so, as it is initially developed as an analytic approach basing its standards on empirical design guidelines and performance measurements. By employing numerical solutions later, these empirical aspects are aimed to be further explored. However, currently let us focus on the semi-empirical model and how it is developed: To better understand the solution described by McPherson [39] , one could consider bulk-air-coolers as a closed system that has two inflows and two outflows (i.e., water/air in and out). Therefore, in ideal conditions there should be an energy balance in between these two heat-exchanging fluids. According to solution suggested by McPherson this relationship can be written as: 𝑚𝑎(𝑆𝑜𝑢𝑡 − 𝑆𝑖𝑛) = 𝑚𝑤𝐶𝑤(𝑡𝑤,𝑖𝑛 − 𝑡𝑤,𝑜𝑢𝑡) (1) See, Equation (1) describes the energy balance between the inlet air and the outlet water. Here S represents the sigma heat of the air (i.e., specific energy of humid air) and ma represents the air mass flow on the inlet. On the right-hand side of the equation, mw represents the mass flow of the water, Cw is the specific heat of water and tw represents the water temperature. Notice that, as most of the observations were made based on the inlet air and outlet water data. The energy balance shown in Equation (1) could have been written different ways, however, this notation fits the best for integrating the empirical observations made as later shown. 47 Here, one might say due to phase change in this closed system the water mass would change depending on different case. This has been observed by McPherson and according to his notes, the mass change in the water side had not changed the results more than 4% in numerous observations made, and the change in water mass could be neglected in this calculation and mw can be assumed constant [39]. See, this is not the same thing with phase change and its thermodynamic impact on the air. This relationship has already covered by introduction of the sigma heat of air. Rather, this note by McPherson mostly refers to the minute water mass change in the overall water injection mass due to condensation (or evaporation if any) and its impact on the water side. At this point, it is also important to note that this equation only considers the water mass flow to the primary stage and not the recycled mass to the other stages. This phenomenon is introduced as merit effect to the calculation later. Although it will be further elaborated later, multi-staging refers to recycling of the injected water in other chambers for improved cooling. This could be done several times after the primary stage to give the circulating water more chance to interact the incoming air. Going back to the development of the BAC model, to create another condition, one could easily see the implication of the first law of thermodynamics on the described system. Imagining an ideal spray cooling chamber, one would expect to have a lossless heat transfer between air and water for the ‘ideal’ bulk-air-cooler (or cooling tower). However, this is hard to achieve and there is usually an efficiency factor describing the limited heat transfer between air and the water. Performance of these systems is very much attached to this relationship and if any improvement can be made it would be mostly based on improving the efficiency at this point. On the other hand, it is crucial to see this as an important motivation for numerical design of such systems as 48 describing this relationship is very difficult analytically and it needs a field definition of fluids for deeper understanding. From steady-state point of view if one sticks to the inlet and outlet observations of the chamber this relationship can be simply put as seen in Equation (2) and (3) [39] below for water and air respectively. 𝜂𝑤 = (𝑡𝑤,𝑖𝑛 − 𝑡𝑤,𝑜𝑢𝑡𝑡𝑤,𝑖𝑛 − 𝑡𝑎,𝑖𝑛) (2) 𝜂𝑎 = (𝑆𝑜𝑢𝑡 − 𝑆𝑖𝑛𝑆𝑤,𝑖𝑛 − 𝑆𝑖𝑛) (3) Here, one would notice the term Sw as it appears in Equation (3). This term represents the sigma heat of the saturated air at the same temperature with the water inlet. This term is used to describe the potential specific energy difference at the inlet conditions. A different approach in noting the ideal conditions could be done by writing the thermal capacities of both fluids as denoted with letter σ as shown in Equations (4a and 4b) below [39]: 𝜎𝑎𝑖𝑟 = 𝑚𝑎(𝑆𝑤,𝑖𝑛 − 𝑆𝑖𝑛) (4a) 𝜎𝑤𝑎𝑡𝑒𝑟 = 𝑚𝑤𝐶𝑤(𝑡𝑤,𝑖𝑛 − 𝑡𝑎,𝑖𝑛) (4b) Then, knowing that the ratio between thermal capacity of water and the thermal capacity of air should also equate to the ratio between air efficiency and water efficiency, following relationship could be set as seen in Equation (5) [39]. Note that, this relationship is referred as tower capacity factor and noted as ‘R’. 𝑅 = (𝜂𝑎𝜂𝑤) = (𝜎𝑤𝑎𝑡𝑒𝑟𝜎𝑎𝑖𝑟) (5) As it can be noticed, the relationship shown in Equation (5) has a limit value around unity (i.e., the ideal conditions) although it is unlikely to be attained in real cases. This creates a 49 mathematical turning point in defining the effectiveness ‘E’ of the system, as the effectiveness of such system is described by the fluid having the larger efficiency [39]. Therefore, to cover all R values, E is defined as piecewise logical expression as seen in Equation (6) showing that the E of the system equals to: - air efficiency when R is greater (or equal one): - water efficiency when R is less (or equal one): 𝐸 = 𝜂𝑎 𝑖𝑓 𝑅 ≥ 1 𝑎𝑛𝑑 𝐸 = 𝜂𝑤 𝑖𝑓 𝑅 ≤ 1 (6) As the mathematical expression shown in Equation (6) dictates, Whillier has noticed that plotting water (or air) efficiency against the tower capacity factor R, yields two mirror image curves around R=1 line as the line of symmetry [37], [39]. Therefore, to maintain a continuous curve around R=1 and express the system design in a more generic way, Whillier coined a new term, R*, to refer the ratio of the thermal capacities as shown below in Equation (7) [37], [39]: 𝑅∗ = 𝑅 𝑤ℎ𝑒𝑛 𝑅 ≤ 1 & 𝑅∗ = 1/𝑅 𝑤ℎ𝑒𝑛 𝑅 ≥ 1 (7) As it would be noticed, so far, developed model only relies on some analytical expressions based on the inlet and outlet conditions of the fluids. This can be considered sufficient for the design of the coolers, however, due to complex internal physics of the chamber, relationships shown here could only be attributed to the overall system performance. At this stage, a factor called ‘merit’ is introduced to the model to relate to system effectiveness with the tower capacity factor. This factor can be considered as an indication of the cooling performance of the system, and it is usually given in ranges. Much of the early observations made by Hemp (as cited in [39]) were complimented by the Whillier’s follow up work in constructing the merit factor and effectiveness 50 relationship [37]. After many stages of data collecting and curve fitting efforts to quantify the merit factor and tower performance following expression was coined by Whillier as shown in Equation (8) [37], [39]. This relationship was showing how the factor of merit can be approached, especially when there is no early design or solution, in the beginning of the BAC design process. 𝐸 = 𝐹𝑅∗ (8) Equation (8) can now provide reliable operational information based on the expected merit of the bulk-air-cooling chamber and can help the design to relate the initial operational parameters. This factor changes with the orientation (i.e., vertical, horizontal etc.) and the stage number of the cooler. A more comprehensive table containing merit factors of different orientations can be found in the next chapter as presented in case studies (See: Table 12). Later, Bluhm has introduced some extra follow up work to correlate multi-stage cooling and number of stages with water efficiency and tower capacity [78]. In his addition, Bluhm [78] has introduced a new term called ‘N’ to represent the number of stages in the chamber and related this number to merit factor and tower capacity of the system. See equation (9 and 10) below. 𝜂𝑤 =1 − 𝑒𝑥𝑝 [−𝑁(1 − 𝑅)]1 − 𝑅 𝑒𝑥𝑝[−𝑁(1 − 𝑅)] 𝑤ℎ𝑒𝑟𝑒, (9) 𝑁 =𝐹𝑅0.4(1 − 𝐹) (10) As seen from the equations above, with inclusion of these relationships one can now predict the number of stages by knowing the inlet parameters and setting the desired outlet conditions. Such that, through usage of these relationships one can also predict minimum stage number to be built based on the expected results. 51 Stages, as it can be remarked from Figure 3, can be simply put as compartments inside the chambers. They are not necessarily built separately but can be considered as sections of the cooling chamber. What differentiates them is their water injection temperatures. Water, having injected in the most primary stage of the chamber, is then collected, and re-sprayed in the second stage and recollected and sprayed in the third stage and so on… According to Bluhm et al. this cascading water injection pattern creates a counterflow effect inside the chamber [51]. Secondary and tertiary stages, as they do not receive water from the plant directly, use the water of the primary stage. Therefore, mw at the beginning, in operation, represents the freshwater flow from the plant and does not include the recycling water inside the chamber. This comment is important to understand the model build-up process for the numerical model to be explained in the forthcoming chapters. See below table, Table 7 , for some generic merit factors for different number of stages used in the chamber. More detailed list of merit factors will be provided later in the following chapters. Table 7 - Standardized Relationship Between Number of Stages and Merit for a BAC System Number of Stages (N) Factor of Merit (F) 1 0.40 – 0.50 1.5 0.50 – 0.60 2.5 0.60 – 0.70 3.5 0.70 – 0.75 As a very important note, although the merit factor is somehow related to the number of stages here, it is not dependent on the number of stages implicitly. See, factor of merit is intrinsic to tower capacity and chamber effectiveness. These parameters can be improved (finer spray, 52 different nozzle design or installation etc.) or diminished (low pressure injection, coarser spray etc.) without increasing or decreasing the number of stages. In other words, theoretically, a very effective single stage cooler might provide similar merit factor range in comparison to moderately performing multistage cooler. This may seem to create a contradiction with equation (10), however, here it is important to see that these numbers are developed in accordance with a series of standardized conditions and testing methods. Therefore, their fitness is limited to the conditions provided by the design guidelines and otherwise they might deviate from real results. This is also important to understand for numerical modelling purposes during validation. To better understand these design recommendations, let us examine the proposed design guidelines set by Bluhm et al. [51]. 2.5.2. Full-Scale Bulk-Air-Cooler Design Recommendations In their paper, Bluhm et al. [51] has provided a series of design recommendations for building an industrial size bulk air cooler. These design recommendations were covering not only operational parameters but also it was providing geometrical set of recommendations for construction as well. This section is dedicated for introduction of these design parameters and the content here is mostly adapted from the cited paper unless mentioned. 2.5.2.1. Distribution of Air and Water in the Chamber Cavity As it can be logically expected, it is highly recommended to have a uniform distribution of the fluids (both air and water) across the chamber cavity. Using the available space as effective as possible is key to enhance the performance of the system as recommended by Bluhm et al. [51]. They recommend having a smooth entrance for air flow at the inlet of the chamber is important 53 to condition the airflow upon entrance into the chamber [51]. Although, at that point they were not able to provide an optimum flow speed for the air, they have mentioned that they have tested the coolers having up to 6 m/s airflow and did not recognize any performance issues [51]. On the other hand, to provide a uniform distribution of droplets across the chamber section they recommend using nozzles installed at the lower corners of each section, facing each other with an angle (~ 65ᵒ) [51]. By using the fan like pattern of the injections this creates a good coverage of the available volume of the chamber. As an important note, they have also mentioned that water sprays should be sprayed at a minimum velocity to reach the roof the chamber to ensure full coverage of the higher parts of the cavity as well [51]. A figure (Figure 11) is reconstructed from the illustrations shown in [51]. Figure 11 - A Typical Nozzle Arrangement and Water Distribution as Explained (After Bluhm) 2.5.2.2. Distribution of Nozzles Proper distribution of nozzles is essential for an effective cooling as it mainly dictates the water injection physics to the bulk-air-cooler cavity. This process impacts the water-air interaction and cooling effectiveness. For example, nozzles installed too far from each other might cause less 54 effective cooling due to insufficient coverage or too close installation could result in underutilization of water due to overlap in the space. Accordingly, Bluhm et al. has suggested a volumetric approach to define distribution of nozzles [51] inside the bulk-air-cooler. Here, it is important to note that; the distribution of nozzles, their physical distancing from each other, their injection angle and pressure can all vary in between different operations, and it is difficult to standardize them. Especially geometry and size of the cooler is hard to designate as they are related to other factors such as space availability, cost of construction etc. Nevertheless, nozzle distribution should be done in such a way that it maintains couple of important conditions as suggested by Bluhm et al. [51]: Water Loading: Water mass injected to the cooling chamber should not exceed 0.25 l/s per m3 of air provided to the cooler with an equal distribution of water mass to the cooler cavity [51]. This design condition can be studied to further understand the importance of water loading in industrial scale bulk-air-cooler designs. However, for this study mentioned loading rate is accepted as optimal. Nozzle Mass Injection Rate per Nozzle: Mass flow per nozzle can be set around ~ 1 l/s [51]. This design condition is also important and can be flexible depending on the available space, nozzle type, pumping capacity etc. if coverage and water loading conditions are met. Injection Pressure: There could be different types of nozzles and each nozzle might produce different scale of droplets and injection velocities depending on its design and inline water pressure. Therefore, to make a fair estimation Bluhm et al. recommends that the sprays should 55 produce droplets that have enough initial velocity to reach the roof of the chamber (or ~4 m/s) [51]. 2.5.2.3. Staging within the Chamber In most of the cases, bulk-air-coolers are designed in stages and staging within the chamber is an important concept that defines the ‘bulkiness’ of the cooling process by shaping the heat transfer mechanism inside the chamber. According to Bluhm et al. [51] stages should be installed in a way that; the primary stage receives the ‘coldest water’ and should be closest to the outlet of the chamber. Then, the water collected at this stage can be pumped to the secondary stage and same process for the tertiary stages. As mentioned earlier, according to Bluhm et al. [51] this creates a ‘counterflow effect’ in the chamber for more effective cooling. As seen in Table 7, merit factor of the chamber increases with increasing number of stages. This shows the impact of reutilization of the injected mass over and again in successive stages. However, Bluhm et al. have observed minimal impact on merit factor after 4 stages [51]. Note that, at steady-state, water mass in the cavity is simply 𝑁 × 𝑚?̇? as the water injected to the primary stage leaves the chamber from the last stage while primary stage is fed with fresh water of 𝑚?̇?. A simplified representation of staging is shown in Figure 12 below. Figure 12 - A Simplified Graphical Representation of Multi-Staging for a 2.5 Stage Cooling Chamber 56 2.5.2.4. Droplet Size Droplet size is one of the most prominent factors impacting the overall performance of bulk-air-coolers, as it dictates the overall contact between the water and air during the heat exchange. Referring to Bluhm et al.’s earlier work, it is understood that there were not many analyses done investigating the impact of droplet sizes concerning BAC performance. Although, Bluhm et al. were not able to provide full characteristics of droplet sizing for such applications, they were able to provide some insight in their paper [51]. According to discussions laid out in their work, Bluhm et al. recommends; having a droplet size range between 0.5 mm (500 µm) to 2.5 mm (2500 µm) would ensure a sufficient performance at the coolers [51]. Logically approaching the problem here, one would recognize that having finer droplet could mean several other physical impacts over the cooling system such as having fine mist and dragging issues or having high pressure at the system pumps. This was highlighted by Bluhm et al. as well signifying that droplet carryover due to drag forces might potentially impact the overall heat transfer within the chamber whereas higher pump pressure might induce extra cost on the pumping system [51]. A trade-off between cooling performance versus pump pressure, or droplet carryover has not been fully investigated yet. However, it is worthwhile to examine such relationships for further analysis of BAC systems. 2.5.2.5. Summary of the Conventional BAC Design Methods This section covered detailed literature review of underground mine cooling practices and design steps for building a full-scale bulk-air-cooling system. With these design guidelines and the mathematical model presented so far, one should be able to find out the operational parameters of an underground cooling system properly. However, as mentioned earlier, performance of these design methods is restricted to the guidelines discussed in this section and this may 57 occasionally create granularity problem. For example, in underground, space is limited, and one might look for a higher merit factor in a smaller space or one might use different nozzle arrangements with different injection properties than that was studied earlier. In much of these cases, it is hard to locate the required solution in the context of merit factors that are studied for limited geometry, size, and installation settings. These issues are expected to be mitigated with numerical methods as now the issues of granularity can be approached exactly as required and proper merit factor assessment can be made for the specific case required. This will be covered in Chapter 4 with more detail. -End of this Chapter-58 CHAPTER 3 – Renewable Mine Air Conditioning Systems: Case Studies and Techno-Economic Evaluations 3. Introduction to Case Studies and Originality of the Work This section is dedicated to case studies done on alternative mine air conditioning methods by the author and a team of researchers from the University of British Columbia and the McGill University. There are three case studies presented here covering: mine cooling with a lake sourced cooling system, reutilization of underground BAC water for mine air heating and energy storage with ice. All these studies are originally studied with real-life mine data provided by the mining company and results are published in peer reviewed journals as introduced above. Also, the lake cooling system to be shown here is designed with the semi-empirical model discussed in the previous section and lays a good example for how such large-scale cooling scenarios are to be approached conventionally. 3.1. Case Studies: A Canadian Example In this section, cooling design of a real-life, Canadian, ultra-deep mine is explained. For the first case study, the selected mine is proposed to be cooled with lake water. Although, the cooling source is lake water, steps of designing the BAC would remain unchanged and can still be employed as explained in the previous chapter (Page: 46). In the second case, a water re-utilization system will be introduced as this kind of recyclable systems could help diminishing energy costs of a mine. For this case, the used BAC water is reutilized for mine heating purposes during cold season using the same design principles. Finally, in the last case study, some energy storage systems are compared to cap from mine’s cooling energy needs. In all case studies, techno-economic details are presented and potential for savings are highlighted. Also, due to 59 uncertainties in design benchmarks, several sensitivity studies (i.e., ambient conditions, water delivery temperature etc.) were also presented where needed. 3.1.1. General Motivation It is a commonly known fact that most of the establishments in the heavy industries (i.e., extractive industries, civil and construction industry etc.) are dependent on conventional, fossil-based energy sources. In fact, it is discussed that a substantial number of Canadian mines are heavily reliant on non-renewable, fossil energy sources for operational or on-site energy generation purposes [79]. These non-renewable, fossil-based energy sources being diesel, heavy oil, natural gas, and such, are substantial drivers of the mainstream, global economy [80]. On the contrary, consensus reached by most of the large economies and member countries of OECD (Organization for Economic Co-operation and Development) and Kyoto protocol targets lower GHG (Greenhouse Gas) thresholds [80], [81]. Certainly, this creates a ‘domino’ effect which progressively compel governments and involved industries to find alternative solutions to their conventionally applied techniques. For instance, considering the mining sector in Canada only, a study done in 2019 shows that the mining industry in Canada employs more than 700 k people with CAD $ 109 Billion GDP (Gross Domestic Product) contribution to the domestic economy [82]. However, on the other hand including British Columbia and many other provinces, Canada takes big leaps towards carbon free future by introducing sharp carbon taxation to the industries that rely on fossil fuel burning. As for British Columbia for example, the carbon tax is CAD $ 45 per tonne of CO2equ as of April 2021 and will be increasing to CAD $ 50 by 2022 [83]. These two movements are creating an internal conflict that can be mitigated through employment of cleaner energies into the picture. 60 Nowadays, with rising environmental consciousness and soaring carbon taxes, mining industry remains to improve its energy outlook as rapid as possible. As mentioned by Thollander et al., heavy industries have not much time to go through a transition and might face harsh regulations due to recent global shift towards cleaner production [84]. On the other hand, renewable and advanced energy systems have always been a point of discussion for mining industry, not only for environmental purposes only but for financial reasons as well. Especially for energy intensive components of mining e.g., comminution, rock breakage, drilling and HVAC. Considering these energy intensive operations of mining, HVAC operations stand out more relatable to the advanced renewable energy systems as they are flexible and may allow easy transportation of stored energy or can simply be upgraded and improved with further research. With this motivation, a mine is studied considering several aspects of mine cooling and potential savings are identified with a techno-economic approach to achieve cleaner production with less financial burden. This section brings different studies that use the same mine as the benchmark and raises several propositions to the mentioned energy problems faced in ultra-deep mine conditioning. Note that, the studies shown here were conducted based on field data collected from the mine site and nearby resources. 3.2. First Case Study: Lake Cooling The lake cooling system, if simply put, suggests utilization of surface water sources (i.e., deep lakes, sea, ocean etc.) for ambient chilling purposes as a heat sink. These systems have been successfully used in several places in Canada [19], [43] and Europe [44] in mostly residential scale, however, have not been implemented in a mining application so far. This study puts forward the 61 first full-scale, end-to-end cooling design of an underground mine using lake water. Before starting to the design procedure, let us first investigate the principles of lake sourced cooling. 3.2.1. Working Principles of a Lake Cooling System Deep surface waters (i.e., deep lakes, seas, oceans, or bays) tend to form thermal layers due to several mechanisms acting on their internal convective movement. Because of the temperature and density relationship inside the water body, these layers stratify across the depth of the lake based on seasonal changes and flow regimes [85]. Year-round, these thermal layers tend to shift, shrink, or enlarge depending on the seasonal ambient temperatures and irradiation caused by the sun. However, once reached at a certain depth the lake water shows very sharp temperature decline (also referred as thermocline hereafter) protecting the layer that sits right below (also referred as hypolimnion hereafter) to be thermally more stable across the whole year [85]-[87]. Figure 13 below shows a generic outlook of the thermal stratifications discussed above. Figure 13 - Thermal Stratification in a Lake (After [85]) 62 Once tapped into hypolimnion zone, as lake cooling suggests, the coolth of the water could be extracted by several means. Next section will be elaborating on the methods that can be used to deliver coolth to the end user. 3.2.2. Mode of Coolth Delivery from Lake to Mine Depending on the environmental concerns, permits acquired, proximity of the mine to the lake, thermal effectiveness and losses associated with the coolth transfer; several different techniques could be employed in extracting the coolth from surface water bodies. These systems are usually named after how the water / coolth extracted from the lake and how the extracted coolth interacts with the mine air. Open-loop, direct system: These are the systems that physically take out the surface water and use it directly on the ambient or medium to be cooled (i.e., mine air). These systems are named open loop as they directly take the lake water and displace it to the mine via submerged or suction pumps. Also, these systems are named ‘direct’ as they get into direct contact with the mine ambient or intake air. To operate these systems, a water treatment cycle is required to be planned and a bio-chemical water quality monitoring is required to be applied before replacement of the taken-out water mass back to the environment. However, as the contact occurs at the intake contamination of the water is usually expected at low levels. Open-loop, indirect systems: These systems are arranged in such a way that; the surface water is displaced like the previous method, but not used on the ambient air directly. Rather, in these systems, taken out water is fed to a closed loop for indirect heat exchange via closed loop spot coolers or a central fan coil system. One advantage of these systems is that they would not 63 require water treatment as the circulating water does not meet mine air in any way. These systems may perform poorly when there is a high demand of cooling (due to reasons i.e., insufficient cooling, low flux, expensive heat exchanging surfaces etc.) or the water source is too far from the mine (due to elevated inline pressure issues). Closed-loop, direct systems: Comparing to open loop systems, these systems use mine’s own water resources. In this type of scenarios, mine water is sent to a helical heat exchanger sunk into the lake and forced to travel through by undergoing a heat exchange with the lake water. Because they are direct systems mine water gets into contact with mine air. However, usage of these systems should be well monitored against for leakages and heat dump issues. Closed-loop, indirect systems: In this type of systems mine water loops through an end-to-end closed, coil system resembling a large-scale heat exchanger. These systems do not require water treatment, however, they must be regularly maintained and monitored for leakage and heat dump issues like the former case. Laid out options can be visually observed as simply sketched in Figure 14 below. Figure 14 - Different Combinations of Water Looping Scenarios Applicable to Mining Settings 64 Here, it shall be noted that due to several other financial and engineering design purposes, first mode of delivery was selected for the case study discussed here. Note that, there could be several reasons in the decision making during this phase. These factors could be related but not limited to proximity of lake to the mine, seasonal changes and habitual needs around the lake, environmental permitting, availability of water treatment, size of the investment and savings and so on [7], [8]. 3.2.3. Mine Properties and Calculating the Cooling Load The mine discussed here is an ultra-deep mine with 2.8 km planned depth. It has three main production zones with total annual production reaching up to 1.4 M tonne. The mine requires ambient cooling during the summer season via bulk air coolers. Cooling need of the mine was determined based on several criteria such as temperatures attained during the peak summer season nearby the mine site, maximum depth reached by the mine bottom and the life-of-mine (LoM) production scheduling of the mine. The selection criteria were mainly based on the ‘worst case scenario’ to better establish the capacity needed by the cooling system. Once the necessary data was collected, critical path design for the mine plan was constructed and simulated in a mine ventilation simulation software called VentSIM. Figure 15 shows the life-of-mine production schedule of the mine. As seen, the mine will be producing from all its zones in year 15, reaching its maximum operational capacity for the given 20 years of mine life. Which makes the year 15 of the mine life to be the year that has the maximum underground activity. In addition to this mechanical activity, other heat emissions will also be added to the solution. This is done with the simulation software. 65 Figure 15 - Life of Mine Production Schedule of the Mine (All 3 Zones) (After Kuyuk et al.) Additionally, year-round temperature distribution of the mine site is also one of the most important factors in designing the cooling system of the mine. This not only determines the operational schedule of the cooling plants but also, naturally; allows sizing of the plant itself. Fig. 3-6 below shows the annual temperature distribution of a weather station located nearby to the mine site. Here, note that heat simulations done for the life-of-mine model was based on the critical path for ventilation network and it is found that the critical surface wet-bulb temperature threshold is 8.5 °C. Note that, this critical temperature was estimated based on the heaviest production year assuming all-fleet members working underground at their full capacities. Note that, once the critical surface wet-bulb temperature is reached, along with other heat contributing factors, mine temperature rises above the threshold value of 28 °C at the low-level working faces according to simulation results. Therefore, the cooling period of the mine was determined to be between May and September as indicated with arrows below [7], [8]. 020040060080010001200140016000 2 4 6 8 10 12 14 16 18 20Life of Mine Production Schedule (ktonnes) ThousandsYears66 Figure 16 - Annual Temperature Distribution at the Mine Site (After Kuyuk et al.) Having considered mine production and ambient weather conditions for the heat simulations, as the last factor, underground fleet management and machine locations were determined and included in the simulation. Accordingly, following tables (Table 8 and Table 9) show the other boundary conditions implemented for the heat simulation study carried out during the plant design and sizing. Note that; here Table 8 shows the operational characteristics of the mine, whereas; Table 9 summarizes the total heat added to the network by the mine fleet and machinery. -10-50510152025Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec(°C)Yearly Surface TemperatureAmbient Dry Bulb Ambient Wet BulbCritical surface temperature indicating the beginning of the cooling period.Cooling PeriodCritical surface temperature indicating the end of the cooling period.67 Table 8 - Mine operational parameters used in the ventilation network simulation (After Kuyuk et al.) Property Value Mine Depth 2,800 m Peak Season Sur. Temp. (Dry Bulb / Wet Bulb) 23 C° / 18 C° Airflow ~1,000 kcfm Surface Barometric Pressure 1 kPa Geothermal Gradient 20 C°/ km Air Density (Compressible Flow) 1.18 kg/m3 Mobile Equipment ~4,200 kW Mine Machinery (Fans/Pumps/Battery Stations) ~3,300 kW Maximum Allowed Underground Temperature 27 C° Mine Life 20 Years Operating Days per annum (Cooling Needed) 150 Electricity Cost 9.7 cents/kWh Table 9 - Summary of the Heat Added to Network by the Mine Fleet and Auxiliary Equipment (After Kuyuk et al.) Property Heat Value (kW) 2 x U/G Booster Fans (@6850) 200 4 x Stope Fans (assumed to be in op. for each level) 110 4 x Level Fans (assumed to be in op. for each level) 620 Mobile Equipment (Battery Electric Fleet) 4,200 Mine Dewatering System (Overall) 1,600 Battery Swap System (Overall) 800 Having introduced the boundary conditions to the ventilation simulation software, it is seen that the surface plant was not sufficient to cool the lower parts of the mine due to heat added by the network and auto-compression effect. Therefore, due to positional benefits of underground plants (Remark: Table 6) a secondary plant is planned to be installed underground to back up the surface plant. This will not only help better local cooling for underground but also significantly ease the cooling load of the surface plant by dividing the overall load. 68 Accordingly, a ‘two-plant’ (surface plant and underground plant) system was selected with the resulting design criteria listed in Table 10. This design criteria is optimized in VentSIM and set the inlet and outlet requirements of the cooling plants later to be used in Bluhm’s model that was discussed earlier in page 46. Table 10 - Optimum Operational Conditions for Selected BAC Plants Cooling Plant Airflow Characteristics S/P Input: WBT @ 18.0°C – DBT @ 23.0°C S/P Output: WBT @ 7.5°C – DBT @ 7.5°C Airflow: 1,000 kcfm U/G/P Input: WBT @ 18.9°C – DBT @ 26.8°C U/G/P Output: WBT @ 8.7°C – DBT@ 8.7°C Airflow: 450 kcfm The underground plant was required to be deployed to operate ‘all-year’ to cool the deepest levels (-2000m) of the mine. This was needed for two reasons; first, even during the summer season, due to low positional efficiency of the surface plant, wet-bulb temperatures at the deep mine workings were overpassing the minimum thresholds and second, even during the coldest season, the shaft bottom and lower levels of the underground mine was still requiring conditioning due to heavy underground traffic, and impact of auto compression. A very similar case can be observed at LaRonde mine as well [2] Fig. 3-7 shows the impact of auto-compression only and preliminary design depths of the cooling plants and their impact on mine temperatures. In this case, it is shown that auto-compression adds approximately 4 °C for per km 69 down in the main intake shaft [7] similar to what was reported by Wilson for LaRonde example [88]. Figure 17 - Effect of Auto-Compression (After Kuyuk et al.) 3.2.4. Lake Water Thermal Study As introduced earlier and shown on Figure 13, deep lake water, shows a pattern of thermal stratification across its depth. A similar lake temperature profile was generated for this study as well. Figure 18 below, shows the thermal stratification of the lake nearby. It should be noted that 9 different temperature probes were sunk into the lake at different locations to collect the data as it can be seen on Figure 18. Locations of the probes sunk and their distance from the shoreline are shown on a Google Earth™ image as presented on Figure 19. Note that several measurements were taken at these locations across a full-year-period, however, no significant change in the thermocline temperature was observed. Some earlier studies in this field had also 051015202530350 500 1000 1500 2000 2500 3000(°C)Depth (m)Effect of Autocompression on Mine AirCooling No Cooling*No other heat sources added. 70 similar observations regarding year-round temperature change around the hypolimnion layer [86], [87]. Much of these measurements are conducted under a research collaboration between UBC and McGill University, supported by UDMN (Ultra-deep Mining Network) and it is important to note that; due to confidentiality agreements signed by the company and the institutions conducting this research, locational information of the lake is intentionally withheld from the context of this section. That is why in here and many relevant parts of this thesis the mine was treated anonymously. As seen from Figure 19, 9 probes were situated across the lake shoreline with distances ranging from ~140 m to 340 m from the coast. Each probe was sunk for about ~40m down from the water surface collecting temperature information across its length. Figure 18 - Lake Temperature Study Data Obtained from 9 Locations (UBC – McGill) (After Kuyuk et al.) 051015200 10 20 30 40 50Temperature (°C)Depth (m)Lake Temperature Gradient71 Figure 19 - Probe Locations and Their Linear Distances from the Shoreline (After Kuyuk et al.) Once the lake data was collected and analyzed, a critical path must be developed for the pipeline selection and water transfer study (i.e., selection of pump station locations, solar irradiation, and pipe insulation study and so on). Also, this part was essential for the cost estimation purposes as well. Therefore, to get the pipeline profile several locations were investigated. The investigation was mainly based on finding the best pathway to reach the lake water from the mine site with minimum deforestation and local community disruption. Accordingly, the best path has been chosen and the topographic profile of the pathway was reconstructed from the raw data obtained from the Google Earth™ software. Figure 20, shows a section view of the topographic profile from mine to lake. Where ‘Point A’ represents the lake water touchline, and ‘Point B’ represents the peak elevation point for pumping purposes. 72 Figure 20 - Topographical Profile Cut out From Satellite Image (After Kuyuk et al.) The lake to mine distance of the selected pathway was found to be 3.44 km. To protect the lake water from heat gains and/or heat losses with atmosphere, PVC piping at schedule 40 pressure rating with mineral wool insulation and insulation painting is considered. Following table, Table 11, summarizes the specs of the selected piping material and corresponding heat gain/loss study for the pumping system applied between the lake and the mine. Table 11 - Pipeline Specifications and Heat Loss/Gain Study Pressure rating Schedule 40 Pipeline route Direct Route (3.44km) Pipe diameter 16” Summer Peak Heat Gain (Cº) 0.47 Summer Peak Insulated Gain (Cº) 0.16 Winter Heat Loss (Cº) -2.20 Winter Insulated Loss (Cº) -0.73 2603103600.0 0.1 0.4 0.7 0.9 1.1 1.2 1.3 1.8 2.3 2.6 2.9 3.0 3.2Elevation (m)Distance (km)Topographical Profile of the Pipeline Route73 3.2.5. Important Highlights Regarding Conventional Designs As seen so far, two underground plants are selected, and their performance are ensured with ventilation simulation software making no place on the critical ventilation path to go above regulatory temperature limits. This information (i.e., desired inlet and outlet conditions of plants, lake water temperature, air mass flow etc.) will provide the designing engineer to what to plug into the equations seen in page 46. However, also note that there are couple of important points to be made at this point to emphasize the importance of the mentioned granularity problem to the audience: - Boundary conditions of the simulations conducted in the ventilation simulation software are set based on ‘worst case scenario’ as mentioned before. This means that the simulation software is set to mimic the worst day of the mine in terms of heat generation to see the impact of the peak temperature at the site. Logically, this so-called ‘worst day’ will happen during the hottest day of the year that the mine is producing at its maximum production rate (i.e., max crew and max fleet size in underground etc.). - However, this peak work happens only during a very limited time of year 15 (See: Figure 29 and Figure 15) (i.e., month of July of the 15th year of the mine) and for the rest of the operational timeline mine will be demanding less performance from the cooling plants. - This creates the under-utilization problem mentioned in Table 3. - Also, no physical design features of both cooling plants can be questioned at this point. They are assumed to be horizontal BACs with 3-stages. However, this aspect is crucial for installation of both plants. Especially for underground plant, the space limitations may make engineers to go with a different design than that was discussed in page 52. Meaning 74 that their design is strictly limited to the criteria set by Bluhm et al. and cannot be modified unless numerically modeled. - This creates the granularity problem that was mentioned several times so far. Now let us explore the plant design process through utilization of the semi-empirical design steps discussed earlier in Chapter 2, page 46. 3.2.6. Plant Sizing and Design Process Based on the selection matrix shown on Table 6 and some of the technical reasons specified in this chapter (i.e., operational heat and auto-compression), it was decided to move on with two-plant scenario (i.e., surface plant or SP and underground plant UGP). This was verified with the critical ventilation path heat simulations as well, resulting in max temperature at the ‘worst case scenario’ still just below the regulatory limits. Based on this two-phased cooling one plant was sized to be located at the surface, nearby the intake shaft and the secondary plant was designed to be installed at the (-2,300 m) level. The secondary plant is to be installed not only to increase the positional efficiency of the cooling but also needed to downsize the capacity of the surface plant by enabling it to operate only during the peak summer season by deliberating the year-round deep cooling needs of the mine. Remark that the plant design shown here is carried out on the empirical relationships stated by McPherson, Bluhm and Whillier as cited in [37], [39], [51]. 3.2.6.1. Surface Plant Design and Relevant Sensitivity Studies Based on the baseline studies done on the lake water and the heat gain/loss calculations conducted during pipe selection, it is seen that the lake water reaches at the mine site at around 75 ~6 °C temperature. However, due to potentiality of the unforeseen circumstances, immaturity of the pipe design and lack of field data a sensitivity study was to be conducted to see the operating ranges of the cooling plants for the given range of water temperatures. Also, it is important to note that; the empirical chiller design relies on a selection factor called the “merit factor” referring to the aggressiveness of the cooling at the plant [39] and this to be selected based on staging, size and orientation of the cooling plant. For the surface plant design case, mentioned “merit factor” was selected as 0.7 as the design was incorporating a three-staged, horizontal spray chamber. Table 12 shows a detailed list of merit factors for different arrangements. Using the inputs shown in Table 8 and through employment of the semi-empirical solution shown in page 46 for the selected merit factor one could reach to the results shown in Table 13. It should be noted that the primary goal to run this work was to find out the water mass flow rate drawn from the lake per second to design a proper pumping system. Table 12 - Typical Merit Factors for Different Applications (After McPherson) Type of Unit Merit Factor Vertical Spray filled towers (No packing) High water loading 0.5-0.6 Low water loading 0.65-0.75 Industrial packed tower 0.7 to 0.8 Horizontal Spray Chambers Single stage 0.4 to 0.5 Two stages 0.58-0.68 Three stages 0.65-0.75 Packed air coolers Crossflow 0.55-0.65 Counterflow 0.68-0.78 Mesh coolers 0.55-0.5 76 Table 13 - Operational Parameters of the Selected Surface Plant Property Value Factor of Merit 0.7 Air Input Temperatures DB/WB 23°C / 18°C Airflow ~1,000 kcfm Barometric Pressure 1.00 kPa Power 14.7 MW Air Output Temperatures DB/WB 7.5°C/7.5°C Water Flow Rate (with make-up) ~550 l/s Water Outlet Temperature 12.7°C As seen in Table 13, a surface plant of 14.7 MW worth of cooling was designed at a water rate of ~550 l/s including ~3.5 % make up water. Note that, this first plant is designed to handle all the intake air at once at the entrance of the mine bringing it down to 7.5°C WB outlet temperature. This would be enough to cool down the main working faces of the mine. However, as the air moves deep into the mine it requires a secondary cooling due to temperature raise above the 28°C WB temperature at the deeper workings. Following figures, Figure 21 and Figure 22 shows the results of the sensitivity studies conducted on the water delivery temperature and the ambient air conditions around the mine site due to high risk of contingencies in these operating parameters. To elaborate, as seen in Figure 21 below, the design water delivery temperature at 6 degrees is capable to provide up to 16 MW of cooling for the ambient temperature of 19 °C WB. This range shows enough confidence for surface cooling of the mine based on the heat simulations. Note that; for this sensitivity chart some operating conditions were kept constant and noted on the figure. Note that, any compensation in the water delivery temperature would result in lower 77 plant power or less durability based on the ambient temperature. Therefore, as an important note to take, one should consider the fact that, as the mine’s life-of-mine cooling demand is unlikely to change, unless there is an expansion or shut-downs, and climatic conditions are rather stable, any issues due to water temperature instability can only be mitigated with more water. This is considered in pump and pipe selection. Figure 21 - Sensitivity Study Over the Water Delivery Temperature Showing Maximum Attainable Cooling Power and Maximum Tolerable Ambient Temperature at the Mine Site for the Given Design (After Kuyuk et al.) On the other hand, Figure 22 gives another angle to look at the design provided. As seen increasing ambient temperature as a slight curvilinear relationship with the cooling power attained at the plant for the given mass flow of the water. In other words, Figure 22 indicates 051015202505101520255 5.2 5.4 5.6 5.8 6 6.2 6.4 6.6 6.8 7Maximum Tolerable Ambient Temperature (C)Max Attainable Cooling (MW)Chilled Water Temperature (C)Inlet Water - Ambient Air - Max Achievable Power - Surface PlantMaximum Achievable Power Maximum Allowable Ambient WBTOperating Conditions:Water Flow Rate: 550 l/sAir Outlet Temperature: 7.5 CAir Flow Rate: 995 kcfm78 that any climatic uncertainty towards local warming of the ambient temperature at the mine site would require increase in the of lake water with given lake temperature. Dashed line shows the current conditions. Remark Figure 16 for the annual temperature distribution at the mine site. Figure 22 - Sensitivity Study Showing Cooling Power and Lake Flow Rate (After Kuyuk et al.) 3.2.6.2. Underground Plant Design and Relevant Sensitivity Studies Underground plant design was also made based on the same design principles. It is considered as a three-stage, horizontal spray chamber with a merit factor 0.7 like the surface plant design. Table 14 shows the operating conditions and the design parameters of the underground cooling plant. 010020030040050060070080090010001100120013000510152025303518 19 20 21 22 23 24 25 26 27 28WATER FLOW RATE (L/S)COOLING POWER (MW)AMBIENT AIR TEMPERATURE (C)Ambient-Power-Flow SensitivityAmbient Air vs. Power Water Flow RateOperating Conditions:BAC Outlet WBT: 7.5 C, 100% Sat.BAC Inlet Chilled Water Temp: 6 CAirflow: 995 kcfmMerit Factor: 0.7079 Table 14 - Operational Parameters of the Underground Plant Property Value Factor of Merit 0.7 Air Input Temperatures DB/WB 27.1°C / 18.9°C Airflow ~450 kcfm Barometric Pressure 1.23 kPa Power 7.1 MW Air Output Temperatures DB/WB 8.7°C/8.7°C Water Flow Rate 250 l/s Water Outlet Temperature 13.4°C Here, it is important to notice that the underground plant will be deployed after year ten. This is due to several reasons. First, the mine production and underground traffic will still be ramping up to its maximum rate by then and the surface plant would be enough for cooling the mine until its tenth year. Secondly, mine development must reach to -2,300m as the secondary plant is required to cool the levels between -2,300 m and -2,800 m otherwise it would lose its positional efficiency. If not, it must be designed bigger in size at upper elevations, this could not only cost more money to the company but also would result in less optimized cooling design. Additionally, it should be observed that the underground cooling plant would be dealing with ~450 kcfm of the overall mine air due to its location and the ventilation needs of the downstream working faces. The rest of the air (~500 kcfm) would already be used at the upper workings and by passed to the surface through the return air raises and main exhaust shaft. Another important factor to remark here is that the underground cooling plant because it will be handling the deepest parts of the mine (levels between -2,300 m and -2,800 m) it would be running year-round. Therefore, it could be simply put that, during the winter season (when the mine’s upper levels would require heating of the intake air, the return water of the underground cooling plant would have some 80 calorific value to be extracted. This will be discussed during the mine heating section of the same mine. Finally, as another design concern for the underground cooling plant, one should remark that the heat gain of the coolant water at the pipeline due to friction. This is a critical design consideration as the water send to underground has a great amount of potential energy and during its movement in the pipe, this results in two outcomes; pressure build-up at the pipes and the heat picked up because of the friction. To control this, coupled pump – turbine installations were considered. This arrangement would not only minimize and mitigate the heat gain issues at the pipe run as it converts the potential energy of the water to kinetic energy for recovery purposes with high efficiency but also acts as pressure relief system in the pipe network and minimizing the pressure related problems. This study is done by a separate team of researchers working on the project and its details can be found in the following study [24]. However, to shortly note; three different locations at the pipe run were picked for ‘Pelton’ turbine installations with turbine-pump coupled system [24]. Turbine locations were selected as -2,000 ft, -4,800 ft and -6,850 ft levels [24]. For the underground bulk-air-cooler, the intake temperature is rather stable and does not depend on the climatic conditions but is controlled by the performance of the surface plant. Assuming the plant would be running properly, and the underground traffic is at its normal levels the underground cooling plant will be receiving a fixed wet-bulb temperature at around 18.9°C. Therefore, it was not seen necessary to conduct a sensitivity study for the ambient conditions of the underground BAC. However, as the water delivery temperature could fluctuate due to contingencies, a similar; water temperature sensitivity analysis was conducted and shown on Figure 23. 81 Figure 23 - Sensitivity Study Over the Water Delivery Temperature Showing Maximum Attainable Cooling Power and Maximum Tolerable Ambient Temperature for the Underground BAC (After Kuyuk et al.) As seen, logically, plant behavior shown in Figure 23, under the given set of conditions, resembles Figure 21 as in the case for the surface plant. As underground mining conditions are very tentative to change it is very critical to study the ranges of safe working temperatures and know what actions to be taken when necessary. This is simply another reason to make all the designs based on worst possible conditions. This not only minimizes the number of unforeseen events in the underground workings but also, sets an operational limit to test, observe, and make an action plan accordingly. 05101520250123456789105 5.2 5.4 5.6 5.8 6 6.2 6.4 6.6 6.8 7Maximum Tolerable Mine Air Temperature (C)Max Attainable Cooling (MW)Chilled Water Temperature (C)Inlet Water - Ambient Air - Max Achievable Power - U/G PlantMaximum Achievable Power Wet Bulb TemperatureOperating Conditions:Water Flow Rate: 250 l/sAir Outlet Temperature: 8.7 CAir Flow Rate: 450 kcfm82 Nevertheless, underground ambient temperature is usually closely controlled by the primary cooling plant and the underground production plan (traffic, personnel, workshops etc.). These factors are mostly planned, and their operations are often closely monitored or automated. Accordingly, ambient air temperature sensitivity study for underground mine was skipped as no sharp changes were expected in the mine atmosphere due to abundance of controllable factors. However, as another important factor, for pipe and turbine selection, a water mass flow sensitivity study for the underground plant was required. In this sense, Figure 24 was developed by iteration of different flow rates under the given set of conditions. Figure 24 - Sensitivity Study Over the Water Mass Flow Rate Showing Maximum Attainabe Cooling, Power and Maximum Outlet Temperature for the UG BAC (After Kuyuk et al.) 0123456789101112012345678910150 250 350 450 550 650 750 850 950Water Flow Rate (l/s)Output Wet Bulb Temperature (C)U/G BAC Power (MW)U/G BAC Power DeliveryU/G BAC Power Delivered Output Wet Bulb TemperatureOperating Conditions:Inlet Water Temperature: 6 CInlet Air WBT = 18.9 CAir Flow Rate: 450 kcfm83 In Figure 24, it is important to observe that; increasing water flow rate to infinity will only push the outlet air temperature to its theoretical limit where the water temperature will almost be equal to the air outlet temperature. However, under given set of conditions and according to the results of the mine air network simulations, outlet air temperature of 8.7 °C was found to be optimum. Although, delivering more water would increase the cooling performance of the UG cooling plant, it would cost more water delivery, higher pumping cost, larger pipes and so on. Finally, for designing the lake water pumping systems and the capacity of the water treatment plant, overall water flow rate and associated temperature relationship was driven in Figure 25. This relationship was studied to find out the amount of water delivery considering the potential fluctuations in the water delivery temperatures. This relationship can also be used to find out how much oscillation contingency could be given to the employed system by introducing a safety factor in designing the entire water delivery system and pump stations. Note that like other sensitivity charts shown above, all the minimum design conditions were set, and all the set values are indicated on the figure. As a conclusive remark, it should be remembered that the cooling plants in this study was done based on three-stage, horizontal bulk-air cooler as no further staging is found necessary after 4-stages according to the tests conducted by Bluhm [51]. Figure 25 shows the overall design sensitivity to the chilled water temperature showing the minimum water mass flow rate to compensate the given set of operational conditions at a merit factor of 0.7. Remark that, red-dashed lines mark the design points as discussed on Table 13 and Table 14. 84 Figure 25 - Overall Water-Flow vs. Water Temperature Curve for the Given Design Criteria 3.2.7. Hydraulic Energy Recovery System As mentioned earlier, a standalone hydraulic energy recovering system was also considered coupled to the lake cooling system discussed above. In this study, three different pump – turbine combinations were studied techno-economically, and conclusions were drawn mostly based on project financials but also operational conditions, availability of underground space and sizing of the turbines. Based on these parameters following three options were studied: y = 21.538x2 - 135.57x + 565R² = 0.9991y = 13.734x2 - 113.25x + 422.38R² = 0.997601002003004005006007004 4.5 5 5.5 6 6.5 7Water Flow Rate (l/s)Chilled Water Temperature (C)Water Flow - Temperature SensitivitySurface Plant Water Flow Rate U/G Plant Water Flow RateOperating Conditions:S/P BAC Outlet: 7.5 C @ 995 kcfmU/G BAC Outlet: 8.7 C @ 450 kcfmMerit Factor: 0.70Datum Temp: 23DBT/18WBT85 a. Underground cooling without any turbine system installed, b. Underground cooling with turbine shafts coupled to pump shafts for minimizing the mechanical to electricity energy conversion, c. Underground cooling with turbine generating electricity and collected through electrical substations. Details of this study are withheld from this thesis as the content was considered outside of the scope of this thesis. However, more detail regarding this selection process can be found in [24]. 3.2.8. Financial Breakdown of Conventional Refrigeration vs. Lake Cooling System Once the design parameters set and proper sizing and selection for equipment, spare parts, installation, and maintenance was carried out, a cost comparison was made against the quote received by a third-party consulting firm. The design proposed by the consulting firm was based on utilizing the mine water available on the mine site and using conventional plantrooms and refrigerant based chilling plants. The comparison done on lump sum of main costs required by each system. All the financial figures shown here were based on real industry figures and cost models of year 2017 and can be carried forward from that that with annual inflation. According to the project quote received from the consulting firm, the mine introduced above is to be cooled with a combination of surface plant and underground plant as similarly suggested. However, employment of conventional refrigeration plant and all other associated costs yielded an upfront investment of CAD $ 52.4 million for the overall CAPEX of the project. Although, details are confidential, it is fair to share that most of this cost is associated with plantrooms and machining of the refrigeration systems. These costly parts yielded nearly 50% of the overall investment (i.e., 86 CAD$ 21.3 M for SP and CAD$ 31.2 M for UG BAC). Following table, Table 15 shows the cost breakdown of the lake cooling system. Table 15 - CAPEX Breakdown of the Lake Cooling Project versus Conventional Plant Installation (After Kuyuk et al.) Item (Surface Plant) CAPEX ($ CAD M) Item (Underground Plant) CAPEX ($ CAD M) Pipeline from lake to mine (PVC-SCH-40) 2.9 Pipeline (UG loop) (Ins. and Pur.) (ST-SCH 80) 8.5 UV resistant painting and insulation 0.3 Pump (water from UG to surface) 3.5 Pipeline foundation and construction Pump (water from lake to mine) 2.2 2.4 BAC (horizontal, 3-stages) Pelton turbines (x3) 6.0 4.6 BAC (horizontal, 3-stages) 2.5 Water Treatment 0.8 Electrical power line distribution 0.9 Project management (15%) 3.5 Control and instrumentation Project management (15%) TOTAL S/P conventional refrigeration CAPEX 1st Stage (0-10 years) CAPEX savings 0.7 1.8 13.7 21.3 7.6 TOTAL U/G conventional refrigeration CAPEX 2nd Stage (10-20 years) CAPEX savings OVERALL PROJECT SAVINGS 27.0 31.2 4.2 11.8 As seen from the Table 15 , the lake cooling system yielded CAD$ 11.8 million less value than the conventional system mostly because it does not require very expensive, large scale refrigeration plantrooms and expensive compressors. Also, because it does not require any refrigerant it does not have the risk of coolant leakage and associated safety issues. 87 3.3. Second Case Study: Reutilization of the Return Water of Underground Cooling Plant As a follow-up work (i.e., outside the cooling system but part of the water circulation system) another study was made to uncover the financial potential of the calorific value of the underground cooling system return water and to show another use of surface bulk-air-coolers. As explained in the earlier chapters, the surface bulk-air-cooler system is only in operation during summer season and under normal circumstances, it will be temporarily shut down during wintertime as it has no use. On the other hand, same mine looks for minimum 5 degrees Celsius shaft temperature during the wintertime to protect shaft liners, hoisting system and other service lines from freezing. However, as it can be seen from Figure 16, the shaft temperature drops down below 5°C between November to March next year. The conventional method to mitigate this shaft freezing problem is to deploy burner systems operated with fossil fuels mostly propane or natural gas. Although, conventional system works effectively in heating the mine air, it is still subjected to the same limitations associated with fossil fuel burning on site and may not be deemed as the best solution in terms of environmental aspects. As also touched upon earlier, the mine of interest here utilizes its underground bulk cooler year-round as it requires cooling at the lowest level considering minimum 5°C shaft temperature at the intake shaft during all-seasons. Considering the underground lake cooling system does still require to pump the used water at the underground BAC to the surface, an idea is proposed to reuse the surfaced water at the temporarily (seasonal operation) decommissioned surface plant for mine heating purposes in the conducted study [89]. 88 3.3.1. Mine Bulk Air Heating System (or Reverse BAC) Comparably alike to mine bulk air cooling system, a bulk air heating system is considered with the same operating principles set for horizontal water spray chambers. As it may be guessed, mentioned bulk air heating system here, operates in accordance with the same principles with the cooling towers. As stated by McPherson, although the physical design of such systems may differ, their behavior is very similar [39] in terms of the amount and the efficiency of the heat exchange. The common factors in design might be listed as: - Supply water mass flow rate, - Supply water temperature, - Air mass flow rate, - Inlet psychrometric conditions, - Duration and contact mechanisms between the air and the water [39]. Approaching the issue with this perspective, the surface plant is used to spray the hot return water from the underground plant to warm the intake air. Originally, air heating system was a conventionally designed burner system. Here, it is important to note that this system is considered as a backup system to cut down from the natural gas burning at the burners not to fully replace the heating system. Therefore, the surface bulk-air-heating (BAH) system proposed here does not intend to save from the capital expenses related with the burner system but aims to save from its operating expenses. However, 89 the system offered might as well contribute lowering the required capacity of the burner system and could help diminishing the CAPEX size of the original burner system. The main reason to use the surface spray chamber as a backup system is the freezing of the circulating water. In other words, if the air temperature at the intake of the surface spray chamber drops below -7°C for the given circulating water mass flow rate of 250 kg/s the bulk of the sprayed water drops below the freezing temperature. This might as well be considered as an advantage in some other case, however, for this case selected water delivery system and turbines are not considered compatible to handle water slush as the returning chilled stream. Therefore, a temperature cut-off of -7°C was established to back up the surface burner system. Also, it is important to mention that the original, conventional burner system was to burn around ~2.6 million m3 of natural gas annually at a cost of CAD $ 670k. Additionally, it is essential to note that for the months of March, April, and November (see Figure 16), the local ambient temperature is above -7°C temperature. Therefore, during this period the intake air could directly be streamline to the surface BAH with a SCADA controlling system. These kinds of complex decision-making systems integrated with fuzzy logic and digital twin system are gaining rapid attention in mining as they can yield very significant savings [90]-[92]. A simplified sketch of the heat exchanger loop between the underground cooling plant and the surface cooling plant is shown in the following figure, Figure 26. 90 Figure 26 - Heat Exchanging Loop between the Underground Cooling Plant and the Surface Bulk Air Heater During the Mine Heating Days (After Kuyuk et al.)91 It is also considered that; during the heat exchange between cold air and hot water, some local freezing could create slush and local freezing of the sprayed water. However, the sheer amount of water remains liquid at around ~1°C temperature. In one hand this could be considered as a helpful phenomenon in heating the intake air due to latent heat transfer between the freezing water and cold air. Nonetheless, this effect due to its negligibility in overall design, is not included in calculations. Once the minimum temperature threshold was set, mine intake air heating days were calculated for the days that the ambient temperature falls below the given threshold. Moreover, remark that the underground plant is to be deployed after 10th year of the operation as discussed earlier. Therefore, this system and savings associated with it are only considered for the second half of the given 20 years of mine life. Other system design parameters and details are shown in Table 16 below. Table 16 - A Summary of the Operational Parameters Set for the Bulk-Air-Heating System Design Property Value Mine Depth 2,800 m Underground Plant Operating Days 365 Days Intake Airflow ~1,000 kcfm / 472 m3/s Surface Barometric Pressure 1 kPa Circulating Water Flow 250 kg/s Circulating Water Temperature (at UG Plant outlet) 13.4 C° Air Density 1.12 kg/m3 Natural Gas Pre-conditioning (air outlet temp. up to) -7 C° Bulk-Air-Heater Pre-conditioning (air outlet temp. up to) 5 C° Circulating Water Temperature (at Surface Plant outlet) ~1 C° Mine Life 20 Years UG Plant Deployment Year 10th Year Electricity Cost 9.7 cents/kWh Natural Gas Cost 0.2437 cents/m3 Natural Gas Calorific Content 37.82 MJ/ m3 92 As discussed in the table above and earlier, -7°C of minimum temperature limit was set to BAH intake. Therefore, any air that is colder than the set minimum intake temperature should pass through the surface burners. Using the generic equation of sensible heat transfer as seen below one can calculate the heating wattage (WNG) that to be backed by the burners. Note that; here mair stands for the mass of airflow passing through the burners, Cpair is the specific air of the air and ΔT is the temperature difference between the burner intake and the burner outlet. 𝑊𝑁𝐺 = ?̇?𝑎𝑖𝑟 . 𝐶𝑝𝑎𝑖𝑟 . ∆𝑇 Once the total heat is calculated by using the time of operation (top) total energy (ENG) needed for each month is calculated. 𝐸𝑁𝐺 = 𝑊𝑁𝐺 . 𝑡𝑜𝑝 Having calculated the monthly energy needed from the burners, one can calculate volumetric flow for the natural gas by using calorific conversion factor of ~38MJ/m3. Following table, Table 17, summarizes the calculations mentioned above. Also, note that as the temperatures of March, April and November are not below -7°C they are not included to the burner system sizing calculations. See table below. 93 Table 17 - Volumetric Calculations for the Natural Gas Backed-up System (coupled to surface BAH) (After Kuyuk et al.) Using the same logic introduced earlier, a second table is generated by decoupling the surface bulk air heating system as in the originally proposed, conventional heating system. As seen from the table below, Table 18, as now higher ΔT values are to be attained with more heating days (i.e., March, April and November included) the plant requires almost ~5 times more natural gas burning and 4.6 k tonne per year more carbon emission to the atmosphere. This property is also factored in for the financial calculations. Table 18 - Volumetric Calculations for the Conventional Gas Burner System (only) (After Kuyuk et al.) Month Av. WB Temp (C°) NG Pre-conditioning (C°) 𝑪𝒑𝒂𝒊𝒓 Operating Hours Heating Power (W) Energy (MJ) N.Gas Vol. Flow (m3) NG Cost (CAD $) January -14.3 -7 1004 720 3,874,077 10,041,607 265,518 64,699 February -12.3 2,812,686 7,290,482 192,774 46,974 March -6.8 - - - - April 0.5 - - - - November -2.6 - - - - December -10.2 1,698,226 4,401,800 116,392 28,361 TOTAL 574,683 140,034 Month Av. WB Temp (C°) NG Pre-conditioning (C°) 𝑪𝒑𝒂𝒊𝒓 Operating Hours Heating Power (W) Energy (MJ) N.Gas Vol. Flow (m3) NG Cost (CAD $) January -14.3 5 1004 720 10,242,422 26,548,359 701,987 171,054 February -12.3 9,181,031 23,797,234 629,242 153,328 March -6.8 6,262,207 16,231,639 429,194 104,582 April 0.5 2,388,130 6,190,032 163,676 39,883 November -2.6 4,033,286 10,454,276 276,430 67,358 December -10.2 8,066,571 10,454,276 552,860 134,716 TOTAL 2,753,388 670,922 94 Additionally, as seen in the sensitivity study below in Figure 27, both scenarios respond the parameter changes linearly due to linearity of the governing equations. However, as also seen, the surface heating system with the BAH system coupled, responses to the parameter changes slower than the system without the BAH. This slope difference between two systems occurs due the fact that the system without the BAH system must deal with more heating days. This not only makes it more costly on its own, but also makes the system more vulnerable to the governing parameters in terms of cost. Dashed line shows original design. Figure 27 - Parametric Sensitivity Study on Operational Parameters (i.e., airflow, cost of fuel or ΔT) (After Kuyuk et al.) $0$200,000$400,000$600,000$800,000$1,000,000$1,200,000-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%System CostChange in Parametersw/ BAH System w/o BAH System95 In summary, reutilization of the underground BAC circulating water at the surface plant, during harsh winter times, would create significant savings from natural gas burning as the enthalpy of the return water is sufficient to upgrade the intake air temperature reasonably. This study has shown that, for the scale of the operation shown here, more than $ 500k saving is possible (incl. carbon savings) with such an application. 3.4. Third Case Study: Plant Downsizing (a.k.a. Peak Shaving) with Solar Panels (Ice Storage for Cooling) As discussed in the earlier parts of this chapter, the cooling plant is designed considering the worst-case conditions (i.e., hottest during summer season). Observing following figure (Figure 28 from [93]) as modified from Figure 16, shows the annual temperature distribution at the mine site and the cooling required to maintain the mine. See, the original surface plant was designed to handle 18 ◦C surface wet bulb temperature. This performance requires 17.4 MW of cooling at the plant. The goal of this study is to investigate if it is possible to downsize the plant by generating the same cooling on-site via solar panels. However, intermittence of solar systems requires a proper storage system for system continuity and safety of the operation. This study investigates feasibility of two storage systems (i.e., battery and ice storage) by bringing lower design criterions for the plant design for CAPEX downsizing. To do so, several temperature setpoints are set as shown on Figure 28 and they are all examined in terms of energy generation, storage, and plant downsizing. Here it is worthwhile to mention that ice generation (or battery storage) itself would require some capital investment too and it might take a few years for the financial break-even to be attained. However, this is investigated across the mine life of 20 years and any option paying 9 years or less is considered fairly feasible. On the other hand, plant 96 downsizing would result in less capital investment towards the cooling plant which would balance out the capital required for the ice generation. To see ‘which option is profitable’, ‘at what rate of ice generation’, ‘at what cap line’ and ‘at how big of a capital investment’ a decision-making matrix was generated, and options are discussed accordingly. Figure 28 - Cap Shaving Lines Bringing Plant Design Criteria to a Lower Temperature (After Madiseh et al.) As seen from the figure, the cooling plant is clearly having its peak operating time in the month of July. As each cap line represents the new design temperature for the plant, a more detailed understanding of the July is required. Accordingly, month of July needs to be further investigated for deeper analysis of the plant performance during the month. For the analysis of the month of July, Natural Resources Canada database is referred for gathering historical climate data (i.e., daily temperature) of the same location [94]. Temperature data was averaged over past 5 years. 0246810121416-10-7-4-12581114172023Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecCooling Power (MW)Wet Bulb Temperature (C°)MonthsCooling Needed S/P Ambient Dry Bulb Ambient Wet Bulb15 C Line 16 C Line 17 C Line 14 C Line 97 Cap lines and detailed daily temperature distribution of the month of July can be seen in Figure 29 below. Remark Table 13 for surface plant operating parameters. Figure 29 - Detailed Temperature Analysis of the Month of July (Avarage over the past 5 years.) (Source: Natural Resources Canada) As seen on the figure, actual design point (18 C Wet Bulb) is attained for only 3 times during the day. This can be considered as an indication of ‘over design’ and can be improved. It is worthwhile to mention that, as the cap lines move downwards two important outcome arises and the goal is to optimize in between these criterions: - Original plant design shrinks. This means ‘savings’ due to downsizing of the cooling plant. - Ice plant / battery storage duty grows. This means more capital investment would be required for building the ice cooling / battery plant and its components.10.011.012.013.014.015.016.017.018.019.020.01 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31Wet Bulb Temperature (Cº)Days of JulyDay Time (07:00-22:00) Night Time (22:00-07:00)15 C Cap Line 16 C Cap Line17 C Cap Line 18 C Cap Line98 Figure 30 below shows a simplified flow diagram of the proposed system. Figure 30 - Proposed System Flow Diagram Including Two Storage Options (Option #1 Battery and Option #2 Ice Storage) (After Madiseh et al.)99 As seen in Figure 30, two options are considered for storage. Certainly, each storage option is required to handle a given period worth of storage for safety. This kind of approach is needed as the cooling operation cannot simply rely on energy from the solar panels directly without any buffering due to intermittence potential of the solar irradiation in the area. To mitigate these issues, both storage systems are designed to store at least ‘one-week’ worth of energy to maintain the operation. In other words, in the case of no solar irradiation for a week system would still be able to provide the necessary cooling to top up the cooling plant. Also, a storage rate was also considered during the financial analysis. This parameter simply refers to strength of the energy generation and size of the panel installments. Longer storage rate means less panel installation, however, slower energy generation and slower return. One disadvantage of the battery storage system (Option #1) is that it stores electricity, and this still needs to be converted into coolth at the plant. This means plant cannot be downsized in terms of capacity and its capital size. Therefore, battery option does not contribute plant downsizing but only saves from downsizing of the operational expenses by outsourcing the energy to solar irradiation. However, direct ice generation (Option #2) would also help diminishing the CAPEX of the plant, as it can directly facilitate cooling without any energy conversion. This means ice generation from solar panels will contribute to both operating expenses and capital size of the original plant design. The relationship between plant downsizing and ice generation to compensate is studied on Figure 31 below. Also, note that the plant is remotely located and uses diesel generators to generate the power needed on mine site. This means any savings from the operating expenses will result in carbon savings as well. This is also factored in during the financial studies. 100 Figure 31 - Cap Lines and Corresponding Plant Size and Ice Generation Requirement (After Madiseh et al.) To assess the solar irradiation potential in the area, again Natural Resources Canada database [95] was referred and following local irradiation data was collected. Table 19 - Local Solar Irradiation Data Collected from NRCAN Irradiation Yield (kWh/kWp) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 64 85 124 127 132 131 136 122 90 73 47 49 To generate a reliable cost model different sources that studied similar sized storage systems are studied. The price estimations for both battery storage and ice storage systems are tabulated in Table 20 below and the sources that the data was collected is cited on the table for further reading on the available information. Note that a small battery investment would be required for both systems for surge protection and reduce the noise from the solar panels, this is considered as default for both options. Remark Figure 30 for flow diagram. 020040060080010001200140016000246810121416182014.5 15 15.5 16 16.5 17 17.5 18 18.5 Energy Compensated by Ice Storage System for Peak Season (MWh)Cooling Plant Max Capacity (MW)Cooling Plant Inlet WB Temperature (C°)Plant Size Monthly Duty on Ice Storage101 Table 20 - Parameters Used in the Developed Cost Model (After Madiseh et al.) Unit Cost Source Battery Storage System [96] Module Cost $ 0.74/W System Eqp. Installation Overhead Battery Cost $ 0.22/W $ 0.15/W $ 0.75/W $ 311/kWh Ice Storage System [97] Installed Cost (All Incl.) $ 32/kWh Panel System [98] Installed Cost (All Incl.) $ 2,700/kW Following table (Table 21) shows the results obtained from the cost model. Cost model was developed in MATLAB to estimate the project savings by using the cost figures listed in Table 20. Results have shown that Opt. #1 CAPEX was significantly higher than the Opt. #2 CAPEX and yielded infeasible return periods (15 years and more for each capping option). Therefore, not shown on Table 21. Also, remark that Opt. #1 has no OPEX savings, as it would not contribute to plant downsizing. As Table 21 explains, any option with payback period less than 9 years is considered as fairly feasible. This means any additional cost to install the corresponding storage system will be paid back within 9 years and for the rest of the mine life (e.g., 11 years) it will return profit. All the options are highlighted on the table. This study was done as a ‘proof of concept level’ and has not been experimentally validated. However, the results have proven that ice storage with solar panel coupling could result in significant savings after its payback period. More on techno-economics of ice storage versus battery storage can also be found in the following literature: Pokhrel, S., Kuyuk, A.F., Kalantari, H. and Ghoreishi-Madiseh, S.A. (2020). Techno-Economic Trade-Off between Battery Storage and Ice Thermal Energy Storage for Application in Renewable Mine Cooling System.102 Table 21 - Financial Matrix Showing Each Cap Line and Resultant CAPEX Investment and Payback Period (After Madiseh et al.) -End of this Chapter- Storage Size: 1 Week for each cap-line | Storage Rate: A Day Cap Line (C°) PV Needed (MW) Capital Cost Option #1 (k$) Capital Cost Option #2 (k$) OPEX Savings (k$/yr) Plant Cap. Sav. (k$) Payback (Years) 17 7.7 50,514 25,323 2,615 3,183 8.5 16.5 11.2 73,339 36,765 3,796 4,162 8.6 16 14.9 97,559 48,907 5,050 5,141 8.7 15.5 19.4 127,342 63,837 6,591 6,059 8.8 15 25.6 168,119 84,279 8,702 6,978 8.9 14.5 34.4 225,786 113,188 11,687 7,957 9.0 14 47.5 311,584 156,199 16,128 8,814 9.1 Storage Size: 1 Week for each cap-line | Storage Rate: 2 Days 17 3.8 30,314 14,222 1,307 3,183 8.4 16.5 5.6 44,011 20,649 1,898 4,162 8.7 16 7.4 58,545 27,468 2,525 5,141 8.8 15.5 9.7 76,419 35,854 3,296 6,059 9.0 15 12.8 100,889 47,334 4,351 6,978 9.3 14.5 17.2 135,496 63,571 5,843 7,957 9.5 14 23.7 186,983 87,727 8,064 8,814 9.8 Storage Size: 1 Week for each cap-line | Storage Rate: A Week 17 1.1 15,885 6,293 374 3,183 8.3 16.5 1.6 23,063 9,137 542 4,162 9.2 16 2.1 30,679 12,155 721 5,141 9.7 15.5 2.8 40,045 15,865 942 6,059 10.4 15 3.7 52,868 20,946 1,243 6,978 11.2 14.5 4.9 71,002 28,130 1,670 7,957 12.1 14 6.8 97,983 38,819 2,304 8,814 13.0 103 CHAPTER 4 – Numerical Methods in Designing BACs in Full-Scale and Lab-Scale, Experiments and Validations 4. Lab-Scale and Full-scale Experimental and Numerical Studies In the scope of this thesis work, this chapter is dedicated to lab-scale and full-scale studies investigating spray cooling and their numerical modeling. 4.1. Introduction to Experiments and the Lab-Scale Numerical Model Spray cooling system performance measurements are anticipated to be conducted over single spray interaction with continuous air flow in a controlled environment (i.e., wind tunnel system). A similar arrangement was previously studied by Sureshkumar et al. and it was published earlier in 2008 [99]. In contrast, experimental studies presented here has not only gone through many design changes, newer arrangements etc. but also designed to cover a different injection temperature spectrum than that was studied in earlier work. As seen in Figure 32, in a conventional spray cooling system, an air stream provided via a blower fan is introduced into a cavity (chamber) section in a continuous and controlled fashion also referred as ‘continuous phase’, whereas several nozzle arrangements are placed in the test cavity to introduce water sprays onto the air stream. Lab design was aimed to mimic similar interaction as seen on Figure 32 to further investigate water – air physics in the way of understanding mine bulk air spray cooling applications. The developed design offers some capability to: 104 - monitor droplet behavior with closer perspective record, study, arrange etc. - allow introduction of fully developed cones in the cavity representing single and multiple injections - easier to model, calibrate and change with respect to the needs of the tests (i.e., changing nozzle tips, arranging shorter/longer distances for optimizing the water-air interaction, installing sensor at various locations to make reliable measurements.) - monitor and record outlet temperature and relative humidity response closely - enhance issues regarding the granularity problem that was laid out earlier in Chapter 2 and Chapter 3. Figure 32 - A representation of conventional bulk-air-spray cooling (Modified after McPherson) Finally, the experimental setup shown in the following images (Figure 33, Figure 34, Figure 35) is constructed with DELPRO Automation and installed to the University of British Columbia, NBK Institute of Mining Engineering CMP Lab Room: 155. 105 Figure 33 - 3-D Sketch of the Lab Experimental Setup Developed with (DELPRO Automation Inc.) 106 Figure 34 - Lab Experimental Setup Dimensions (1) 107 Figure 35 - Lab Experimental Setup Dimensions (2) 108 4.2. Components of the Experimental Setup The experimental unit is equipped with 6 main parts as listed below: - Flow generating unit (Blower Fan and Heater) - Data management system - Flow conditioning unit - Wind tunnel sections x 2 (Equipped with nozzles facing different directions) - Drift eliminators and outlet sensors. a- Flow generating unit (Blower and Heater): This unit is designed to generate different climatic conditions for the designed test. The unit is equipped with a rectangular intake that carries a 14kW capacity PID (Proportional Integral Derivative) controlled heater and a centrifugal fan. The fan is a VFD (Variable Frequency Driver) controllable fan and user can input the fan speed via the HMI (Human-Machine Interface) screen. b- Data management system: This unit is composed of the electrical circuit board, PCI connections and the HMI screen. This system collects information from 10 thermocouples installed in the wind-tunnel system (i.e., 1 sensor at the inlet and 9 at the outlet), regulates the flow meter sensors, stores data in .csv format and has an installed screen to enhance the human-machine interaction. From this screen, water injection, airflow parameters and the heater settings can be arranged and modified before or during the experiment. 109 c- Flow conditioning unit: This unit simply regulates the flow entering the wind-tunnel arrangement downstream of the flow generating unit. It is installed with ‘honeycomb’ shaped sieves to regulate the flow to prevent occurrences of unwanted eddies in the win the wind-tunnel during the test. d- Wind tunnel sections: As seen on Figure 35, this part of the system is composed of two sections attached together with bolts and gaskets in between the connections. These sections are equipped with nozzles facing different spray directions and an internal illumination system. Additionally, to facilitate visual apprehension of tests and access to the test tunnel, these sections are installed with removable windows. e- Drift eliminators and outlet sensors: This part has two layers of ‘honeycomb’ shaped drift eliminators to prevent water escaping from the system and installed with 9 nozzles arranged to pick-up temperature and relative humidity data from 9 locations at the outlet. See following figures (Figure 36, Figure 37, Figure 38) to get a better understanding of the installed system at the UBC, CMP Lab Room: 155. You may notice slight differences between the technical sketches and the actual installation. Please note that the design of the system has undergone several changes to be compatible to the space available at the lab. Nevertheless, all the arrangements mentioned here, and the functions of the equipment/components are not changed and applicable to the final design. 110 Figure 36 - Lab Experimental Setup (Actual) Figure 37 - Human-Machine Interface of the Lab Equipment 111 Figure 38 - Outlet Thermocouple Locations (T2-T10) 4.3. Rationale of the Experiments and the Difference from Other Lab Studies In their study, Sureshkumar et al. [99], [100] have mostly targeted evaporative cooling performance of a water spray cooling system. Their work covered warm water (~35 °C) injections over hot-dry air (~40 °C @ 10-15% RH) and over hot-humid air (~40 °C @ 40-45 % RH). Although, their experimental approach is relatable to the concept of mine bulk air cooling, their water injection temperatures are relatively higher than what is considered as industry standard for mining applications. To contrast, in mine cooling, water injection temperatures are usually in the range of 5-10 °C and it is highly assumed that; at this temperature due to lower enthalpy of the water, evaporation of the water is very minimal and the heat exchange between water and air is 112 mostly governed by sensible cooling. Evaporation for such water injection temperatures is argued to be approximating around 1% for such cold injections in mine cooling applications [39]. To test this argument and validate the lab-scale CFD model in the way of upscaling, the experiments are designed in a way that they resemble typical mine cooling applications with cold water injections. As mentioned, the lab-scale model is then validated using these tests and finally upscaled to represent industrial bulk-air cooling applications. Once validated in the lab-scale and upscaled to full-scale size, the numerical model developed and presented here is expected to open a new way of understanding chilled water cooling of mines. 4.4. Test Preparation Testing unit has arrived to the UBC lab in March 2022 and fixed to be fully operational in April 2022. Starting from April 2022, several tests are conducted during the spring and early summertime in Vancouver BC. During this timeline lab initial temperatures has oscillated between 15ᵒ C to 22ᵒ C depending on the outside temperature in Vancouver, BC area. Temperature control of the testing unit is managed with a 14 kW, PID controlled external heating unit and the lab temperature is upgraded to ~28-30ᵒ C levels before each test. Inlet temperatures are run until a steady temperature is reached inside the wind-tunnel and the airflow velocities are pre-set in accordance with test requirements of that day. Before each experiment, water tank levels are set, and temperatures are regulated with ice cubes in the water tanks. Water temperatures are set between 4-9ᵒ C before each test to represent mine cooling applications. Each test required 20-30 kg of ice pouring to maintain a stable injection temperature. Ice amount used each test has changed based on the availability of ice and tank water conditions. Water 113 conditioning and air conditioning are done before each test and once reached to a stable temperature, tests were initiated along with data logging process. Airflow and water injection rates are changed randomly targeting between 0.1 – 0.3 water mass to air ratio. This ratio could go up to 0.8 in industrial applications, however, such injection either were too much for the built-in injection capacity of the lab equipment or resulted in very low airflow velocities which deemed as to be not representative enough in description droplet-air interaction. Figure 39 - Test Preparation (a) Nozzle, (b) Spray, (c) Water Chilling with Ice, (d) Ice Pouring 114 4.5. Instrumentation and Experimentation 4.5.1. Air Provision to the Unit and Temperature Control The cooling tests are conducted in a close wind-tunnel system as explained earlier. The air is provided to the system via a blower fan managed through the HMI system introduced in Figure 37. The velocity of the flow was controlled with an air-flow meter attached to the wind-tunnel system feeding information back to control unit to maintain proper mass flow to the tunnel during the test. The flow is matched with the RPM of the fan through the control unit and both data is recorded. On the other hand, the temperature control of the system is done with the heating element attached to the “flow generating unit” introduced earlier. Before each experiment, the heating unit was set to a constant temperature value via HMI at a given air velocity to maintain the proper thermal inertia on the heating element for the given flow settings. 4.5.2. Injections and Nozzle Arrangements The injection types are classified as type A (i.e., parallel injection) and type B (i.e., counter injection). Each injection setting was operated with a full-cone, BETE 3/8 WL4 4.78 mm nozzles. Some more detailed information regarding the nozzle used in the experiments can be found in Table 22*. *(Info taken from: https://bete.com/product/wl/) 115 Table 22 - Technical Details of the Nozzle Used in the Experiments Identifier Value Nozzle Name WL 4 K Factor 9.40 Flow Rate (l/min) K*(bar)0.47 Orifice Dia. (mm) 4.78 Pipe Size (inch) 3/8 Type Full cone Spray Angle 60° Injection masses are various (0.1 l/s, 0.15 l/s and 0.3 l/s) and selected based on the nature of the experiment to maintain air/water ratio of 10-30%. This air/water ratio could be anticipated out of range in comparison to the industry standards in matching the necessary cooling, however, higher injection rates at the machine caused technical issues that needs to be addressed by the fabricators and this maintenance is considered beyond the timeline dedicated for this thesis. These setbacks are usually associated with the capacity of the testing unit (i.e., return pump capacity, drift elimination issues at higher air / water settings, wetting of flow meter sensor, wetting of thermocouples etc.). To prevent tests clear from these technical issues and to maintain safe data management, tests are designed to have injections of 0.10 kg/s to 0.30 kg/s across the 0.25 m2 chamber cross section. This corresponds to 0.40 kg/s to 1.2 kg/s per 1 m2 of the cooling space yielding lower than the 2 kg/s limit discussed in McPherson [39]. Regardless from technical issues, the validation of the lab-scale model was made based on undisturbed, clear sensor readings. On the other hand, injection temperatures are expected to represent the industry standards of 5-10 °C. Therefore, injection waters are preconditioned in plastic storage tanks before each experiment. For water chilling, ice cubes are used, and water temperature is close circuit 116 monitored through the HMI system. The water injection is not started without the water in tank reached a steady temperature. Water temperature during the injection is recorded and used for simulations (See Figure 39). Injections are made with full-cone pattern at 60° injection angle (See Figure 39). Droplet size assessment based on different injection settings was a significant challenge due to lack of technology at the lab to measure drop sizes. This assessment requires high quality image capturing with laser enhancement. This analysis was not possible with the current lab conditions and is planned for future studies. For the present study Sureshkumar’s [100], [101] experimental data and approaches used by Montazeri [41] is considered and approximated for the numerical modelling of the lab tests. It is worthwhile to mention here that changing mass flow at the nozzle tip expectedly dictates different pressure reading at the inlet line pressure. These pressure settings are used for categorization of the droplet size distribution and referred as “low and high” pressure settings as explained in table below. Table 23 - Mass Flow and Pressure Classification Mass Flow Rate (l/s) Inline Pressure and Class (PSI) 0.10 10 (Low Press) 0.15 24 – 18 (Low Press) 0.30 86-100 (High Press) Accordingly, each injection setting would be expected to have their own size distribution due to different surface tension response of the water at different pressures at the tip of the nozzle. Droplet mean size change in accordance with the changing pressure is hard to predict, however, 117 for the same nozzle tip a fair estimation of the size correction factor is discussed by a commercial nozzle producer [102] as follows: 𝑑1𝑑2= (𝑃1𝑃2)−0.3 4.6. Developing the Numerical Model: Multiphase Modeling Approach To simulate the discussed spray cooling scenario, a multiphase model is constructed in ANSYS Fluent. This was done by coupling a species transport model with DPM injection (for setting up injection settings and the air-vapor mixture as the primary phase of the domain) to a Eulerian solution (to let the primary phase to exchange mass with another continuum field – i.e., water through a mass and heat transfer mechanism). 4.6.1. Mass Transfer Mechanisms and the Continuity of the Model The continuity of the solution is maintained via coupling of two heat/mass transfer mechanisms. These mechanisms allowed heat and mass transfer between two phases, also named as: primary, and secondary phases. Here, primary phase is set as a mixture representing that air and vapor are in gaseous state and present together as a mixture. By doing so, vapor mass fraction can be specified at the inlet boundary condition during the solution. On the other hand, a secondary phase is needed to represent when the vapor specie in the air-vapor mixture needs to condense when it attains its saturation point at a given temperature. For this purpose, a secondary phase with material liquid water is defined under the mixture multiphase model. Mixture multiphase model is a continuum model using the Eulerian reference frame to attain continuity of the flow. The most significant 118 difference between mixture model and other Eulerian models (i.e., VoF and Eulerian etc.) is that mixture model solves one equation for the continuity and treats the flow field as one bulk mixture, whereas; other models solve separate continuity equations for each of the fluids in the domain. Accordingly, selecting mixture model for the solution is considered logical here, hence the condensation in the domain happens in the form of mist and dragged as part of the continuous field. This explains why other Eulerian models would only add sophistication to the solution under the current scope. The formed liquid behavior and the coalition of mist is sophisticated and was not elaborated in this research work. With current settings the goal was to allow a mass and heat transfer within the solution domain to maintain the thermodynamics of the solution correctly. Following image should summarize the coupling within the model: Figure 40 - Model Coupling in Fluent 119 4.6.2. Boundary Conditions For inlet conditions, ‘mass flow inlet’ boundary condition is used for easier management of the inlet mass flow. Pressure inlet and velocity inlet boundary conditions were also used in earlier tries but it was found that that the model could get unstable during phase change (if any). Inlet thermal conditions are dictated with static air temperature and the vapor content of the inlet air. To manage this boundary condition during the simulation, logged and averaged inlet thermocouple readings are used. Note that, these data are gathered at the inlet temperature sensor of the testing unit and monitored during the experiment. Lastly, inlet turbulence settings are adapted from Montazeri et al. [41] and it is set as intensity and length scale mode with 10% intensity and 0.042 m length scale. On the outlet, pressure outlet boundary condition was selected. Although this created local reverse flow issues at the end of the domain, it yielded lower and more maintainable scaled residuals during the solution. The reverse flow issues are mitigated through extension of the flow field domain beyond the real length of the system for clear reading of the results at the proper sensor location. On the walls, no flux, no slip boundary conditions are used for the surrounding walls of the system. Note that, wall boundary conditions are important in managing the droplet behavior when reached to walls. For this setting, for left, right and top wall boundaries reflect boundary condition is used with normal and tangential coefficients of 0.1 to represent the droplet response at the interior walls of the experimental system. For bottom, discrete particles (droplets) are set either to escape or reflect with very low normal but high tangential coefficients to simply slide across the floor of the unit and escape from the outlet. A need for this modification has emerged 120 due to different ‘bottom’ boundary behavior between Type A (i.e., parallel injection) and Type B (i.e., counterflow injection). As one can predict, in parallel fashion, injected droplets escape from the domain within relatively less time than counterflow injections, as they are in line with the flow and with the added effect of drag, thermal impact of lower droplets at the bottom is relatively minimal due to short exposure times. In contrast, for counterflow injections, these lower droplets experience nearly two times longer exposure at the bottom of the testing unit as they first travel to the inlet of the unit and then escape back from the outlet. For these conditions, reflect with high tangential and low normal reflection coefficients is used to keep these droplets at the lower part of the domain slightly longer to resemble reality. 4.6.3. Phases in the Numerical Domain As one can say, simulating evaporation/condensation phenomenon with water injection requires a multiphase solution. This is managed with coupling of two mass transfer models through the primary phase of the fluid domain (i.e., air and vapor phase as a mixture). To make this happen, first, a mixture of air and vapor is introduced via a specie transport model. This model allows defining two separate mass entities in the same bin for mixture simulations. Additionally, defining vapor as a specie under species transport model allows ‘droplet’ particle setting under DPM settings. This means DPM injection can now be in water droplets as well. Although now, one would have a sufficient model for evaporative cooling cases (this is also what was studied by Montazeri et al. earlier[41]) the model is currently lacking a mass transfer coupling to solve cases with problems that could have condensation phenomenon. Condensation coupling is required to move the vapor mass to liquid water phase when the saturation of the cell is triggered. This coupling is made through the ‘Multiphase’ option of the Fluent. For this coupling, User-Defined 121 function was developed to trigger the mass transfer mechanism for each cell when reached to the saturation temperature. As this attained, corresponding vapor mass is now moved to a new bin named as ‘liquid water’ representing the condensed water in the domain and is different than what is injected through the injection point. 4.6.4. Governing Equations for the Mixture Model Air-vapor mixture and condensed water (which is allowed to form as part of the mixture multiphase model) are the two continuous streams in the domain. Then for this mixture one can write the following continuity equation as stated by the ‘ANSYS User Manual’. Let us say, ?⃑?𝑚 stands for the mass weighted average velocity of the mixture, 𝜌𝑚 is the bulk density of the mixture and 𝛼𝑘 stands for the volume fraction of the corresponding phase k, then the continuity equation for this mixture would be [103]: 𝜕𝜕𝑡(𝜌𝑚) + ∇ ∙ (𝜌𝑚?⃗?𝑚) = 0 Where: ?⃑?𝑚 =∑ 𝛼𝑘𝜌𝑘?⃗?𝑘𝑛𝑘=1𝜌𝑚 and, 𝜌𝑚 = ∑ 𝛼𝑘𝜌𝑘𝑛𝑘=1 122 To solve conservation of momentum, say n stands for the number of phases in the mixture domain, ?⃗? represents the body force and 𝜇𝑚 stands for the bulk viscosity of the mixture, then the conservation of momentum would yield [103]: 𝜕𝜕𝑡(𝜌𝑚?⃗?𝑚) + ∇ ∙ (𝜌𝑚?⃗?𝑚?⃗?𝑚) = −∇p + ∇ ∙ [𝜇𝑚(∇?⃗?𝑚 + ∇?⃗?𝑚𝑇)] + 𝜌𝑚?⃗? + ?⃗? − ∇ ∙ (∑ 𝛼𝑘𝜌𝑘𝑛𝑘=1?⃗?𝑑𝑟,𝑘?⃗?𝑑𝑟,𝑘) Here bulk viscosity would be considered as the sum of all viscosities in the mixture system in accordance with their volume fraction, 𝛼𝑘. So, the following expression can be considered for the bulk viscosity of the mixture [103]: 𝜇𝑚 = ∑ 𝛼𝑘𝑛𝑘=1𝜇𝑘 At this point, it is important to note that in the above equation ?⃗?𝑑𝑟,𝑘 represents the drift velocity of the formed water mist due to condensation. This formation is governed with the formation characteristics set in the multiphase solution dialogue box. For momentum solution, ?⃗?𝑑𝑟,𝑘 can be considered as [103]: ?⃗?𝑑𝑟,𝑘 = ?⃗?𝑘 − ?⃗?𝑚 Lastly, for solving the energy conservation of the mixture domain, let us say ℎ𝑗,𝑘 is the enthalpy of specie 𝑗 present in phase 𝑘 and 𝐽𝑗,𝑘 represents the diffusive flux in between phases then the energy equation would be [103]: 𝜕𝜕𝑡∑(𝛼𝑘𝜌𝑘𝐸𝑘) + ∇ ∙ ∑(𝛼𝑘?⃗⃗?𝑘(𝜌𝑘𝐸𝑘 + 𝑝)) = ∇ ∙ (𝑘𝑒𝑓𝑓∇T − ∑ ∑ ℎ𝑗,𝑘?⃗?𝑗,𝑘 + (?̿?𝑒𝑓𝑓 − ?⃗⃗?)𝑗𝑘) + 𝑆ℎ𝑘𝑘 123 Where the effective conductivity can be written as: 𝑘𝑒𝑓𝑓 = ∑ 𝛼𝑘(𝑘𝑘 + 𝑘𝑡) Note that, 𝑘𝑡 here represents the thermal conductivity added by the turbulence and this factor is selected in accordance with the picked turbulence model, in this case it is selected as k-ε model. More detailed analysis of different turbulence models on a similar model was done by Montazeri et al. [34], [41], [104] and it was found that the other turbulence models are yielding similar results with negligible impact. 4.6.5. Mass Transfer Criterion and Lee’s Model As it is shown and discussed so far to model the multiphase behavior inside the chamber mixture model is used and governing equations are shared. If one could refer to the continuity equation laid out in previous section, the air-vapor is introduced to the solution domain as a mixture and each specie in this mixture (i.e., vapor and air) has a mass fraction dictated by the boundary conditions. Note that, at this point, when the cooling by droplets is introduced to the solution and the above-explained cooling mechanism is triggered, solution requires a criterion to transfer the mass from H2Ovapor bin to H2Oliquid bin for proper management of heat and mass transfer. To manage this, Fluent offers entry of desired criterion in response to trigger the selected mass transfer mechanism. In this case this mass transfer mechanism is selected as Lee’s thermal phase change model and the saturation temperature of the cell is calculated by using the Antoine’s equation. Some details regarding Antoine’s Equation can be found here [105]. The equation and the developed UDF code to find the saturation temperature can be seen as follows. 124 𝐴𝑛𝑡𝑜𝑖𝑛𝑒′𝑠 𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 log10(𝑃) = 𝐴 − (𝐵(𝑇 + 𝐶)) Where, A, B and C are factors and T is the saturation temperature. See below for UDF function developed to acquire saturation temperature of the cell for air-vapor phase during multiphase solution. UDF CODE for MASS TRANSFER: #include "udf.h" #define h2o_mol_w 18 // gr water 1 mole weight #define air_mol_w 29 // gr air 1 mole weight #define phase_index 0 // phase index for primary phase #define zone_id 2 // primary phase subdomain id #define domain_id 1 // mixture level domain id DEFINE_PROPERTY(saturtemp,c,mix_thread) { Thread *pp_thread = THREAD_SUB_THREAD(mix_thread, phase_index); int n = 0; real sat_t; real mix_p; real static_p = C_P(c, pp_thread); real operating_p = RP_Get_Real("operating-pressure"); real vapor_mass_frac = C_YI(c, pp_thread, n); // vapor mass fraction real vapor_partial_p; real cell_d = C_R(c, pp_thread); real cell_v = C_VOLUME(c, pp_thread); real cell_m = cell_d*cell_v; // cell mass real vapor_m = cell_m*vapor_mass_frac; // vapor mass in the cell real vapor_mole = vapor_m/h2o_mol_w; // vapor mole in the cell real air_mole = (cell_m-vapor_m)/air_mol_w; // air mole in the cell real N = (vapor_mole)/(vapor_mole+air_mole); // vapor molar fraction 125 mix_p = (static_p + operating_p); // absolute pressure = static pressure + operating pressure vapor_partial_p = (mix_p)*(N); // partial pressure for vapor real P_in_mmhg = vapor_partial_p/133.322; sat_t = ((1730.63/(8.071-(double)log10(P_in_mmhg)))-233.08)+273.15; // Antoine's Law Message("Calculated Saturation Temperature is: %g\n", sat_t); return sat_t; } As seen in the code above, first the mixture thread is addressed, and the phase index is indicated as the primary phase of air-vapor mixture. Note that, here the liquified water is the secondary phase and expected to form as dictated by the saturation temperature acquired by the code above. Once the primary phase is addressed, some cell information is gathered (i.e., cell density, cell mass, cell volume and total pressure). Then, the mass and mole fraction of vapor in the cell is calculated to find out the partial pressure of the vapor at the given cell. On the other hand, by using the Antoine’s equation shown above, the saturation temperature of the vapor at that given cell pressure is calculated and returned as the code output. Once the saturation temperature for the cell is defined, Lee’s mass transfer model is triggered, and the condensation mass is determined based on the frequency indicated in the model settings. 4.6.6. Droplet and Specie Settings Droplet settings play an important role in managing proper spray characteristics. For such simulations, Fluent offers water droplet as a particle selection. This property can be set for droplet injection when the species of the water is introduced as part of the species transport equation. Droplet evaporation is managed with species transport coupling of the water to the primary phase as explained earlier. Therefore, any droplet reaching the vaporization temperature 126 will report to air-vapor mixture as a vapor specie and kept in the continuity of the solution. Injection points are located at the center of the Y-Z axis and for X it is located at either at 0.25m facing outlet (i.e., parallel flow or Type A injection) or at 2.25m facing inlet (i.e., counter flow or Type B injection). See image below for injection and the domain positioning for Type A injection. Figure 41 - Injection Positioning and the Domain for Type A Injections Droplet distribution is made according to Rosin-Rammler distribution as offered by the injection settings of Fluent. ‘Spread factor’ of the distribution is set as 3.67 in accordance with the earlier studies put forward by Montazeri et al. [41]. However, in the counterflow injection settings, in some cases, droplet distribution was visually observed to be relatively more irregular than the parallel injection due to crossflow impact of the air flow and droplet breakups. Although minimal, this impact is managed by tuning down the spread factor to 2.1 to create more spread sample distribution. As known, the spread factor represents the spreading of the tails of a distribution and as this number grows, the sample would be more populated closer to the mean size, 127 conversely; decreasing spread factor will result in more evenly distributed population between min and max of the sample. See Figure 42 for droplet size settings used to simulate cases explained in Table 23. Figure 42 - Droplet Size Distributions in Accordance with Injection Settings (a) Low Pressure (b) High Pressure 3% 4%5%6%8%10%12%14%17%20%0.273 0.299 0.326 0.352 0.379 0.405 0.432 0.458 0.484 0.511millimetersLow Pressure Drop Size DistributionDroplet Size Distribution3%4%5%6%8%10%12%14%17%20%0.173 0.190 0.206 0.223 0.239 0.256 0.272 0.289 0.305 0.322millimetersHigh Pressure Drop Size DistributionDroplet Size Distributionb a 128 The mass coupling between the discrete phase and mixture is attained via species transport coupling as explained earlier in Figure 40. This coupling allows DPM specie (i.e., H2O (droplet)) to evaporate (i.e., H2O (vapor)) when the Vaporization (Law 2) law is attained by the droplet. Say, 𝑌𝑖 represents the local mass fraction of each species, then by using the convection-diffusion equation for the 𝑖𝑡ℎ species following conservation equation can be derived [103]: 𝜕𝜕𝑡(𝜌𝑌𝑖) + ∇ ∙ (𝜌?⃗?𝑌𝑖) = −∇ ∙ 𝐽𝑖 + 𝑅𝑖 + 𝑆𝑖 Where, 𝑅𝑖 is the net production of species 𝑖 (i.e., vapor through vaporization of droplets) by the reaction defined in Law 2 (DPM Vaporization See: [103]). Also, note that 𝐽𝑖 stands for the mass diffusion in turbulent flows and 𝑆𝑖 is the source term defined by the user [103]. Note that for coupling of DPM phase with continuous phase, Fluent uses following procedure [103]: Accordingly, the heat transfer coupling between the droplet and the continuous phase can be shown as explained in [103]: Continuous Phase Flow Field Calculations Particle Trajectory Calculations Update Cont. Phase Source Terms 129 𝑚𝑝𝐶𝑝𝑑𝑇𝑝𝑑𝑡= ℎ𝐴𝑝(𝑇∞ − 𝑇𝑝) −𝑑𝑚𝑝𝑑𝑡ℎ𝑓𝑔 + 𝐴𝑝𝜀𝑝𝜎(𝜃𝑅4 − 𝑇𝑝4) Here, 𝑚𝑝 represents the mass of the droplet and 𝐶𝑝 represents the droplet heat capacity. Also, as for other terms, 𝐴𝑝 stands for the surface area of the droplet, 𝑇𝑝 stands for temperature of the droplet, ℎ for convective heat transfer and 𝑇∞ for the temperature of the continuous phase. Lastly, 𝑑𝑚𝑝𝑑𝑡 shows the mass change of the droplet and ℎ𝑓𝑔 represents the latent heat. The last term on the right-hand side of the equation represents the radiation heat transfer, however, for this case it is not used and can be dropped off from the heat balance. Note that; here the droplet temperature is coupled to the continuous phase and gets updated to a heat balance that relates the sensible heat change (as seen on the left hand side of the equation) to convective and latent heat transfer between droplet and air-vapor mixture (as seen on the right hand side of the equation) [103]. Consequently, any heat transfer from (or to the) continuous phase at this point acts as a sink or source through the 𝑆ℎ term that was shown on the energy equation of the mixture phase [103] through the coupling of two models [103]. 4.7. Experiments Between April 14, 2022, and June 22, 2022, 18 horizontal cooling experiments were conducted. During these experiments approximately 500 kg of ice is generated and used for water temperature regulation. For ice generation an industrial 20 lbs. capacity ice-cube generation machine is used. 130 4.7.1. Procedure Water conditioning: Collected ice was collected into plastic bags and stored in deep freezer installed at the lab. This storage procedure was carried on throughout the week until enough ice is stored to make sure the close temperature control during the conditioning of the water. Following image shows how the ice is stored for the experiments. Figure 43 - Ice Storage for the Experimental Process Once ice was poured into the water feeder tank, the water in the tank was let for conditioning for a few minutes (Remark Figure 39). During this time, water tanks are stirred with a metal stick to induce mixing in the tank for faster results. As the water in the tank gets conditioned with ice, the temperature monitoring was carried out through the inlet temperature sensor. These readings are controlled through the HMI unit until a stable temperature is reached in the feeder tank. Air preconditioning: For this process, the heating unit is used to upgrade the temperature of the intake air when needed. This process is controlled via the HMI unit and can be dictated in degree 131 Celsius through the control panel. The heating unit is a PID controlled element, and it feedbacks with the inlet temperature sensor (T1). This monitoring was carried out via the HMI screen for a given air velocity setting to achieve desired experimental conditions. To prevent temperature oscillations, the heating unit was operated along with the flowing air until the temperature reading at the T1 sensor gives a stable reading for the selected air velocity. This process is essential to make sure the heating unit to settle down to a pulsation rate that would not harm the experimental process. Spraying: Spraying was carried out when the temperature reading at the inlet water sensor is stabilized on the HMI screen. Spraying was applied at various injection rates as discussed earlier. Initial spraying moment was considered as the time ‘zero’ indicating that the experiment had started. Therefore, along with the injection procedure, data storing was also commanded via the HMI control panel. Data storage and sensors: Data storage was made through the control panel to a standard USB stick. For every second, a data line is stored to a text file and transferred to the USB stick at the end of each experiment. Each line would include: - Inlet Air Temperature and RH - Outlet Air Temperature and RH readings (from 9 outlet sensors) (Remark Figure 38) - Inlet Water temperature and flow readings - Outlet Water temperature and flow readings - Airflow and Fan RPM readings Please see the following figure for locations of the sensor locations on the experimental unit:132 Figure 44 – Sensor Locations and Parts of the Experimental Unit Type A Type B USB HMI Fan T1 T2-10 Chamber Water Tanks Air Flowmeter Water Pumps and Flowmeters Sump Water return 133 4.7.2. Data Following Table 24 shows a summary of the collected data for the 18 successful experiments recorded in between the dates indicated. Table 24 – Experimental Data ALL EXPERIMENTAL INLET OUTLET DATE ID NOZZLE WATER ᵒC AIR ᵒC RH % WATER IN (l/s) AIR IN (kg/s) ENT. (kJ/kg) WATER ᵒC AIR ᵒC RH % ENT. (kJ/kg) 14-Apr-22 RUN#1 A 9.9 28.0 29% 0.30 1.0 45.6 13.3 16.6 82% 41.2 20-Apr-22 RUN#2 A 8.5 28.9 33% 0.30 1.0 50.0 12.7 16.7 85% 42.3 21-Apr-22 RUN#3 A 5.6 28.3 29% 0.30 1.0 46.2 10.8 14.7 85% 36.8 22-Apr-22 RUN#4 A 5.5 27.9 32% 0.15 1.0 47.2 11.6 23.0 44% 42.7 26-Apr-22 RUN#5 A 7.2 27.8 35% 0.30 1.0 48.8 11.8 16.3 90% 42.8 5-May-22 RUN#6 A 6.5 29.1 30% 0.15 1.0 48.5 12.0 23.0 52% 46.3 6-May-22 RUN#7 B 5.9 27.8 36% 0.30 1.0 49.3 12.0 14.9 84% 37.4 10-May-22 RUN#8 B 8.0 28.5 34% 0.15 1.2 49.7 14.0 22.7 58% 48.2 12-May-22 RUN#9 B 7.3 28.2 35% 0.15 1.5 50.0 13.6 24.2 53% 48.2 17-May-22 RUN#10 A 6.0 31.0 30% 0.15 0.8 52.7 11.0 23.0 53% 49.7 25-May-22 RUN#11 A 6.5 32.0 31% 0.15 0.6 55.7 12.0 22.7 62% 50.0 25-May-22 RUN#12 A 4.9 29.0 40% 0.15 1.2 54.7 12.0 23.4 61% 51.5 1-Jun-22 RUN#13 A 9.8 34.0 30% 0.15 0.9 62.3 15.3 26.7 59% 60.0 7-Jun-22 RUN#14 B 6.2 32.0 34% 0.10 1.2 58.0 14.5 26.1 54% 55.4 10-Jun-22 RUN#15 A 3.8 27.1 43% 0.15 0.9 51.5 10.5 21.4 64% 47.4 15-Jun-22 RUN#16 A 5.8 24.0 46% 0.15 1.2 45.9 11.3 19.9 64% 43.5 15-Jun-22 RUN#17 B 6.4 34.7 24% 0.10 1.2 56.1 14.5 28.0 43% 54.1 22-Jun-22 RUN#18 A 4.9 28.2 40% 0.15 1.2 52.7 13.0 23.5 58% 50.3 Note that: The experiments are named with RUN prefix to the test number and will be referred as such. As seen in Table 24 lab air temperature is preconditioned to a value between 27-34 °C with the heating unit. On the other hand, inlet relative humidity values for the experiments were oscillated around 25-40 %. As introduced earlier (and shown on Figure 44 above), two types of injection settings were used to conduct the experiments. NOZZLE column in Table 24 refers to 134 this distinction and each experiment can be traced in terms of its injection type. Also, WATER IN column refers to the injection mass and is used as another indicator for each experiment. Three injection mass flow rates (0.30 l/s, 0.15 l/s and 0.10 l/s) were used for consistent data management and easier categorization. In all the conducted lab tests, only dry cooling (i.e., sensible cooling) of cold injections were observed with small evaporation (on / closer to walls) and some saturation (at the core of the spray mostly). Low evaporation was expected as explained earlier in this chapter (i.e., low enthalpy -cold- injection). On the other hand, none of the experiments yielded a full saturation at the outlet. There were some cases showed very close results to saturation (i.e., RUN2,3,5 etc.), however, behavior after full saturation was not observed in the lab scale for the listed experiments. This behavior is rather discussed under full-scale model. There might be several reasons for not attaining full saturation at the lab scale, these reasons and their impact will be further investigated in future studies. Under normal industrial scale conditions, bulk air coolers would attain some level of saturation at the cooler outlet. This happens due to several reasons such as using much higher water/air mass ratios, having multi-stage injections and allowing more conditioning time for inlet air. This type of injections results in cooling of the air to a level that it cannot contain the vapor content that it has at the input. After this point saturation with condensation occurs. Much of the discussions around this behavior will be further explored again in full-scale for higher water/air ratios. 135 Note that; all the collected data needed to go through a post-process for categorizing and plotting. For plotting the data, average of the sensors is used. Meaning that the timely average of the 9 outlet readings were used as the representative of the outlet conditions. Additionally, these results are used during the comparison of the outlet conditions of the experimental and numerical studies. Following figures are some plotted examples for couple of runs as indicated. Figure 45 – Example RUN #10 Data Plots 22242628300 100 200 300 400 500 600 700 800 900TEMP (C)TIME (S)Average Experimental Temperature Poly. (Average Experimental Temperature)296.13 °K = 22.98 °C01020304050600 100 200 300 400 500 600 700 800 900RH (%)TIME (S)Experimental Relative Humidity Poly. (Experimental Relative Humidity)53.30 %136 Figure 46 – Example RUN #11 Data Plots 22242628300 100 200 300 400 500 600 700 800TEMP (C)TIME (S)Average Experimental Temperature Poly. (Average Experimental Temperature)295.82 °K = 22.67 °C0102030405060700 100 200 300 400 500 600 700 800RH (%)TIME (S)Experimental Relative Humidity Poly. (Experimental Relative Humidity)62.1 %137 Figure 47 – Example RUN #16 Data Plots 18202224260 100 200 300 400 500 600TEMP (C)TIME (S)Average Experimental Temperature Poly. (Average Experimental Temperature)293.07 °K = 19.92 °C 3035404550556065700 100 200 300 400 500 600RH (%)TIME (S)Experimental Relative Humidity Poly. (Experimental Relative Humidity)63.73 %138 4.8. Lab-Scale Model Validation With the explained procedure and experimental conditions, 18 experiments were conducted. To remark the experimental inlet and outlet conditions of the conducted experiments one can refer to the Table 24 shown earlier in this chapter. On the other hand, same experimental information was transferred to Fluent for CFD simulation of each case. All information relating to boundary conditions, injection properties and numerical approach were laid out earlier in this chapter. Once all the boundary conditions applied as discussed earlier, results were collected from the CFD simulations after they reached a steady-state solution (i.e., as the attained results are capped and will not change even further iterations are dictated). Once the steady-state conditions were reached in the solution, the experimental and numerical results are compared with respect to 4 important properties to make sure that results are physically sensible and valid. Following list is the four parameters used during experimental validation. - Outlet average static air temperature - Outlet average relative humidity - Outlet average enthalpy - Outlet average water temperature 139 4.8.1. Summary of Numerical Simulations and Example Cases Following figures represent the numerical solution contour outputs taken when the steady state is reached during the solution. This means the solution is capped to a result representing the solution of the picked case. For example, let us have a closer look to RUN#10 contour plots for temperature distribution: RUN #10 Properties: Type A Nozzle, 0.15 kg/s water injection at 6 °C Water Temp Temperature Contours (Top View of the Central Plane - Horizontal) Figure 48 - Horizontal Central Plane Temperature Contour Plot of RUN#10 at Steady State 140 Temperature Contours (Side View of the Central Plane - Vertical) Figure 49 - Vertical Central Plane Temperature Contour Plot of RUN#10 at Steady State Temperature Contours (Outlet) Figure 50 - Outlet Temperature Contour Plot of RUN#10 at Steady State 141 Droplet Contours Figure 51 - Droplet Temperature Distribution of RUN #10 at Steady State Figure 52 - RUN #10 Experimentation 142 Remark that RUN#10 is a Type A nozzle injection at 0.15 kg/s. At this injection rate, as expected, droplets are reaching a shorter distance and the size distribution is slightly coarser than 0.30 kg/s injection case. To show this difference let us contrast droplet positions of another Type A injection at a higher injection rate. To observe the changes let us have a closer look at the experiment RUN #3 with doubled water flow rate: RUN #3 Properties: Type A Nozzle, 0.30 kg/s water injection at 5.6 °C Water Temp Temperature Contours (Top View of the Central Plane - Horizontal) Figure 53 - Horizontal Central Plane Temperature Contour Plot of RUN#3 at Steady State 143 Temperature Contours (Side View of the Central Plane - Vertical) Figure 54 - Vertical Central Plane Temperature Contour Plot of RUN#3 at Steady State Figure 55 - Outlet Temperature Contour Plot of RUN#10 at Steady State 144 Droplet Contours Figure 56 - Droplet Temperature Distribution of RUN #3 at Steady State Figure 57 - Images of Different Injection Settings for Type A injection (Fig. a - at 0.30 kg/s and Fig. b – at 0.15 kg/s) 145 As it can be recognized from the contour plots and the injection comparison in Figure 57, spraying at 0.30 kg/s resulted in comparably more coverage of the chamber volume with droplets. Additionally, all the 4 cases representing 0.30 kg/s injection settings caused comparably higher vapor fractions resulting higher relative humidity values for similarly conditioned intake air. This is caused due to two reasons. Firstly, more injection mass and higher droplet coverage caused higher rate of cooling diminishing the static temperature relatively colder. Secondly, generation of finer droplet sizes allowed more evaporation in the chamber resulting in more vapor fraction inside the chamber. For instance, see RUN#3 and RUN#4 for comparison with respect to vapor fraction distribution inside chamber changing with respect to injection rate. First, remark the RUN#4 and RUN#7 properties shortly from Table 24 (as Table 25 below). Table 25 - RUN#4 and RUN#7 Cooling Performance Comparison ALL EXPERIMENTAL INLET OUTLET DATE ID NOZZLE WATER ᵒC AIR ᵒC RH % WATER IN (l/s) AIR IN (kg/s) ENT. (kJ/kg) WATER ᵒC AIR ᵒC RH % ENT. (kJ/kg) 21-Apr-22 RUN#3 A 5.6 28.3 29% 0.30 1.0 46.2 10.8 14.7 85% 36.8 22-Apr-22 RUN#4 A 5.5 27.9 32% 0.15 1.0 47.2 11.6 23.0 44% 42.7 As seen from the table, although inlet conditions of these two experiments are almost identical to each other, they have resulted in comparably different outlet conditions. In between these two conditions, in addition to minor differences in the inlet conditions, the most major difference is attained at the injection rates as it is doubled for RUN#3 comparing to RUN#4. Notice that; even at similar inlet conditions, RUN#3 yielded colder outlet temperatures yielding higher vapor fraction and RH due to above discussed reasons. 146 Please see the vapor fraction contour plots of these two cases as shown below: Figure 58 - Vapor Fraction Distribution within the Chamber RUN#3 @ 0.30 kg/s and RUN#4 @ 0.15 kg/s (Fig. a – RUN#3 and Fig. b – RUN#4) a b 147 From Figure 58, it is seen that the cooling performance of higher rate of injection is comparably more effective in comparison to the lower rate injection. This performance difference can also be highlighted with a comparison of the enthalpy results of these two runs. 𝑅𝑈𝑁#3 ∆ 𝑖𝑛 𝐸𝑛𝑡ℎ𝑎𝑙𝑝𝑦 = 9.4𝑘𝐽𝑘𝑔 𝑅𝑈𝑁#4 ∆ 𝑖𝑛 𝐸𝑛𝑡ℎ𝑎𝑙𝑝𝑦 = 4.5𝑘𝐽𝑘𝑔 As seen, with doubled injection rate, the cooling performance of the system is also doubled with a slight difference. Noting that these experiments were conducted in two different dates, this results sows that the experiments are done in a well-controlled environment and under similar conditions. It also shows that results are reproducible. Also, obtained results are in line with the thermodynamics discussed by the equation shown in Chapter 2, Semi-Empirical model Equation (1). Lastly, although this injection setting creates finer droplet size distribution around the perimeter of the cone that are more prone to drag and turbulence, central part of the overall injection (i.e., center of the full cone) was able to reach further lengths in chamber cavity comparing to the 0.15 kg/s case where droplets fall to the floor before reaching to the drifters as seen in Figure 51 and Figure 56. On the other hand, Type B injection created a different behavior in terms of cooling pattern in the chamber and the droplet distribution within the chamber is now in a counter-flow fashion with respect to the air flow. This type of injection is observed to create droplet break-ups during injection resulting a more spread distribution. This resulted in some of the finer droplets to drag 148 out from the system short after the injection point. Please refer to earlier sections discussing injection properties for more information on how this droplet break up is approached in the numerical solution by tuning the spread factor of the droplet distribution. On the other hand, it was also observed that some of the larger droplets managed to reach the inlet area and dragged back to the outlet on the chamber floor. For comparison of injection distribution inside the chamber, please see the following temperature distribution images from RUN#8 representing a Type B spray scenario. RUN #8 Properties: Type B Nozzle, 0.15 kg/s water injection at 8 °C Water Temp Temperature Contours (Top View of the Central Plane - Horizontal) Figure 59 - Horizontal Central Plane Temperature Contour Plot of RUN#8 at Steady state 149 Temperature Contours (Side View of the Central Plane - Vertical) Figure 60 - Vertical Central Plane Temperature Contour Plot of RUN#8 at Steady State Figure 61 - Droplet Temperature Distribution of RUN #8 at Steady State (Type B Injection) 150 4.8.2. Lab-Scale Numerical Results and Discussion Table 26 below shows all results of all 18 numerical runs and relative error margins yielded with comparison to experimental readings. As seen below, for all four comparison criteria the numerical results captured the experimental readings within 10 % error. Table 26 - Numerical Results NUMERICAL OUTLET ERROR ID WATER ᵒC AIR ᵒC RH % ENT. (kJ/kg) WATER AIR RH ENT. RUN#1 12.8 16.0 84% 40.2 3.7% 3.4% 1.9% 2.4% RUN#2 12.8 16.1 87% 41.3 1.0% 3.2% 2.8% 2.5% RUN#3 10.9 14.6 85% 36.9 0.9% 1.0% 0.6% 0.4% RUN#4 11.5 22.1 46% 41.6 0.9% 4.2% 4.3% 2.6% RUN#5 12.0 15.6 88% 40.3 1.8% 4.4% 1.8% 6.1% RUN#6 12.3 23.1 48% 44.7 2.3% 0.5% 8.3% 3.5% RUN#7 12.1 15.7 87% 40.3 0.5% 5.0% 3.6% 7.2% RUN#8 14.6 22.6 55% 46.7 4.0% 0.5% 5.5% 3.2% RUN#9 13.9 23.5 50% 46.5 1.8% 3.0% 6.0% 3.7% RUN#10 11.4 23.0 54% 47.2 3.5% 0.1% 1.2% 5.3% RUN#11 12.3 22.4 61% 49.1 2.1% 1.2% 2.2% 2.0% RUN#12 12.0 23.8 57% 50.9 0.1% 1.6% 6.8% 1.2% RUN#13 15.9 26.9 54% 57.6 3.8% 0.6% 9.3% 4.1% RUN#14 14.5 26.3 51% 54.5 0.2% 0.7% 4.7% 1.7% RUN#15 10.6 20.8 66% 46.5 0.5% 3.2% 2.6% 2.0% RUN#16 11.0 20.0 61% 42.7 2.6% 0.5% 4.6% 1.9% RUN#17 15.1 28.4 40% 53.2 3.7% 1.5% 7.8% 1.6% RUN#18 13.2 23.2 56% 48.6 1.2% 1.3% 2.7% 3.6% Results shown in Table 26 was also represented as error charts to show the scatter of the results. In the following configuration, diagonal black dashed line indicates zero error line. As spreading away from the central line, other error margins appear with light-grey and red color representing 5% and 10% error margins respectively. Finally, the blue marks on the graphs represent the numerical component of each experimental work and showing the scatter of the results across the study range. 151 Figure 62 - Outlet Average Static Temperature Comparison Chart shows the scattering of the numerical results in terms of the average static temperature of the outlet. Numerical results are mass flow averaged at the outlet of the test domain and the results were logged for this comparison. 101214161820222426283010 12 14 16 18 20 22 24 26 28 30Numerical Results (ᵒC)Experimental Results (ᵒC)Outlet Temperature ComparisonZero Error Line Temp Results +5% Line-5% Line +10% Line -10% Line152 Figure 63 - Outlet Average Relative Humidity Comparison Outlet average relative humidity data comparison also shows a high agreement with the experimental results. Again, printed results represent ‘mass weighted average’ readings from the outlet face of the numerical model domain. 3040506070809010030 40 50 60 70 80 90 100Numerical Results (%)Experimental Results (%)Outlet RH ComparisonZero Error Line RH Outlet +5% Line-5% Line +10% Line -10% Line153 Figure 64 - Outlet Average Enthalpy Comparison Chart shows the scattering of the numerical enthalpy results in comparison to the experimental readings. As seen, enthalpy results obtained from the numerical model also fall within 10% error range in comparison to experimental readings. 30354045505560657030 35 40 45 50 55 60 65 70Numerical Results (kJ/kg)Experimental Results (kJ/kg)Outlet Enthalpy ComparisonZero Error Line Enthalpy Outlet +5% Line-5% Line +10% Line -10% Line154 Figure 65 - Outlet Average Water Temperature Comparison Finally, numerical average outlet water temperatures are sampled from the steady state DPM locations and compared with the average readings read from the spray unit return pump temperature sensor. 10111213141516171810 11 12 13 14 15 16 17 18Numerical Results (ᵒC)Experimental Results (ᵒC)Outlet Water Temp. ComparisonZero Error Line Temp Results +5% Line-5% Line +10% Line -10% Line155 As seen in Figure 62, Figure 63, Figure 64, Figure 65, numerical simulation results are in a good agreement with the experimental results in all the four aspects of comparison. All the experiments are designed in a way that different conditions are to be tested and the boundary conditions for the numerical solution were set in accordance with these changing properties. 4.8.3. Lab-Scale Model Grid Sensitivity Study To investigate the grid sensitivity of the lab scale model a case (RUN#4) is selected and run for different mesh sizes named as Coarse, Medium, Fine and Ultra-fine mesh. The mesh statistics for these different grid sizes can be found in Figure 66 below. Figure 66 - Grid Sensitivity Study and Relative Error Curve 0.00%0.05%0.10%0.15%0.20%0.25%0.30%0.35% - 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 1,600,000 1,800,000 2,000,000Relative ErrorNumber of ElementsGrid SensitivityCoarse MeshMedium MeshFine MeshUltra-Fine Mesh156 Mesh sensitivity study done over the lab scale model has shown that the model is robust, and the solution domain is grid size independent as the relative difference between all the runs has not surpassed 0.35% by yielding almost identical results for outlet conditions. 4.9. Introduction to Full-scale Model As introduced in Chapter 2 with detail, there is an established method to use in sizing industrial scale bulk air coolers. This model is semi empirical by its nature and gives reliable results when the cooler geometry, airspeed, drop size range and some similar criterion discussed earlier are kept within the studied range. However, this kind of cooling systems are complex, and it is hard to find out the impact of its components on the overall system performance. This creates granularity problem for specific cases in terms of performance estimation. Luckily, numerical modeling could ease this problem by offering high level of parametric control over the physics governing the heat transfer inside the cooling chamber. To prove this concept, first, a numerical domain is upscaled from the model introduced earlier for the lab-scale simulations. The model construction is identical to the numerical arguments discussed earlier in this chapter. However, there made some physical modifications to the lab-scale model in terms of injection points, staging etc. Note that, at this point, it was important to develop a generic numerical approach that could be used to simulate multi-stage bulk coolers and set a baseline for further studies. This baseline work, when validated, is then could be used to better understand the system sensitivity based on operating parameters such as drop size, air velocity, injection design, nozzle placement etc. Eventually, once a detailed parametric understanding is established, the model could be used for tweaking the semi-empirical model 157 factors in the way of establishing high merit factor chambers with smaller sizes or less underground development. To test this statement, a numerical model is developed and compared with the industrial model. This comparison is based on the example case study to be shown further in this chapter. 4.9.1. Model Geometry The model geometry is built based on the design guidelines discussed earlier in Chapter 2 (Page: 52). Certainly, to match the results of the semi-empirical solution, the full-scale model was expected to respect the set-out guidelines. Accordingly, a chamber with square cross-section with 3.1623 m edge length (or 10 m2 area) is drawn. The chamber length is kept at 35m to maintain sufficient space for nozzle placement allowing a multistage injection for 30 kg/s water. As discussed earlier in Chapter 2 earlier, multistage injection (i.e., 2.5 Stage) means that the primary stage receives 30 kg/s constantly and other stages recycle the water that returns from the previous stage. In other words, with 𝑁 × 𝑚?̇? the cooling chamber occupies 75 kg/s water at any given instant once the steady state is reached (i.e., meaning all nozzles are operating at their full pressure with 30 kg/s fresh + 45 kg/s recycled). This was ensured with placement of 30 individual injections (i.e., totaling 60 injection points at 1.25 kg/s injection) on each side wall facing each other with 60° angle. Note that, this design not only ensures minimized computational burden but also considers the water loading limit of 0.25 l/s/m3 set by Bluhm et al. [51]. A simplified sketch of the full-scale model with dimensions attached can be seen in Figure 67 below. Also, see Figure 68 for ‘Fine’ size meshing of the domain. Note that a detailed mesh 158 sensitivity study for full-scale model was also as it was done for the lab-scale study earlier. For full-scale model three different element sizes were considered. This study will be further shared under the ‘grid sensitivity’ section further below. Figure 68 shows only the fine mesh case. Figure 67 - Geometry of the Computational Domain Developed for Full-Scale Model Figure 68 - Fine (0.05 m) Mesh of the Domain 159 4.10. Changes Introduced to the Model for Upscaling It is worthwhile to mention some of the minor changes made to the discrete phase model for this solution. First, the lab-scale model was a steady-state solution and the particle tracking at steady-state is rather a straightforward solution where the streamlines are calculated first, and the solution is iterated based on continuous phase-discrete phase interaction. For transient model, however, it is impossible to apply the streamline approach for discrete phase solution because the faith of the particles is required to be solved in with a transient approach where each particle location 𝑥(𝑡) is expected to be represented based on the fluid flow time t. For this type of DPM solutions, ‘Unsteady Particle Tracking’ can be employed where parcels representing droplet masses and they are iterated based on the flow of time set in the solution. Also, Spray locations are set in accordance with the design guidelines shown in Chapter 2. The first row of injection is set to 1.5 m after the inlet with 1 m spacing between each rows having 30 rows with 2 injection points in each row. Injection points are represented by 100 streams and the mass balance is matched with case study solved by Bluhm et al. during validation. Also, the DPM injection velocities are set as the leaving DPM droplets makes a natural contact with the roof as covered in Figure 11 earlier and discussed in page 52. Following figure shows the developed solution domain for modeling the multi-stage, full-scale bulk air cooler discussed in the case study. Notice the injection points and the direction of the airflow. 160 Figure 69 - A Representative 3D View of the Solution Domain and Injection Points Furthermore, when it comes to droplet size, the droplet range is different than what was discussed for the lab scale solution. In this, full-scale cooler study, 4-different droplet size settings are used to during the validation of the numerical model. Main reasons to use a wider range of droplet sizes in this solution can be summarized as: - To prove that the numerical model is capable in capturing the impact of some important parameters that wasn’t studied before such as the impact of droplet size distribution on the cooler performance. - To test the model within the specs range discussed by Bluhm et al. [51] - To show that merit factor is a sensitive empirical factor, and this was not much associated with parameters impacting cooling performance such as droplet size and assumed as a fixed factor during semi-empirical solution. 161 Some of the injection settings used during the numerical solution for full-scale model are shown in the following table (Table 27). Table 27 - Injection Settings Used During the Full-Scale Solution Injection Setting Value Particle Type Droplet Droplet Diameter 0.001 m / 0.0008 m / RRD* Cone Type Solid Flow Rate 1.25 kg/s/nozzle (60 Nozzles) Staging (2.5 Stages) 30 kg/s Primary + (30 kg/s 2nd + 15 kg/s 2nd ½) The RRD* term in the table above refers to truncated Rosin-Rammler-Distribution and for these settings two other size distribution settings were studied scattered according to parameters shown below. Table 28 - Rosin-Rammler Distribution (RRD) Summary Inlet Boundary Conditions Truncated Size Distribution (Low – Mean – High) RRD Regular 500-μm – 1 mm – 2.5 mm RRD Fine 500-μm – 800-μm – 2.5 mm Spread Factor 3.5 Along with RRD options, overall, 4-different size settings are simulated based on the droplet size range (i.e., minimum droplet size 500-μm and maximum droplet size 2.5 mm) discussed in [51]. 162 4.11. Boundary Conditions of Full-Scale Model For easier management of the continuity of the problem and to match the flow of the example case discussed earlier ‘mass-flow inlet’ boundary condition was selected. For outlet boundary condition, ‘pressure-outlet’ condition was selected. Turbulence intensity was set to 10% as offered by the default settings of the solver. Like the lab scale model, all the walls are set as to be stationary walls with zero heat flux via the exterior. For the DPM boundary conditions, side walls and outlet boundaries are set to reflect DPM (i.e., walls and drift eliminators) and the bottom boundary (i.e., working as a sump) was set to escape. To represent the spray interaction with the walls, wall reflection coefficient on the side walls is set to 0.1 meaning that the parcel normal and tangential momentum is reduced to its 10% after the collision. Additionally, for DPM injections ‘Temperature Dependent Latent Heat’ and ‘Two-way Turbulence Coupling’ models were also activated for higher precision. 4.12. Full-Scale Cooling Case Study To demonstrate capabilities of their model and to show how to approach industrial size problems, Bluhm et al., in their work, ‘The design of spray chambers for bulk cooling of the air in mines’ [51] present some examples. This case study predicts the cooling performance of a multi-stage cooling chamber that is satisfying the design guidelines in the same study. To match this solution same design guidelines and operating conditions are introduced to the full-scale model developed in Fluent. Followings are the operational characteristics of the published case study. 163 Table 29 - Inlet Conditions of the Case Study (After Bluhm et al.) Inlet Boundary Conditions Value Inlet Air Dry Bulb Temperature 28 °C Inlet Air Wet Bulb Temperature 32 °C Air Mass Flow Rate 60 kg/s Water Mass Flow Rate (with 2.5 Stage) 30 kg/s (+ 45 kg/s recycling w/ merit of 0.65) Chilled Water Inlet Temperature 10 °C Atmospheric Pressure 110 kPa Once they introduce the case and operational characteristics of the problem, they analytically solve the case with the semi-empirical model that was discussed earlier in Chapter 2 (Page: 46). Let us have a closer look to their solution as seen below: Solution as in [51]: 𝑆𝑖𝑛,𝑎𝑖𝑟 (@ 28 °𝐶 𝑊𝐵) = 82.0𝑘𝐽𝑘𝑔 𝑆𝑖𝑛,𝑤 (@ 10 °𝐶) = 27.4𝑘𝐽𝑘𝑔 𝑚𝑎𝑖𝑟 = 60𝑘𝑔𝑠 𝑚𝑤𝑎𝑡𝑒𝑟 = 30𝑘𝑔𝑠 Setting up the merit factor for a 2.5 stage cooling chamber, F = 0.65; therefore, 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑓𝑎𝑐𝑡𝑜𝑟 = 𝑅 =𝑚𝑤𝐶𝑤(𝑡𝑤,𝑖𝑛 − 𝑡𝑎,𝑖𝑛)𝑚𝑎(𝑆𝑤,𝑖𝑛 − 𝑆𝑖𝑛)=30 × 4.18 × (28 − 10)60 × (82.0 − 27.4)= 0.689 Using Equation (8), E = 0.743 and this effectiveness is accepted as ‘water efficiency’ as discussed in equation (6). Accordingly, using equation (2) delta temperature of the water side can be estimated as: 𝜂𝑤 = (𝑡𝑤,𝑖𝑛 − 𝑡𝑤,𝑜𝑢𝑡𝑡𝑤,𝑖𝑛 − 𝑡𝑎,𝑖𝑛) 𝑡𝑤,𝑜𝑢𝑡 − 𝑡𝑤,𝑖𝑛 = 0.743(28 − 10) = 13.4 °𝐶 𝑎𝑠 ∆𝑇 𝑤𝑎𝑡𝑒𝑟 164 This means that water injection temperature has increased by 13.4 °C at the outlet and this yields a heat transfer of ~1.7 MW in between water and air for the purpose of bulk air cooling as seen below. 𝑄 = 30𝑘𝑔𝑠× 4.18𝑘𝐽𝑘𝑔 °𝐶× 13.4 °𝐶 = 1,680 𝑘𝑊 As seen, the solution uses an analytical approach (i.e., energy balance equation) merged with some empirical categorization (i.e., merit factor estimation and staging) to conclude the estimation at the outlet conditions of the selected BAC. This solution returns reliable results provided that the design recommendations discussed prior to the solution are well met. Following table is a summary of the results: Table 30 - Bluhm et al.'s Solution Results Water Outlet Temperature 23.4 ᵒC Air Outlet Temperature (Wet bulb) 20.3 ᵒC Air Outlet Temperature (Dry bulb) 20.3 ᵒC Outlet Relative Humidity 100 % Air Outlet Enthalpy 54.0 kJ/kg 4.13. Full-Scale Model Validation: Results and Discussion Validation of the full-scale model was conducted on the example case that was studied by Bluhm et al. in (p. 7) [51]. For more details about the selected case and the details of the semi-empirical solution please refer to Chapter 2. For model comparison, some of the most essential outlet 165 parameters associated with the cooling performance of BACs (i.e., temperature, vapor content, RH and sigma heat of the system) are used. Numerical results are plotted with the semi-empirical solution results and discussed for each parameter. Note that, semi empirical solution is a steady state solution, therefore is shown as a straight line on the comparison graphs below. 4.13.1. Temperature, Relative Humidity, Vapor Mass Fraction, and Sigma Heat Comparison of All Solutions First comparison is made based on the outlet temperature results of both models. All temperature results are shown in Figure 70 below. Note that, on Figure 70, the red dashed line shows the inlet temperature of the BAC (@ 32 °C for all cases), the black dashed line at the bottom represents the steady-state results obtained from the semi-empirical model and the other colors with curves are representing the transient solution results obtained from the numerical solution. There are four-curved dashed lines representing 4-different droplet sizes discussed earlier. As the results have shown, all the 4 curves are close to the semi-empirical solution results (< 5%). However, in all the droplet size cases 800-μm case converged the semi-empirical solution with exactly matching results. Also, RRDfine has resulted in very close results too with slightly overperforming semi-empirical estimation. Also, see Figure 71 for the temperature contours of the chamber across its stages. As seen in the figure, the warmer air is cooled through the stages gradually and leaves the chamber when it reaches its coldest state. 166 This was intended by multi-stage cooling allowing the primary stage injecting close to the outlet to create a counterflow effect for better results. The solution clearly agrees with this argument as shown below. Figure 70 - Static Temperature Comparison of the Numerical Model with the Semi-empirical Solution Figure 71 - Static Temperature Distribution within the BAC (@ steady-state w/ RRD Regular) 1920212223242526272829303132330 5 10 15 20 25 30 35 40Temperature (°C)Time (s)Temperature (1mm) Inlet Temperature Bluhm's Semi Empirical Sol.Temperature (800 Micron) Temperature RRD Reg Temperature RRD Fine20.2 ◦C Experimental20.2 ◦K Numerical 800µm Droplet21.1 ◦K Numerical 1mm Droplet20.7 ◦C Numerical RRD Droplet20.0 ◦C Numerical RRD Droplet FineInjection Temperatures (set based on merit factors in [51]): -Stage 1: 10.0 °C -Stage 2: 19.0 °C -Stage 2½: 23.0 °C RRD Reg. 2nd and ½ Stage 2nd Stage 1st Stage 167 Contemplating on the cooling behavior inside the chamber, one would notice that; as the static temperature of the air drops via the heat exchange between the droplets and the air, the air starts to saturate. Note that during this process, the air starts losing its vapor mass fraction around the injection points due to local saturation. This shortly means that, as the static temperature of the air drops, it becomes less accommodating for vapor at that temperature by the Antoine’s Law. At this point, dehumidification by cooling starts. During this process, expected relative humidity behavior is to first ramp up to 100% at the outlet and remain there as the cooling progresses to its steady-state equilibrium. This means, once the saturation by cooling is achieved, every other cooling added to the system (or simply every heat removed from the air) will result in more vapor loss lowering the wet-bulb temperature of the system (i.e., also the sigma heat). This is called bulk air cooling and this behavior can be multiplied with multi-stage arrangements for more cooling. However, as Bluhm et al. [51] shows that adding more stages than 4-stages has no more significant contribution to the overall cooling. Capturing this behavior is important in terms of ensuring proper mass transfer during the cooling. This is maintained by tuning the Lee’s mass transfer coefficient in a way that the vapor condensation was kept at a rate that the solution maintains saturated outlet conditions once reached. At this point, improper management of Lee’s mass transfer coefficient would yield results either super-saturated (i.e., RH > 1) or under-saturated results (i.e., RH<1) and these conditions are not physically correct for BACs. Accordingly, this saturation behavior was also used as another ‘boundary condition’ in setting Lee’s coefficient settings during the solution. Following figure shows the vapor loss behavior within the cooling chamber. See all the results for relative humidity comparison in the figure below. 168 Figure 72 - Relative Humidity Comparison for All Cases Also, see the average relative humidity across the chamber length in Figure 73 below. As seen, the full saturation of air is already achieved near the second stage, indicating the rate of condensation will only be increasing towards the outlet as the temperature drops further. This is expected; hence, the decreasing chamber temperature dictates saturation around ~10m chamber length and the vapor in the air-vapor mixture starts to condense into liquid water form. Emergence of this condensation behavior and where the air-vapor mixture starts to lose its vapor content can also be seen in Figure 75 below. It should be also noted that, this saturation zone is also where the wet bulb of the system starts to drop showing that the sensible cooling is started 0.70.750.80.850.90.9510 5 10 15 20 25 30 35 40Relative HumidityTime (s)Relative Humidity 0.07 m Grid Inlet Relative HumidityBluhm's Semi Empirical Sol. Relative Humidity 0.07 m Grid (800 Micron)Relative Humdity 0.07 m Grid RRD Relative Humidity 0.07 m Grid RRD Fine100 % RH Experimental~100 % RH NumericalInlet RH169 to being achieved through the cold the injection and importance of multistage cooling in maintaining this behavior. Figure 73 – Average Relative Humidity Change across the Chamber Length On the other hand, Figure 74 below shows the vapor loss behavior of the inlet air during cooling. As expected, finer droplet distributions are offering higher cooling performance with more drop in the vapor fraction. This is logically credible, as the finer distributions are expected to offer higher heat flux capacities due to increased contact area and more mixing due to drag forces and turbulence coupling. Also, like the temperature results, 800-μm case resulted in with an exact match with the semi-empirical solution results. To better understand the bulk distribution of vapor mass fraction within the bulk-air-cooler, please see the following figure. 75%80%85%90%95%100%0 5 10 15 20 25 30 35RHChamber Length (m)Average Relative Humidity Across Chamber Length for RRD Regular Case170 Figure 74 - Transient Behavior of Vapor Mass Fraction During Bulk-Air-Cooling Figure 75 - Vapor Mass Fraction Distribution within Chamber (@ steady-state w/ RRD Regular) 0.0130.0140.0150.0160.0170.0180.0190.020.0210 5 10 15 20 25 30 35 40Vapor Ratio kg/kgTime (s)Vapor Mass Fraction 0.07 m Grid Inlet Vapor Mass FractionBluhm's Semi Empirical Sol. Vapor Mass Fraction 0.07m Grid (800 Micron)Vapor Mass Fraction 0.07 m Grid RRD Vapor Mass Fraction 0.07 m Grid RRD Fine0.0143 kg/kg Numerical 1mm Droplet0.0138 kg/kg Experimental0.0137 kg/kg Numerical 800µm Droplet0.0140 kg/kg Numerical RRD0.0135 kg/kg Numerical RRD Fine2nd and ½ Stage 2nd Stage 1st Stage 171 As mentioned, the phase change in the chamber (i.e., condensation) will result in formation of a new phase in the chamber and, logically, this new mass (waterliquid) is expected to coalesce around the coolest locations of the chamber due to local saturation of these cells as explained above. These ‘coolest locations’ in the chamber are certainly represented by the cells around the injection points and the areas closer to the outlet (i.e., primary stage) as shown on Figure 71 previously. Here, Figure 76 below shows the condensation locations as expected. See, the condensation starts to appear in the second stage around the injection points and it intensifies towards the 1st stage (i.e., closer to the outlet) representing highly saturated locations yielding more water formation within the chamber. On the other hand, not much water phase is formed in the 2nd ½ stage as this stage has the minimum temperature drop due to counterflow effect discussed earlier. Figure 76 - Water Phase Forming around the Injection Points and the Outlet Where the Coldest Temperatures are Attained (@ Steady-state w/ RRD Regular) RRD Reg 2nd and ½ Stage 2nd Stage 1st Stage 172 Capturing condensation zones and the amount of condensation locally as seen in Figure 76 is not possible with semi-empirical models. At this point, as offered, numerical modelling offers much granular information about the multiphase behavior of the water within the chamber. Also, see Figure 77 for droplet temperature distribution when the steady state is achieved for the RRD regular case. Here, please notice the reflection of the droplets from the outlet boundary representing the drift eliminators for such cooling systems. Also see that the droplets once they interact with the walls, they leave the chamber from the bottom boundary. The stages are marked on the figure for easy understanding of the transition between stages. Figure 77 - Injection Points and Droplet Temperatures across the Cooling Chamber (@ Steady-state w/RRD Regular) Finally, the sigma heat (S) of each solution was compared in Table 31 below. As the results show, all four cases are yielded <5% relative error showing high agreement between numerical and semi-empirical results. 173 Table 31 - Sigma Heat Comparison of All Cases and Inlet Outlet Summary of All Solutions Case ID Inlet DB Temperature (° C) Inlet WB Temperature (° C) Outlet Saturated Temperature (°C) Outlet Vapor Fraction (kg/kg) Inlet Enthalpy (kJ/kg) Sigma Air (Sp. Energy) (kJ/kg) Rel Error % Bluhm et al. 32.0 28.0 20.3 0.0138 82.0 54.0 - Ultra-Fine Droplet (@ 800 μm) 20.2 0.0137 53.8 0.00 Regular RRD 20.7 0.0140 55.4 0.03 Fine RRD 20.0 0.0135 53.2 0.02 Fine Droplet (@ 1mm) 21.1 0.0143 56.7 0.05 4.13.2. Grid Sensitivity Study for Full-Scale Solution As briefly mentioned earlier, a standalone mesh sensitivity study was done for full-scale model as well to ensure the model physics are insensitive to changes in the grid size and robustly maintain the solution for different grid sizes. Accordingly, three different mesh sizes are studied and temperature and vapor mass fraction results of these three runs were compared. This study was done for 1-mm (i.e., fine droplet) case. Following table lists mesh statistics of these three cases. Table 32 - Mesh Statistics Used in the Mesh Sensitivity Study Mesh Type Element Size (m) Number of Nodes Number of Elements Fine 0.05 2,887,680 2,794,176 Medium 0.06 1,717,524 1,651,692 Coarse 0.07 1,068,580 1,020,600 174 As seen from the figure, the full-scale numerical model is responded to ranging grid sizes with almost no difference in temperature and vapor fraction results, proving that the solution domain is mesh independent. Figure 78 - Grid Sensitivity Study for Full-Scale Model Comparing Static Temperature and Vapor Fraction of Different Mesh Sizes (Left Axis: Static Temperature and Right Axis: Vapor Mass Fraction) 4.14. Implications of Changing Droplet Size on Merit Factor Here, it is important to remark that semi-empirical methods do not offer granular information about some of the most important contributors of the cooler performance, such as droplet size distribution as discussed above. This study has shown that droplet size distribution of the spray injections is very critical in determining the system merit factor and in conventional designs this impact is taken into consideration with generic approach. Such as offering a range of droplet size for a given range of merit factors. On the other hand, numerical methods could add more 0.010.0150.020.0250.03101112131415161718192021222324252627282930313233340 5 10 15 20 25 30 35 40Vapor Mass Fraction (kg/kg)Temperature (°C)Time (s)Temperature 0.05 m Grid Temperature 0.06 m GridTemperature 0.07 m Grid Vapor 0.05 m GridVapor 0.06 m Grid Vapor 0.07 m Grid175 granularity to the merit factor determination by offering more control in understanding the contributors of the heat exchange within the cooler chamber. This was one of the main objectives of this thesis work. Remark that, to establish a semi empirical solution, an estimate of merit factor is required initially and for the example case the merit factor for the mentioned 2.5-Stage cooler was selected as 0.65 by Bluhm et al. [51]. However, note that solutions offered by the numerical model show slight shifts from the original merit factor selected in the example solution because of changing droplet sizes. This shows that the empirical design ranges can be further detailed in terms of granularity of information with addition of other physical parameters, such as droplet size distribution, to the equation as seen on Figure 79. Figure 79 - Merit Factor Sensitivity Based on Different Droplet Size Settings As shown on the Figure 79, the merit factor of the system (for the same operating conditions and design benchmarks) shows slight shifts towards left and right based on changing droplet sizes indicating system’s sensitivity to droplet size distribution. Capturing this operational sensitivity is not possible with the existing design methods. -End of this Chapter- 0.5900.6000.6100.6200.6300.6400.6500.6600.670Fine Particle (@1mm)Regular RRD Bluhm et al. Ultra-Fine Particle (@ 800 μm)Fine RRDMerit Factor176 CHAPTER 5 –Summary, Recommendations and Future Work 5. Summary, Recommendations and Future Work 5.1. Summary In this dissertation, as a response to growing number of deep underground mines, mine bulk-air-cooling systems were studied in multiple aspects. The studies done under this research work extended from providing end-to-end designing process of a cooling system to investigation of design methods and proposal of numerical approaches to the existing methods. The results of this study have revealed that current knowledge on bulk-air-coolers can be reconsidered and improved in multiple aspects. To start with, the case studies done on the alternative cooling systems presented in Chapter 3 have shown that the energy required to cool mines can be sourced from alternative, natural sources such as lake or solar photovoltaic energy. For example, a real-life case study done on an ultra-deep Canadian underground mine has shown that nearly CAD $ 12M CAPEX savings are possible by replacing a conventional chiller unit with a lake cooling system. Also, other additions to this natural cooling systems, such as bulk-air-heating and ice storage systems are also helpful in downsizing capital and operational expenses of mining operations. Moreover, it has been revealed that existing bulk-air-cooling design methods are too generic in describing the cooler physics and lack granularity in explaining the importance of performance affecting parameters such as droplet size distribution of sprays. To investigate this granularity problem with closer angle, experimentation was needed. However, the literature survey conducted on studies investigating similar spray cooling applications in the field has revealed that 177 much of the earlier work was focusing on evaporative cooling of hot water injections, and they were not expanding on cooling humid air with low enthalpy water. Therefore, an experimental system was designed and installed to the university lab for further exploring the interaction between cold water and hot air in the lab-scale. Accordingly, a diverse set of tests were conducted at the lab with different combinations of operational settings (i.e., different droplet sizes, different combinations of injection and airflow settings, different inlet conditions and so on). To complement the experimental work, a Eulerian-Lagrangian, multiphase numerical model was developed and validated with the experimental results. This was an essential step for approaching the full-scale problem and provided the basis of the developed numerical model for multiple stage cooling scenarios. This study has shown that the developed multiphase model was able to capture the experimental results within 5-10 % agreement. Note that, achieving this multiphase behavior was not given as part of the CFD solver and it required development of a code to couple the vapor phase to liquid phase with a mass transfer criterion as a user-defined-function. This code was required to mimic the partial saturation of humid air in the chamber to represent the condensation phenomenon that occurs during bulk cooling. This code and its coupling to the solver was also presented in this work with detail for future references. Moreover, lab-scale model is also upscaled to a full-scale, industrial model to further explore the granularity problem associated with range of merit factors used in the field. Developed full-scale model was then integrated to a case study and results were compared with industrial scale results. Full-scale simulations were also in agreement (< 5% error) with industry accepted models proving that the developed numerical model is scalable to different sizes. 178 To further delve into the granularity problem, different droplet size distributions were studied within the range offered by the industrial models, and it is proven that for the same design settings, changing droplet size distribution of a cooler has slight effect on the merit factor selection for semi-empirical solutions. This revealed that standardized merit factor selection could be insufficient in explaining actual characteristics of a bulk-air-cooler under different operational settings. Agreement in full-scale results has also proven that bulk-air-cooling operations that do not fit into the design guidelines provided earlier can still be successfully modeled numerically with higher precision. Lastly, grid sensitivity of both lab-scale and full-scale models were tested with various grid sizes. With this study, it is shown that both solutions were proven to be insensitive to grid size providing same results without compensating from robustness for different mesh sizes. 5.2. Future Work and Recommendations This dissertation and the numerical studies shown here are considered as the beginning of a new way of looking to an older problem. By using the developed numerical model, now the semi-empirical model can be challenged from many aspects. These aspects could be re-modelling of merit factors by using implicit factors assumed in the model such as droplet size, air velocity, turbulence in the chamber, chamber wall roughness, water loading per volume etc. As discussed in Chapter 2 and mentioned several times in Chapter 4 these factors can be investigated with numerical methods in the way of better understanding the energy balance within the cooling chamber. Most certainly, referring to energy balance in between the inlet and outlet of the cooler 179 during the design process is a way to design full-scale BACs, however, numerically modelling the controlling parameters could be a way to optimize these systems with much higher precision. Moreover, the numerical models studied here neglected the droplet-droplet interaction. Modelling this behavior is a complex study and requires laser photography for validation purposes. Similarly, droplet sizes used in this study were mostly acquired from relevant literature and manufacturer’s catalogues for DPM model. Droplet size analysis also requires high-speed laser scanning of the chamber for droplet size assessment. Therefore, studies listed in this dissertation can be further enhanced through addition of laser aided measurements in the future. 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Thesis/Dissertation
eng
Mining Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Ghoreishi-Madiseh, Seyed Ali
Numerical and experimental study of the performance of bulk-air spray coolers and renewable cooling systems for application in mine ventilation
Text