Applied Science, Faculty of
Civil Engineering, Department of
DSpace
UBCV
Kumaran, Varshaa
Master of Applied Science - MASc
University of British Columbia
Gravity-driven membrane filtration (GDMF) is a process in which hydrostatic pressure (gravity) serves as the driving force to maintain water flow through the membrane. Limited fouling control measures are applied, resulting in the formation of a biofilm layer on the membrane surface. This porous layer hosts a stable microbial community, crucial for sustaining water output. GDMF systems are simple to operate and cost-effective, making them well-suited for small-scale applications. Typically, the biofilm layer in GDMF systems enhances the removal of various contaminants, including organics and viruses. At the same time, the presence of the biofilm layer increases the resistance of to water flow, decreasing overall throughput. To increase the overall throughput, passive (gravity-driven) fouling controls, such as air scouring, and tank drain can be applied to GDMF systems. However, limited knowledge exists on the efficacy of these techniques. This present study addresses critical knowledge gaps in the literature regarding the effects of the passive fouling controls in gravity-driven membrane filtration systems; referred to as passive gravity-driven membrane filtration (PGDMF). The results of the study demonstrate that passive fouling controls significantly contribute to increased overall throughput. The study also concludes that passive fouling controls did not impact the chemical and biological properties of the biofilm layer developed on the membrane surface. The study also concludes that, after it is established at 1-2 weeks, the microbial community present on the biofilm layer is not similar to the microbial community present in the feed or reject of the PGDMF system.
Comparative study of biofilm in GDMF and PGDMF systems by Varshaa Kumaran B.Tech., National Institute of Technology, Tiruchirappalli, 2020 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Applied Science in The Faculty of Graduate and Postdoctoral Studies (Civil Engineering) The University of British Columbia (Vancouver) January 2024 © Varshaa Kumaran, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Comparative study of biofilm in GDMF and PGDMF systems submitted by Varshaa Kumaran in partial fulfillment of the requirements for the degree of Master of Applied Science in Civil Engineering Examining Committee: Dr. Pierre R. Bérubé, Professor, Department of Civil Engineering, UBC Supervisor Dr. Sara Beck, Assistant Professor, Department of Civil Engineering, UBC Supervisory Committee Member iii Abstract Gravity-driven membrane filtration (GDMF) is a process in which hydrostatic pressure (gravity) serves as the driving force to maintain water flow through the membrane. Limited fouling control measures are applied, resulting in the formation of a biofilm layer on the membrane surface. This porous layer hosts a stable microbial community, crucial for sustaining water output. GDMF systems are simple to operate and cost-effective, making them well-suited for small-scale applications. Typically, the biofilm layer in GDMF systems enhances the removal of various contaminants, including organics and viruses. At the same time, the presence of the biofilm layer increases the resistance of to water flow, decreasing overall throughput. To increase the overall throughput, passive (gravity-driven) fouling controls, such as air scouring, and tank drain can be applied to GDMF systems. However, limited knowledge exists on the efficacy of these techniques. This present study addresses critical knowledge gaps in the literature regarding the effects of the passive fouling controls in gravity-driven membrane filtration systems; referred to as passive gravity-driven membrane filtration (PGDMF). The results of the study demonstrate that passive fouling controls significantly contribute to increased overall throughput. The study also concludes that passive fouling controls did not impact the chemical and biological properties of the biofilm layer developed on the membrane surface. The study also concludes that, after it is established at 1-2 weeks, the microbial community present on the biofilm layer is not similar to the microbial community present in the feed or reject of the PGDMF system. iv Lay summary Gravity-Driven Membrane Filtration (GDMF) is an economical and simple method for treating drinking water, particularly suitable for small and remote communities. This process utilizes gravity to push untreated water through membranes, producing clean drinking water. The method allows the formation of a biofilm layer on the membrane surface that aids in consuming retained organics, thus preventing material build-up. Passive Gravity-Driven Membrane Filtration (PGDMF), a modification of GDMF, utilizes gravity to induce hydraulic cleaning measures in the GDMF system, increasing water output. The present study demonstrates that the application of gravity-driven cleaning can successfully increase the water output in PGDMF system. The research demonstrates that, although the cleaning measures increase the water output, the chemical and biological properties of the biofilm layer are not impacted by these measures. The microorganisms in the biofilm layer of the PGDMF system are also distinct from those in the feed or reject of the PGDMF. v Preface This dissertation is original, unpublished, independent work by the author, Varshaa Kumaran. The beta diversity and EPS analyses were performed by Dr Leili Abkar. vi Contents Abstract ........................................................................................................................................................ iii Lay summary ............................................................................................................................................... iv Preface .......................................................................................................................................................... v Contents ....................................................................................................................................................... vi List of tables ................................................................................................................................................. ix List of figures ................................................................................................................................................ x List of equations ........................................................................................................................................... xi List of abbreviations ................................................................................................................................... xii Acknowledgements ..................................................................................................................................... xv Dedication .................................................................................................................................................. xvi Chapter 1: Introduction ................................................................................................................................. 1 1.1. Conventional membrane filtration ................................................................................................ 1 1.2. Gravity driven systems.................................................................................................................. 1 1.2.1 Gravity driven membrane filtration or GDMF ...................................................................... 2 1.2.2 Passive gravity driven membrane filtration or PGDMF ....................................................... 3 1.3. Differences between conventional membrane filtration, PGDMF and GDMF systems ............... 4 1.4. Characteristics of the microbial community in the active layer .................................................... 7 1.5. Knowledge gaps in the literature and hypotheses for the present study ....................................... 8 Chapter 2: Objectives .................................................................................................................................... 9 Chapter 3: Materials and Methods .............................................................................................................. 10 3.1. Description of system: ................................................................................................................ 10 3.2. Operation of the systems ............................................................................................................. 14 3.3.1 Feed water characteristics ................................................................................................... 14 3.3. Monitoring, sampling, and analysis ............................................................................................ 15 3.4.1 Monitoring .......................................................................................................................... 15 3.4.2 Sampling ............................................................................................................................. 16 vii 3.4.3 Analysis ............................................................................................................................... 18 3.4.4 Data analysis ....................................................................................................................... 20 Chapter 4: Results and Discussion .............................................................................................................. 21 4.1 Impact of passive fouling controls on the permeability and overall throughput ......................... 21 4.1.1 Permeability ........................................................................................................................ 21 4.1.2 Overall throughput .............................................................................................................. 25 4.1.3 Summary: Permeability and overall throughput of PGDMF and GDMF systems ............. 25 4.2 Effects of passive fouling control measures on the chemical and biological properties of the active layer .............................................................................................................................................. 26 4.2.1 Evolution of the active layer to steady state ........................................................................ 27 4.2.2 Magnitude of the parameters at steady state ....................................................................... 29 4.2.1 Summary: Effects of passive fouling controls on the properties of the active layer ........... 32 4.3 Comparison of microbial properties of feed, reject and the active layer of the PGDMF system 33 4.3.1 Two component beta diversity analysis .............................................................................. 33 4.3.2 Summary: Comparison of microbial properties of feed, reject and the active layer of the PGDMF system ................................................................................................................................... 34 Chapter 5: Conclusions & future work ....................................................................................................... 35 5.1 Conclusions ................................................................................................................................. 35 5.2 Future work ................................................................................................................................. 36 References ................................................................................................................................................... 37 Appendix A: Introduction ........................................................................................................................... 42 A.1. Hupacasath water treatment plant ............................................................................................... 42 Appendix B: Materials and methods ........................................................................................................... 43 B.1. Float switch interface .................................................................................................................. 43 B.2. Membrane flow cell modification ............................................................................................... 44 B.3. Air flow rate calculation ............................................................................................................. 44 B.4. Protocol for cleaning and clean water resistance tests for virgin membrane .............................. 45 viii B.5. Operational timelines .................................................................................................................. 46 B.6. Extracted active layer samples .................................................................................................... 47 Appendix C: Quality control and quality assurance ................................................................................... 48 C.1 Construction and operation ......................................................................................................... 48 C.2 Sample collection and storage..................................................................................................... 49 C.3 Data Analysis and Statistics ........................................................................................................ 50 Appendix D: Results ................................................................................................................................... 51 D.1 Throughput calculation ............................................................................................................... 51 D.2 FTIR of the active layer .............................................................................................................. 52 D.3 Mass balance of TOC .................................................................................................................. 53 D.4 SEM images ................................................................................................................................ 54 D.5 Properties of the permeate and reject .......................................................................................... 55 D.6 Correlation analysis .................................................................................................................... 56 ix List of tables Table 1: Differences in hydrodynamic conditions in conventional membrane, PGDMF and GDMF systems .......................................................................................................................................................... 5 Table 2: Operating parameters for PGDMF system .................................................................................... 14 Table 3: TOC of feed water for EI and E II ................................................................................................ 15 Table 4: Sampling frequency protocols....................................................................................................... 16 Table 5: Sample collection and storge protocols ......................................................................................... 17 Table 6: Summary of statistical tests on permeability ................................................................................ 22 Table 7: Summative parameters from the average permeability vs time plot ............................................. 24 Table 8: Impact of passive fouling controls on the properties of the active layer ...................................... 28 Table 9: Properties for the full-scale PGDMF plant ................................................................................... 42 Table 10: Different timelines of operation with dates ................................................................................. 46 Table 11: Summary of statistical tests comparing permeate of PGMDF and GDMF ................................. 55 x List of figures Figure 1: Illustrations depicting GDMF and PGDMF systems .................................................................... 3 Figure 2: Schematic of PGMDF and GDMF systems (Note: only one of each system is illustrated) ........ 12 Figure 3: Bench scale setup with different components of the system highlighted .................................... 13 Figure 4: Typical coupons cut from harvested membrane .......................................................................... 18 Figure 5: Permeability vs time .................................................................................................................... 22 Figure 6: Average permeability vs time of PGDMF and GDMF systems (Error bars corresponds to standard error) ............................................................................................................................................. 23 Figure 7: Chemical and biological properties of PGDMF and GDMF, including a) concentration of ATP; b) concentration of TOC; c & d) beta diversity of PC 1 & PC2 respectively; e & f) Relative FTIR at 3360 & 1640 cm-1 respectively; g) concentration of EPS; dotted line corresponds to linear fit of the data ........ 27 Figure 8: Differences in TOC in the active layer and TOC retained in GDMF and PGDMF ..................... 30 Figure 9: Correlation between cumulative differences in the permeability and mass of TOC retained in the active layer for PGDMF and GDMF system .............................................................................................. 31 Figure 10: Beta diversity plots of feed, reject and active layer of PGDMF system .................................... 33 Figure 11: From left: Membrane modules inside the plant, the outside view of the plant and storage reservoir ...................................................................................................................................................... 42 Figure 12: From top left: Float switch interface box, circuits inside the Float switch interface, controller for maintaining cyclic operation of the system, constant level float switch ............................................... 43 Figure 13: Modified and unmodified CF042 cells ...................................................................................... 44 Figure 14: Digital level measurements in the feed tank using a laser level ................................................ 48 Figure 15: Vent system on the flow cell with open to atmosphere arrangement ......................................... 49 Figure 16: Full spectra of absorbance vs wavenumber of various operational timelines ........................... 52 Figure 17: Cumulative TOC vs time (a: PGDMF, b:GDMF) ..................................................................... 53 Figure 18: Illustrative SEM images of active layer taken on 4 days and 10 weeks of operation ............... 54 Figure 19: Properties of permeate over time ............................................................................................... 55 Figure 20: Properties of reject over time (a: ATP, b: TOC, c & d: PC1 and PC2 respectively) .................. 56 Figure 21: Correlation plots of active layer vs reject (a: TOC, b: ATP) ...................................................... 57 Figure 22: Beta diversity correlation between reject and the active layer .................................................. 57 xi List of equations Equation 1 ................................................................................................................................................... 15 Equation 2 ................................................................................................................................................... 15 Equation 3 ................................................................................................................................................... 15 Equation 4 ................................................................................................................................................... 16 Equation 5 ................................................................................................................................................... 23 Equation 6 ................................................................................................................................................... 51 xii List of abbreviations A Maximum extent of initial rapid decline ATP Adenosine Tri Phosphate ATR Attenuated Total Reflectance B Maximum extent of sustained decline CF Cross flow cell DNA Deoxyribonucleic Acid DSA Direct Surface Analysis DOC Dissolved Organic Carbon EPS Extra-Polymeric Substances FTIR Fourier Transform Infrared Spectroscopy GDMF Gravity Driven Membrane Filtration HFW Horizontal Field Width J Flux K1 Rate of initial rapid decline K2 Rate of sustained decline N/A Not applicable NPOC Non-Purgeable Organic Carbon NTU Nephelometric Turbidity Unit Pa Atmospheric pressure PBS Phosphate Buffer Saline PC Principle Component PGDMF Passive Gravity Driven Membrane Filtration PP Polypropylene Po Power required xiii Pc Air pressure at point of discharge ppm Parts per million PVDF Polyvinylidene Fluoride Q Overall throughput over the operating period QA/QC Quality Control and Quality Assurance QGDMF Overall throughput of the GDMF system QPGDMF Overall throughput of the PGDMF system rRNA Ribosomal ribonucleic acid RLU Relative Light Units S.D. Standard Deviation S.E. Standard Error SEM Scanning Electron Microscopy UB70 Flat sheet UF membranes used in the study (Sterlitech) TOC Total Organic Carbon TOCin TOC input (TOC in feed) TOCout TOC output (TOC in permeate (and reject for PGDMF)) TMP Trans Membrane Pressure UF Ultrafiltration U.S.EPA United States Environmental Protection Agency UN-SDGs United Nations Sustainable Developmental Goals UVA Ultraviolet Absorbance V Aeration vol Va Vol of air µ Dynamic viscosity of water V5 Type of flat sheet membrane (Sterlitech) xiv V7 Type of flat sheet membrane (Sterlitech) ZW1500 ZeeWeed® 1500 membrane module (SUEZ) xv Acknowledgements Firstly, I would like to wholeheartedly thank my supervisor, Dr. Pierre Bérubé, for consistent support, patience, and encouragement to strive for greater achievements, I am super grateful to receive his mentorship. I would also like to thank Dr. Sara Beck, my co-supervisor for always cheering me up and setting a great example. I would like to thank Dr. Leili Abkar for performing beta diversity analysis and being a super fun colleague to work with. I would like to thank the faculty, staff, and my co-grad students at the Department of Civil Engineering and at UBC, who have been super supportive, encouraging and for the fun chitchats in the grad lounge. Special thanks to Felix Shuen, Brad Wishard and other lab and workshop managers for all the help, support, and endless guidance. To my friends and family, a sincere thank you for being my pillars of strength during the challenges of thesis work. The unwavering moral and emotional support I received have been crucial, and I feel truly fortunate to have such a supportive network as I navigate this academic venture. xvi Dedication Dedicated to Almighty, Engal Kula Deivam “Meivalzhi Salai Andavargal”, Guru Vazhga! Guruve Thunai! 1 Chapter 1: Introduction On a global scale, approximately 14% of urban population and 40% of rural population do not have access to safe drinking water (WHO-Joint Monitoring Program, 2020). This is equivalent to a total population of 2.2 billion. The higher percentage of rural population without access to safe drinking water compared to urban population is largely because of the limited access to technical and financial resources for effective water treatment. To address the limited availability of safe drinking water in rural settings, simple and low-cost treatment technologies are required. One of the United Nations Sustainable Development Goals (UN-SDGs) is to provide universal access to clean water by 2030 (UN-SDGs, 2023). Ensuring access to safe drinking water for everyone requires that appropriate treatment be implemented to make the available water safe for consumption. Traditional water treatment approaches, such as coagulation, flocculation, and sedimentation, are relatively complex, require the use of multiple treatment chemicals and processes (US EPA, 2005). There is a growing interest in exploring simpler approaches for drinking water treatment. Membrane filtration is emerging as a promising solution. It is gaining attention because of its, efficacy in removing particulate contaminants such as pathogens, low environmental impact, smaller footprint and versatility (Crittenden et al., 2012). 1.1. Conventional membrane filtration Ultrafiltration (UF), hereafter referred to as membrane filtration, is a physiochemical separation process that uses size exclusion as a separation mechanism to filter substances over a semi-permeable membrane. (Crittenden et al., 2012). A pressure differential across the membrane which the driving force for the permeation of water through the membrane. During filtration, particles larger than the pore of the membrane, and certain dissolved substances are retained. The retained material can accumulate on the membrane surface and within the membrane pores, a phenomenon commonly referred to as fouling. This accumulation increases the resistance of the membrane to permeate flow. Typically for conventional membrane filtration, fouling can be controlled by employing a variety of methods depending on the type of fouling, including permeate flow interruption (period of no filtration), backwashing (reversal of water flow), air scouring (introducing air bubbles) and chemical cleaning (soaking or permeating chemicals through the membrane). A combination of these methods is typically used for effective fouling control. 1.2. Gravity driven systems While conventional membrane filtration is an effective technology for water treatment, it is complex and costly to operate (Peter-Varbanets et al., 2010). The complexity and cost for operation, mainly comes from 2 the mechanical and chemical systems needed for permeation, hydraulic cleaning (e.g. backwash, air scouring) and chemical cleaning (Table 1). Gravity Driven Membrane Filtration (GDMF) systems have emerged as a simple alternative to conventional systems, especially for small scale application in rural areas. GDMF systems offer the advantages of conventional membrane filtration without the need for complex mechanical systems. 1.2.1 Gravity driven membrane filtration or GDMF GDMF utilizes gravity to provide hydrostatic pressure as the driving head for permeation (Figure 1). Fouling control measures such as backwashing or chemical cleaning procedures are not applied. The use of gravity to provide the driving force for permeation and the absence of fouling control makes the operation of GDMF simple and low cost. In the absence of fouling control measures, a fouling layer forms on the membrane surface. When operated with a relatively low hydrostatic pressure, and in the absence of chemical cleaning, a biological community establishes itself in the foulant layer. Typically, the formation of a foulant layer on membrane is considered detrimental; however, the biological community has been reported to contribute to the formation of a porous foulant layer resulting in stabilizing the water flow through the membrane (Peter-Varbanets et al., 2010). Furthermore, the presence of high-level organisms, such as protozoans and metazoans in the foulant layer, have been reported to contribute to the formation of a porous foulant layer in GDMF (Derlon et al., 2013). Because of the above-mentioned contribution of the microbial community to the structure and composition of the foulant layer, material accumulated on the membrane surface will henceforth be referred to as the active layer. The active layer has also been reported to degrade material that would otherwise contribute to the resistance to the permeate flow (Derlon et al., 2014) as well as organic material that could be of concern in the permeate water quality (Chomiak et al., 2015). In addition to removing organics, the active layer has been reported to contribute to the removal of pathogens and in particular viruses, which are not effectively removed in conventional membrane systems. (Tobias & Bérubé, 2020). Moreover, to maintain a porous active layer, a relatively low hydrostatic pressure must be maintained. Combined with the relatively high resistance of the foulant layer in GDMF systems, the low hydrostatic pressure, results in a relatively low permeate flow in GDMF systems. It is important to note that even though the GDMF system serves as a good alternative to conventional UF filtration, the overall throughput of the GDMF system is low, independent of the applied hydrostatic pressure for permeation (Peter-Varbanets et al., 2010). 3 Figure 1: Illustrations depicting GDMF and PGDMF systems 1.2.2 Passive gravity driven membrane filtration or PGDMF One of the approaches to increase the throughput in GDMF system, is to reduce the resistance to water flow by partially removing the active layer accumulated on the membrane. The accumulation of material in the active layer can be controlled by applying appropriate fouling-control measures. Introducing fouling-control measures have been considered to increase the throughput in GDMF. In particular, permeate flow interruption, backwashing and air scouring are of interest because fouling-control measures could be applied passively, that is, without introducing any mechanical complexity (The University of British Columbia, 2021). The (passive) driving force needed for backwashing and air scoring can be generated by introducing a tank drain. Scouring of the active layer occurs during the tank drain when a vacuum is generated within the Gravity driven membrane filtration system (GDMF) Passive gravity driven membrane filtration system (PGDMF) - Permeation Air Reject Reject Passive gravity driven membrane filtration system (PGDMF) - Passive cleaning Air 4 membrane module (The University of British Columbia, 2021). Similar to air being drawn into an inverted water bottle that is filled with water, air enters the membrane module when it is draining. The rising air bubbles induce shear force at or near the membrane surface, detaching some of the accumulated active layer. During draining, some of the permeate is also drawn through the membrane from the permeate to the feed side, resulting in a simultaneous backwash. The detached material from the active layer is removed from the membrane system with the drained water as reject (Oka et al., 2017). The above-described process is essentially gravity driven, aerated tank drain and backwash, but at a lower intensity than that typical of conventional UF filtration. The overall throughput in GDMF system with passive fouling control, hereafter referred to as PGDMF system (Refer to Figure 1), could be substantially greater than that can be achieved in GDMF system (Wei, 2023). A recent study demonstrated that the permeate throughput decreased 3-5 times faster for a GDMF system compared to a PGDMF system, demonstrating the efficacy of passive fouling controls (Wei, 2023) (Refer to Appendix A.1 for more details). However, the operation of the GDMF system and PGDMF system during that recent study was sequential (Wei, 2023) and therefore the observed differences could have been impacted by seasonal variability in water quality. Also, the GDMF systems were only operated for a short time (25 to 50 days). Therefore, there is a need to investigate the parallel operation of GDMF and PGDMF systems. 1.3. Differences between conventional membrane filtration, PGDMF and GDMF systems Typical operating parameters of interest for conventional membrane filtration, PGDMF and GDMF systems are summarized in Table 1. A PGDMF system has hydraulic (passive) fouling control measures, which are typical of conventional membrane filtration, meanwhile still maintaining the simplicity of GDMF in terms of operation and maintenance. A conventional UF filtration is typically operated with a typical hydraulic head between 1500 – 2800 mbar (Veolia, 2022). This is usually provided by mechanical equipment such as pumps, while the PGDMF and the GDMF systems operate under hydrostatic pressure typically between 35 to 50 mbar using gravity (Wei, 2023). For fouling controls, a conventional membrane system typically undergoes mechanical air scouring with air flow rates up to 85 L/min (ZW1500 membrane module), while the PGDMF system undergoes gravity driven aeration with an air flow rate between 5-10 L/min (Wei, 2023). Backwashing followed by a train 5 drain is performed on a conventional UF filtration at the same pressure as permeation, while backwash and drain in PGDMF system is performed using gravity. Typically, no chemical cleaning is performed on either in PGDMF or GDMF system while periodic chemical cleaning is performed on a conventional UF filtration (Pronk et al., 2019). Table 1: Differences in hydrodynamic conditions in conventional membrane, PGDMF and GDMF systems Parameter of interest Conventional membrane system1 PGDMF system2 GDMF system2 Typical Permeate flow 30 to 170 Lm-2h-1 3 -5 Lm-2h-1 1 - 20 Lm-2h-1 Operation Cyclic operation typically ~ 1hr cycle of permeation, aerated backwash, and tank drain Cyclic operation typically 12 - 24hrs of permeation, permeate flow interruption, aerated backwash, and tank drain Continuous permeation Typical hydraulic head for permeation ~ 1500 - 2800 mbar 35 - 50 mbar 35 - 50 mbar Air scouring Mechanical Gravity driven N/A Air flow rate for scouring Up to 85 L/min (For ZW1500) 5 - 10 L/min Typical hydraulic head for backwash ~ 1500 - 2800 mbar 35 - 150 mbar Permeate flow interruption N/A 55 mins Tank drain At the end of every cycle (Typically, every hour) At the end of every cycle (Typically, once or twice per day) Chemical cleaning Mild cleaning every few days to weeks Extensive cleaning every few weeks to months N/A 1 (US EPA, 2006) (Crittenden et al., 2012) (Veolia, 2022) 2(Wei, 2023) (Pronk et al., 2019) (Oka, 2015) (Tobias Costa de Oliveira, 2019) 6 As outlined in the Table 1, there are considerable differences in the hydrodynamic conditions present in PGDMF and GDMF systems. The cross flow created by backwash, the scouring effects created by aeration, backwash, and drain, all influence the hydrodynamic conditions and therefore could affect the development of the active layer. A literature review indicated that the active layer developed on a surface consists of three distinct sub-layers (Coufort et al., 2007): 1) A top sub-layer that is characterised as fragile and easily detached. 2) A middle sub-layer with intermediate characteristics between easily detached and resistant to shear. 3) A bottom sub-layer that is characterised as cohesive and resistant to shear forces. Changes in the shear stresses could impact the types of sub-layers developed inside a GDMF system. The literature provides evidence regarding the influence of hydrodynamic conditions on the properties of the active layer within the system, which might influence the overall system throughput. Some of the evidence from relevant studies are summarized below. The impact of differential shear stresses on the thickness of the active layer was studied for a GDMF system treating grey water (Ding et al., 2016) and membrane bioreactor with wastewater (Celmer et al., 2008). It was demonstrated that an increase in shear stress, due to cross flow, developed a thinner and denser biofilm, resulting in lower permeate throughput. In a study, using tertiary wastewater treatment with RO (Reverse Osmosis) membranes, it was reported that microbial communities under high shear stress produce high amounts of EPS (extracellular polymeric substances), resulting in lower permeate throughput (Al Ashhab et al., 2014). The above-mentioned studies suggest that an increase in shear stress (i.e., applying shear forces for fouling control) negatively impacts overall throughput of the membrane. Meanwhile, in a study conducted by Oka et al., (2017), a daily 1-hour period of permeate flow interruption followed by a few minutes of air scouring, achieved a significant recovery in overall throughput. The differences in hydrodynamic conditions between PGDMF and GDMF systems, could potentially influence the characteristics of the active layer. The active layer developed in PGDMF and GDMF systems may exhibit distinct properties, thereby potentially affecting the overall throughput of each type of system. Understanding these differences is essential to optimize the performance of PGDMF systems. Therefore, there is a need to investigate the impact of hydrodynamic conditions on the properties of the active layer for PGDMF and GDMF systems. 7 1.4. Characteristics of the microbial community in the active layer As previously discussed, the active layer has been reported to be of great importance for gravity driven membrane systems (See section 1.2.1); however limited research has focussed on the impact of the passive fouling controls on the characteristics of the microbial community present in the active layer of the gravity driven systems. A study by Wu et al., (2019) demonstrated that the microbial composition of the communities on the active layer of GDMF system is influenced by the amount of organics in the feed water. Another study by Derlon et al., (2012) concluded that external eukaryotic predation of the microbial community of the active layer influences the permeate throughput of GDMF systems by changing the composition of microbial community of the active layer. Klein et al., (2016) reported a similar outcome for nematode predation. However, the above-mentioned studies focussed on characterizing the microbial communities in GDMF (i.e., without passive fouling control). Hence, the effects of passive fouling controls on the composition of the microbial community in the active layer have not been explored. Wei et al., (2024)1 and Bereschenko et al., (2008) monitored the microbial communities in the active layer of the PGDMF system and GDMF system, respectively, by analysing the diversity of the active layer by sequencing the 16S rRNA gene of the microbial community. The diversity analysis revealed that the microbial communities observed in the active layer were distinct from that of the feed water (Bereschenko et al., 2008; Wei et al., (2024)1 or the reject . The monitoring of the microbial communities in the feed or the reject was of interest because, access to the active layer in the full scale PGDMF or GDMF system may not be readily feasible. During full scale operation, the active layer present inside the PGDMF system is inaccessible for investigation. Therefore, there is an interest in investigating whether monitoring other parameters could provide insights into the properties of the inaccessible active layer. To guide future research, it is important to further access, if the properties of the microbial communities on the active layer could be estimated by studying the properties of the feed or the reject from the PGDMF systems. 1 In progress 8 1.5. Knowledge gaps in the literature and hypotheses for the present study In the previous sections, a number of knowledge gaps regarding the operation and properties of PGDMF and GDMF system were identified. These can be summarized as follows. Knowledge gap 1: Do passive fouling controls in PGDMF result in higher overall throughput compared to that in GDMF ? No studies have compared the overall throughput of PGDMF and GDMF systems in parallel. - The study conducted by Wei, (2023) compared the throughput of PGDMF and GDMF sequentially, but not in parallel. Knowledge gap 2: Do the differences in hydrodynamic conditions induced by passive fouling controls impact the physical, chemical, and biological properties of the active layer, potentially resulting in distinct active layers in PGDMF and GDMF systems? - No studies, to date, have compared the active layers developed inside PGDMF and GDMF systems. Knowledge gap 3: The active layer in the full scale PGDMF systems cannot be readily accessed. As a result, it is difficult to characterize the microbial community in the active layer of a full-scale systems. Could the monitoring of the microbial communities in the feed or the reject of the PGDMF system, be used as a surrogate for the microbial communities in the active layer? - The study conducted by Wei (2023) compared the characteristics of the microbial communities in the feed and reject to that of the active layer for only one isolated sampling event. Based on the above knowledge gaps identified, the following research questions were formulated. • Do passive fouling controls increase the throughput of gravity driven membrane systems? • Do passive fouling controls affect the chemical and biological properties (such as organic carbon content, chemical composition, microbial diversity, microbial activity, etc.) of the active layer in the gravity driven membrane systems? • Is the microbial community in the active layer of a PGDMF system similar to those of the feed or the reject? 9 Chapter 2: Objectives Objective 1: To quantify the benefit of passive fouling controls on permeability and cumulative throughput over time. - This is achieved by comparing the permeability and the overall throughput of PGMDF and GDMF systems. Objective 2: To quantify the effects of passive fouling controls on the chemical and biological properties of the active layer. - This is achieved by comparing the chemical and biological properties of active layer in PGDMF and GDMF systems. Objective 3: To investigate if the microbial community in the feed or the reject can be used to provide insights into the microbial community in the active layer - This is achieved by comparing the microbial properties of feed, reject and the active layer 10 Chapter 3: Materials and Methods 3.1. Description of system: The bench scale apparatus consists of a feed system, a filtration system, a permeate system, an aeration system, a vent system, a drain system, and a control system(see Figure 2 and Figure 3). Feed system: Consists of a main tank and a feed tank. The main tank (6 gallons) is manually filled as necessary. Water flows from main tank to the feed tank (2 gallons) by gravity. The liquid level in the feed tank is constant. The constant level in the feed tank is maintained by a custom-made float switch interface (Refer to Appendix B.1 for mechanism). The feed tank serves as the supply for both the PGDMF and GDMF systems. The flow to the PGDMF system is controlled by a normally open ball valve (Easy-Maintenance Motor-Driven On/Off Valves (5241T21)- McMaster-Carr®)(This is absent in the GDMF system). The driving force for permeation is provided by maintaining a water head of about 35cm (35mbar) from the top of the “T” connection in the permeate line (Refer to Appendix C.1 for images) to the top of water level in the constant level feed tank. (Refer to Figure 2). Filtration system: Consists of six modified commercially available filtration cells (CF-042 cell assembly- Sterlitech®). The modifications are as follows. - The crossflow cavity in the cell was increased from 1.5 mm to approximately 15 mm in depth. - Two ports were added at the bottom and top of the crossflow cavity, one for aeration (i.e., at the bottom) one for venting (i.e. at the top). Note that for the operation as GDMF, both these ports are plugged. Visual references of these modifications are provided in Appendix B.2. Permeate system: The permeate is collected from each filtration cells and conveyed to storage containers located inside a refrigerator (Mini Fridge (NS-CF26BK8)-Insignia) at 4C. Aeration system: Air is added to the port at the bottom of the PGDMF filtration cell. A pressure regulator (ARO Modular Compressed Air Regulators- McMaster-Carr®) and an air flow meter (Oxygen Air Flow Meter (B07WZ17QVR) -BTIHCEUOT) control the pressure of air flow to the filtration cell. The flow of air is controlled by a normally closed solenoid valve (Brass Body Solenoid On/Off Valve (4711K513)-McMaster-Carr®). The air is added at a flow rate of 1 L/min and at a pressure of 2 psi. Vent system: The vent system enables air or water to be added or drained, to and from each filtration cell, respectively. The vent system is connected to the port of the top of the PGDMF filtration cell. 11 Drain system: The reject collected from the PGDMF filtration cell is conveyed to storage containers located inside a refrigerator (Mini Fridge (NS-CF26BK8)-Insignia) at 4C. The flow of reject is controlled by a normally closed ball valve (Easy-Maintenance Motor-Driven On/Off Valve (5241T22) - McMaster-Carr®). Control system: Consists of a programable timer (XT series timer - Chrontrol®) that controls the opening and closing of various automated values installed in the feed, the aeration, and the drain systems to ensure cyclic operation. The membranes used were flat sheet, (UB70 PVDF- Trisep®) UF membranes with a pore size of 0.03 microns. The virgin membranes were cleaned, and their clean water resistance was determined using DI (Millipore) water as feed. The protocol used for cleaning and determining the clean water resistance is outlined in Appendix B.4. Note: • Manual valves illustrated in Figure 2 were only used for system maintenance and were normally open. • For operation as GDMF, the aeration system, the vent system, the drain system, and the control system are not present. • In order to prevent algae growth due to light exposure, both the feed tanks and tubes emerging from it, are covered with opaque material. 12 Figure 2: Schematic of PGMDF and GDMF systems (Note: only one of each system is illustrated)13 Figure 3: Bench scale setup with different components of the system highlighted Main tank Feed tank Float switch interface Feed Valve Controller Main feed Valve Drain Valve Permeate Flow cells Pressure gauge Funnel shaped vent 14 3.2. Operation of the systems The PGDMF systems were operated in a cyclic manner as summarized in Table 2. The permeation, permeate flow interruption, aeration and drain operations are controlled by automated valves. The valves are triggered by the control system. The GDMF systems were operated in continuous dead-end filtration mode. Both the PGDMF and GDMF systems were operated during two replicate experiments: Experiment I (E I) occurred from October 11, 2022, to December 20, 2022, and Experiment II (E II) occurred from March 6, 2023, to May 16, 2023. Six separate flow cells were simultaneously operated, with three functioning as PGDMF and the remaining three as GDMF. The sequence of operation of the three GDMF and the three PGDMF systems enabled consideration of multiple durations of 4 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 6 weeks, and 10 weeks (See Appendix B.5 for details). Each set of three, PGDMF and GDMF systems shared identical designs and operational protocols, differing only in the duration during which they were operated. Table 2: Operating parameters for PGDMF system Operating parameters Value* Operation of valves Permeation duration 23 hrs Feed valve open, air and drain valve closed Permeate flow interruption 55 mins All valves closed Aeration duration 2 mins Air valve open, feed & drain valve closed, Drain duration 3 mins Drain valve open, feed & air valve closed, *selected to mimic the operation of the full scale PGDMF system (Wei, 2023) 3.3.1 Feed water characteristics The raw water for the system was collected from Spanish Bank Creeks, Vancouver, Canada. A single batch of raw water was collected for each of experiments I and II and stored in dark at 4C until used. The Total Organic Carbon (TOC) of the water was monitored and diluted with DI (Millipore) water to achieve a feedwater concentration of 2.5 mg/L TOC before use. Table 3 summarizes the TOC of the raw and feed water for experiments I and II. The raw water for experiments I and II were obtained from the same source, but on different dates. 15 Table 3: TOC of feed water for EI and E II Spanish bank creeks water (Raw water) Diluted feed water Experiment I 3.1 ± 0.03 2.5 ± 0.56 Experiment II 3.5 ± 0.13 2.4 ± 0.42 Note: Values presented are average of TOC in mg/L ± standard deviation 3.3. Monitoring, sampling, and analysis 3.4.1 Monitoring 3.4.1.1. Temperature The temperature measurements were taken from the feed tank directly. The standard methods highlighted in 2550 A, B (Baird, R., & Bridgewater, L., 2013) were used as reference. A mercury thermometer (Laboratory Grade Spirit Thermometers-Fisherbrand™) with 0.1C least count was used to measure temperature by partial immersion in the feed water tank. 3.4.1.2. Flux and permeability The flow measurements were taken by continuously collecting the permeate sample and measuring the collected weight every 24 hrs using a digital scale (Compact Portable Top loading Balance (UZ-11000-28) - Cole-Parmer®). The Flux (J) through the system was calculated as outlined in Equation 1, assuming the density of the water is 1g/ml (Equation 2.1 from US EPA, (2005)). Flux (m/s)= Weight of the colleted sample for �low measurement (g) ∗ (m3)area of the membrane (m2) ∗ 1000 ∗ 1000 (g) ∗ Time elapsed between �low measurements (s) Equation 1 The Permeability (P) of the membrane was calculated from flux, TMP (Trans-membrane pressure), and viscosity (Equation 2.7, US EPA, (2005), J = TMPR ∗ μ Equation 2 P = 1R Equation 3 16 P = J ∗ μ TMP Equation 4 where, J is flux at temperature 23C (m/s); TMP is the transmembrane pressure (Pa); R is the total membrane resistance (m-1); P is the permeability (m); and μ is the viscosity of water at 23C (Pa.s) TMP is defined as the difference between the height of water in the feed tank and the vent on the permeate line. The monitoring of temperature and flow was done daily throughout the duration of the study. 3.4.2 Sampling 3.4.2.1. Sampling frequency Table 4 summarizes the sampling frequency and nature of the sample collected. The collection and storge protocols were adapted from 1060 A. B. C. (Baird, R., & Bridgewater, L., 2013) (See Appendix C.2 for QA/QC). Table 4: Sampling frequency protocols Analysis performed Frequency of sampling Performed on TOC & DOC 3 per week in first week, 2 per week in weeks 2 - 4 , 1 per week afterwards Feed, permeate, reject and active layer UVA Feed, permeate, reject ATP quantification End of each timeline Feed, permeate, reject and active layer FTIR analysis Active layer SEM imaging Active layer Beta diversity analysis Feed, permeate, reject and active layer EPS quantification Active layer TOC & DOC : Total Organic Carbon and Dissolved Organic Carbon UVA : Ultraviolet Absorbance at 254 nm. ATP : Adenosine Triphosphate FTIR : Fourier Transform Infrared Spectroscopy SEM : Scanning Electron Microscope EPS : Extra Polymeric Substances 3.4.2.2. Sample handling: Feed, permeate and reject samples: The samples were collected and analyzed immediately or stored at 4C in clean/sterile containers until analysis. Refer to Table 5 for details. 17 Active layer samples: The membranes with active layer samples were harvested from the filtration cells and cut into coupons (as illustrated in the Figure 4) using a sterile membrane cutter (12" Paper Trimmer - X-Acto®). Two types of active layer samples were prepared. o Attached active layer samples: The coupons were either analyzed immediately or dried at 50C overnight in an oven, depending on the type of analysis performed. o Extracted active layer samples: The active layer present on the coupons were scraped off into a known volume of PBS solution (Dulbecco′s Phosphate Buffered Saline - Sigma-Aldrich®) and made into a uniform solution by sonication, following the protocol outlined in Appendix B.5. Table 5: Sample collection and storge protocols Analysis performed Collection and storage Feed, permeate and reject samples Active layer samples TOC & DOC Collected directly into 40ml amber vials (baked at 450C) and stored in the dark at 4C Extracted active layer samples were collected and stored in 50 ml amber vials (Cole-Parmer®) in the dark at 4C, UVA Collected in polypropylene 1L bottles (VWR®) and stored in the dark at 4C N/A ATP quantification Collected and analysed immediately Attached active layer samples were analyzed immediately FTIR analysis N/A Attached active layer samples were stored in a sterile petri dish (VWR®) in the dark at room temperature SEM imaging N/A Attached active layer samples were analyzed immediately Beta diversity analysis Collected directly into sterile 15 ml falcon tubes (VWR®) and stored in the dark at -80C Attached active layer samples were collected and stored in sterile 15 ml falcon tubes (VWR®) and stored in the dark at -80C EPS quantification N/A Attached active layer samples were collected and stored in sterile 15 ml falcon tubes (VWR®) containing PBS solution at 4C in the dark, 18 Figure 4: Typical coupons cut from harvested membrane Note: For microbial analyses, the samples were collected directly from the respective feed/drain systems for analysis. 3.4.3 Analysis 3.4.3.1 TOC & DOC TOC and DOC concentrations were measured as outlined in Standard methods 5310 A , B (Baird, R., & Bridgewater, L., 2013). High Temperature combustion method using a TOC-L-CPH (combustion catalytic oxidation method) high sensitivity model (with Autosampler ASL) from Shimadzu was used for analysis. NPOC (Non-Purgeable Organic Carbon) method, calibrated with standards was used. For DOC measurements, filtrate from a 0.45μm filter (PES filter- Supor® Pall Corporation) was used for analysis. The concentration of the TOC obtained for the extracted active layer samples are converted to mg/cm2 using the area of the coupon from which the active layer solution was extracted. The TOC of the feed, permeate and reject samples are expressed in mg/L. 3.4.3.2 UVA UVA was measured as outlined in standard methods 5910 B (Baird, R., & Bridgewater, L., 2013). The analysis was performed using a UV-Spectrophotometer (UV-300 Spectrometer - Spectronic Unicam). A light path length of 1 cm with matched quartz cuvettes at a wavelength of 253.7 nm was used for analysis. The samples were filtered with 0.45μm filter (PES filter- Supor® Pall Corporation) before analysis. 3.4.3.3 ATP ATP concentration was measured using a luminometer (PhotonMaster™ - LuminUltra®) as RLU (Relative Light Units) present in the sample. For feed, permeate and reject samples, QGO-M (Quench Gone™ Organic Modified) test kit was used, following protocol outlined in LuminUltra QGA, (2023) and for attached active layer samples, DSA (Direct & Surface Analysis) test kit was used, following protocol outlined in LuminUltra DSA, (2022). Background RLU and standards were used to adjust the obtained RLU values, according to the protocol highlighted in the user manual for each type of sample. A simple conversion protocol using myLuminUltra™ software converts RLU to pg/ml. 19 The concentration of the ATP obtained for the attached active layer samples are converted to pg/coupon using the area of the coupon from which the active layer was harvested. The ATP of the feed, permeate and reject samples are expressed in pg/L. 3.4.3.4 FTIR analysis FTIR analysis was performed as outlined in (Robinson & Bérubé, 2021). FTIR (Nicolet™ iS50 FTIR-Thermofisher) was used in ATR (Attenuated Total Reflectance) mode for analysis. The FTIR analysis explores the chemical composition and biological products (such as amino acids) of the active layer. The obtained spectra are corrected for background noise by subtracting the background spectrum from the original spectra. Power BI software was used to normalize the obtained spectra using the spectrum of the virgin membrane to obtain a differential FTIR spectra, from 400 – 4000 cm-1 wavenumbers. This is referred to as the Δ FTIR hereafter. (Refer to Appendix D3 for the full spectra). According to Schmitt & Flemming (1994), the region between 1700-1470 cm-1 represents the protein fraction (amino acids) and a wide peak between 3000-3750 cm-1 represents products from various bacterial strains (Gieroba et al., 2020). Plotting the Δ FTIR against wavenumber revealed peaks near 1640 cm-1 and 3360 cm-1. Therefore, the maximum (peak) absorbance value observed for peaks near wavenumbers 1640 cm-1 and 3360 cm-1 were considered for analysis. 3.4.3.5 SEM imaging SEM analysis was performed using the procedure outlined in Miura et al., (2007) for fixing the samples. The samples were fixed, dried using a critical point dryer (Critical point dryers- Tousimis Samdri®-795), sputter coated with 9-10 nm thick iridium coating (EM Coating System (MED020) - Leica) and images were captured with SEM (Focused ion beam SEM – Helios NanoLab™ 650). The images were captured primarily in SE (Secondary Electron) mode with an accelerating voltage of 1kV, working distance between 4 to 4.5 mm and the Horizontal Field Width (HFW) was set to 15 µm. 3.4.3.6 Beta diversity analysis Beta diversity analysis of the genomics data was performed using the protocol outlined in (Abkar et al., 2023). For beta diversity analysis of feed, permeate and reject, samples were vacuum filtered through 0.22 µm filter (PVDF filter - VWR®). The filtered samples and the attached active layer samples were sent to the Microbiome Insights lab for DNA extraction and 16S rRNA sequencing. Principal Component (PC) analysis is used for visualizing the beta diversity among the different species present in a given sample. 20 3.4.3.7 EPS quantification EPS quantification was done using the colorimetry method of protein assay as outlined in BCA Protein assay kit manual (BCA protein essay kit - Thermofisher Pierce™). The concentration the protein was expressed in µg/sq.cm of the coupon using the area of the coupon from which the active layer was harvested. 3.4.4 Data analysis Steady state conditions of the active layer: Linear regression was used to assess when steady state conditions were reached. Steady state conditions were considered to have been reached, when the slope of the regression was zero (based on 95% confidence interval). This was done by plotting the data with respect to time from the last day of operation and accessing the time at which the data points no longer fit a linear relationship. The following steps were carried out. 1. Assessing if the data could adequately be described by a linear relationship. 2. Identifying the time at which the data could no longer be described as a linear relationship. 3. Assessing if the slope of the linear relationship was not greater than zero with 95% confidence interval (i.e., true steady state conditions were achieved). The time corresponding to when the properties could no longer be described by a linear relationship, was defined as the time to reach steady state. 21 Chapter 4: Results and Discussion Section 4.1 quantifies the impact of passive fouling controls on permeability and cumulative throughput, by comparing the permeabilities and the cumulative throughput of PGMDF and GDMF systems. Section 4.2 quantifies the effects of passive fouling controls on the chemical and biological properties of the active layer by comparing the properties of active layer in a PGDMF and GDMF system. Section 4.3 investigates if the microbial communities in the feed or the reject provide insights on the microbial community in the active layer of the PGDMF by comparing the microbial properties of feed, reject and the active layer. 4.1 Impact of passive fouling controls on the permeability and overall throughput 4.1.1 Permeability Figure 5 illustrates permeabilities recorded during different timelines within the same experiment. For each experiment, the results representing different timelines can be considered as replicates of different durations. As observed from Figure 5, for both the GDMF and the PGDMF systems, the permeability initially declined rapidly, followed by a period of slower sustained decline. An initial fast decline followed by a slower sustained decline has also been observed by other researchers (Ding et al., 2017; Peter-Varbanets et al., 2010). The initial rate of decline of permeability of the GDMF system appears to be greater than that of the PGDMF system. Similarly, the sustained rate of decline in the permeability of the GDMF system appears to be greater than that of the PGDMF system. Also, apparently, greater variability in the permeability is observed for the GDMF system compared to the PGDMF system. Paired t tests were performed comparing the permeability for different timelines within each experiment for both the GDMF and the PGDMF system. The tests demonstrated that the results for each timeline within an experiment were similar for both GDMF and PGDMF systems. Paired t tests were also performed comparing the permeability observed during experiments I and II, for both the PGDMF and GDMF system (Table 6). The tests demonstrated that the permeabilities were similar for both experiments for the PGDMF and the GDMF systems. For these reasons, the permeability results for all the timelines for both the experiments were combined to compare permeabilities for both the GDMF and the PGDMF systems. The results from the combined averaged permeability for both PGDMF and GDMF systems is presented in Figure 6. 22 Figure 5: Permeability vs time a) PGDMF; b) GDMF; results presented for all timelines and experiments 1 and 2 (E I & E II respectively)) Table 6: Summary of statistical tests on permeability Permeabilities compared Type of test Confidence interval P value Are the differences in permeability statistically significant? PGDMF E I vs E II Paired two sample t test 95% 0.96 > 0.05 No GDMF E I vs E II Paired two sample t test 95% 0.053 ≥ 0.05 No 0.00E+005.00E-131.00E-121.50E-122.00E-120 10 20 30 40 50 60 70Permeability (m)Time (days)a: E II - 10W E II - 6W E II - 4W E II - 3W E II - 2W E II - 1W E II - 4DE I - 4D E I - 1W E I - 2W E I - 3W E I - 4W E I - 6W E I - 10W0.00E+005.00E-131.00E-121.50E-122.00E-120 10 20 30 40 50 60 70Permeability (m)Time (days)b:E II - 10W E II - 6W E II - 4W E II - 3W E II - 2W E II - 1W E II - 4DE I - 4D E I - 1W E I - 2W E I - 3W E I - 4W E I - 6W E I - 10W23 Figure 6 illustrates the average permeability for PGDMF and GDMF systems plotted with respect to time. Each data point corresponds to the average from the relevant timelines, for both experiments ± the 95% confidence interval of the estimated average. The average permeability vs time for both the PGDMF and GDMF systems could be modelled with a simple exponential relationship, describing the initial rapid decline, combined with a linear relationship describing the sustained slower decline, as presented in Equation 5. P = A ∗ exp(K1 ∗ t) + B + K2 ∗ t Equation 5 where, A and B are the extent of the decline in permeability, which are exponential (i.e. initial rapid) and linear (i.e. slower sustained) respectively (m); K1 and K2 are the exponential and linear rates of decline respectively (day-1); t is the time (days). The summative parameters A, B , K1 and K2, estimated by fitting Equation 5 to the permeability data, were used to compare the performance of the GDMF and PGDMF system in terms of permeability. The summative parameters obtained from curve fitting extracted from Equation 5 are summarized in Table 7. Figure 6: Average permeability vs time of PGDMF and GDMF systems (Error bars corresponds to standard error) 0.00E+005.00E-131.00E-121.50E-122.00E-120 10 20 30 40 50 60 70Permeability (m)Time (days)Fitted PGDMF Fitted GDMF PGDMF GDMF24 Table 7: Summative parameters from the average permeability vs time plot Estimated parameters (Values ± S.E) PGDMF GDMF Maximum extent of initial rapid decline (A) x 10-13 (m) 9.99 ± 0.001 12.00 ± 0.866 Rate of initial rapid decline (K1) x 10-2 (day-1) -6.10 ± 0.539 -25.12 ± 1.960 Maximum extent of sustained slow decline (B) x 10-13 (m) 4.26 ± 0.001 4.26 ± 0.002 Rate of sustained slow decline (K2) x 10-15 (day-1) 1.00 ± 0.050 4.00 ± 0.250 Goodness of the fit (R2) 0.71 0.75 ± corresponds to the standard error of the estimated parameters The extent and rate of initial rapid decline for the PGDMF was approximately 11% and 75% lower than that of the GDMF system. Both the PGDMF and GDMF systems then transition to a slower sustained linear behaviour after the initial rapid exponential decline. The time taken by the GDMF system to reach the slower sustained linear decline was approximately one-third (10 days) of the time for the PGDMF system (30 days). These results indicate that the passive fouling controls reduces the extent and the rate of initial rapid fouling of the membrane; and delay the time for the transition between the fast initial decline and the slow sustained decline. This has a direct impact on the cumulative overall throughput for the PGDMF and the GDMF system as discussed in section 4.2.1. The maximum extent of the slow sustained decline was similar for both systems. However, the slower sustained rate of decline for the PGDMF system was approximately four times lower than that of the GDMF system. The sustained permeability at the end of 10 weeks of operation for the PGDMF system was 3.45 ± 0.95 x10-13 (m), while the sustained permeability of the GDMF system at the end of 10 weeks was 1.31 ± 0.11 x10-13 (m) which is approximately three times lower than that of the PGDMF system. These results indicate that the PGDMF system will have lower overall decline in permeability for the same duration of operation than that of the GDMF system, necessitating a smaller membrane area or less frequent replacement or cleaning. This means that the passive fouling control measures contribute to lower overall decline of permeability compared to systems without passive fouling controls. 25 4.1.2 Overall throughput To comprehensively compare the overall throughput of the PGDMF and GDMF systems, an overall throughput calculation was performed. The overall throughput (Q) for the entire 70-day operation period was determined by integrating the area below Equation 5 for the PGDMF and the GDMF systems. Details of the calculations are presented in Appendix D.1. The PGDMF system produced approximately 26.83 m3 of water per m2 of membrane area during the operation period. In contrast, the GDMF system produced approximately 7.34 m3 of water per m2 of membrane area, which is approximately four times (3.7x) less than the overall throughput of the PGDMF system. 4.1.3 Summary: Permeability and overall throughput of PGDMF and GDMF systems The observed lower rates and extent of initial rapid, as well as the sustained slower decline in permeability, coupled with the resulting greater throughput for the PGDMF system compared to the GDMF system, conclusively validate the positive impact of including passive fouling control measures. 26 4.2 Effects of passive fouling control measures on the chemical and biological properties of the active layer Figure 7 illustrates the monitored chemical and biological parameters of active layer of the PGDMF and the GDMF systems plotted with respect to time. Note that in the present section, the time corresponds to that of the end of the different timelines considered. The data presented is an average of results for the chemical and biological parameters observed during the two replicate experiments and the errors correspond to the maximum and minimum values of the measured parameters. In general, the data is characterised by a rapid increase to apparent steady state conditions (i.e., minimal or no change in the magnitude of the results). Linear regression was used to assess if the apparent steady state conditions were truly sustained (i.e., did not change over time) and if so, quantify the magnitude of the sustained values, as well as the time at which the sustained values were reached (refer to section 3.4.4 for more details). Figure 7: Chemical and biological properties of PGDMF and GDMF, including a) concentration of ATP; b) concentration of TOC; c & d) beta diversity of PC 1 & PC2 respectively; e & f) Relative FTIR at 3360 & 1640 cm-1 respectively; g) concentration of EPS; dotted line corresponds to linear fit of the data 11010010001000010000010000000 20 40 60Conc. of ATP (pg/coupon) Time (days)a:GDMF PGDMF0.11101000 20 40 60TOC (mg/sq. cm)Time (days)b:PGDMF GDMF05101520250 20 40 60Scaled values of PC 1Time (days)c:PGDMF GDMF05101520250 20 40 60Scaled values of PC 2Time (days)d:PGDMF GDMF27 Figure 7 continued: Chemical and biological properties of PGDMF and GDMF, including a) concentration of ATP; b) concentration of TOC; c & d) beta diversity of PC 1 & PC2 respectively; e & f) Relative FTIR at 3360 & 1640 cm-1 respectively; g) concentration of EPS; dotted line corresponds to linear fit of the data 4.2.1 Evolution of the active layer to steady state For both the PGDMF and GDMF systems, all of the chemical and biological parameters monitored (except EPS) could be fitted using a linear relationship within 20 – 25 days from the start of the operation. The time to reach steady state conditions for the different properties monitored are summarised in Table 8. Note that the data for 0 corresponds to that of a virgin membrane and not that of the active layer. In general, the time to reach steady state conditions was greater for the PGDMF system than the GDMF system. These results indicate that the passive fouling controls delayed the establishment of steady state conditions in the active layer. 0123450 20 40 60Relative absorbance Time (days)e:PGDMF GDMF0123450 20 40 60Relative absorbance Time (days)f:PGDMF GDMF0.11101000 20 40 60EPS Protein Concentration µg/cm2Time in daysg:PGDMF GDMF28 Table 8: Impact of passive fouling controls on the properties of the active layer Analyses Magnitude at steady state Time to reach steady state Are the properties of PGDMF statistically# different from GDMF? (P value) PGDMF GDMF PGDMF GDMF TOC* 2.47 ± 0.05 (mg/cm2) 3.74 ± 0.1 (mg/cm2) N/A N/A Yes (0.0024<< 0.05) Concentration of ATP 95968 ± 10000 (pg/coupon) 80906 ± 10000 (pg/coupon) ~4 days ~4 days No (0.3034 > 0.05) FTIR- magnitude of peak at 3360 cm-1 3.48 ± 0.1 (Relative abs.) 3.52 ± 0.1 (Relative abs.) ~ 3 weeks ~2 weeks No (0. 4781 > 0.05) FTIR- magnitude of peak at 1640 cm-1 1.81 ± 0.1 (Relative abs.) 1.95 ± 0.1 (Relative abs.) ~ 3 weeks ~1 weeks No (0.9011 > 0.05) Beta diversity magnitude of PC 1 9.02 ± 1.0 (Scaled Value) 10.06 ± 1.0 (Scaled Value) ~2 weeks ~1 week No (0.0777 > 0.05) Beta diversity magnitude of PC 2 12.30 ± 1.0 (Scaled Value) 12.50 ± 1.0 (Scaled Value) ~2 weeks ~1 week No (0.09884 > 0.05) EPS* 15 ± 1.0 (µg/cm2) 25 ± 2.5 (µg/cm2) N/A N/A No (0.0854 > 0.05) *Steady state not achieved (data corresponds to last day of operation) #two tailed t tests ± corresponds to standard error For both PGDMF and GDMF systems, the time to reach steady state conditions were lowest for the concentration of ATP, followed by the beta diversity and the differential FTIR peaks (Table 8). Steady state conditions were not observed for the EPS and TOC over time. The short time to reach steady state conditions for ATP, a measure of microbial activity, suggests that a stable microbial mass was rapidly achieved (i.e., within 4 days of operation) for the GDMF and the PGDMF systems. The longer time to reach steady state conditions for principle components of beta diversity, a measure of microbial diversity, suggests that although the microbial mass did not change, after the first 4 days of operation for both the PGDMF and the GDMF systems, the composition of the microbial community continued to change for a few days for the GDMF system (i.e., <1 week) and for slightly more than one week for the PGDMF system (i.e., <2 weeks). The longest times to reach steady state conditions for differential FTIR peaks, a measure of chemical composition and biological products (such as amino acids etc.) of the active layer, suggests that the 29 composition of the active layer continued to change for longer (i.e., <1-2 weeks for GDMF and, <3 weeks for PGDMF) following the establishment of a stable microbial community. Note that, the concentration of protein components in the EPS of the active layer continued to change until the end of 10 weeks of operation. This indicated that even though the microbial activity, microbial diversity and composition of the biopolymers reached a steady state, the components of the EPS matrix developed on the active layer continued to change over time. Further studies beyond the scope of the present research, exploring the reason behind this behaviour observed in the active layer, is needed to investigate the change in concentration of various components of the EPS matrix. 4.2.2 Magnitude of the parameters at steady state The magnitude of the sustained values of the different parameters monitored are summarized in Table 8. For all the parameters reaching steady state, the magnitude of the monitored parameters was similar for GDMF and PGDMF systems, except for TOC. Within 95% confidence interval, using t tests, it was demonstrated that the chemical and biological properties of the active layers of PGDMF and GDMF systems were statistically similar to after reaching steady state. The results indicate that, although the time to reach steady state conditions differed for the PGDMF system compared to the GDMF system, the passive fouling controls did not affect properties of the active layer at steady state. The application of passive fouling controls changes the hydrodynamic conditions present in the system and in turn, the change in hydrodynamic conditions affects the time taken for the chemical and biological property of the active layer to reach steady state. These results indicate that although the active layer evolved at different rates for PGDMF and GDMF systems, overall chemical and biological parameters monitored, were similar for both PGDMF and GDMF systems. 4.2.2.1 Mass balance of TOC Steady state conditions were not reached for the magnitude of the TOC of the active layer. Additionally, the magnitude of the TOC in the active layer was greater for the GDMF system compared to the PGDMF system. It was hypothesized that the increase in TOC content of the active layer predominantly resulted from the retention and accumulation of organic material originally present in the feed water, and that the passive fouling control measures could more effectively remove the retained organic material, reducing the extent to which it accumulated in the active layer. To validate these hypotheses, a mass balance analysis was performed to access if the difference between the TOC in the active layer for the GDMF and the PGDMF 30 system, could be accounted by the TOC present in the reject of the PGDMF (i.e. removed by passive fouling control). The mass balance analysis compared the cumulative difference between in the mass of TOC in the active layer of PGDMF and GDMF system and the cumulative differences between the mass of TOC retained, for the GDMF and the PGDMF system. If the hypotheses are true, the cumulative differences between the mass of TOC present in the active layer of GDMF ad PGDMF would be expected to be similar to the cumulative difference between the mass of TOC retained for the PGDMF and the GDMF. The cumulative mass of TOC retained was defined as the TOC input (TOCin = feed) subtracted from the mass of TOC output (TOCout = permeate and reject for the PGDMF system; TOCout = permeate for GDMF system). Figure 8 illustrates the cumulative differences between the mass of TOC present in the active layers of GDMF and PGDMF with respect to time (in dark grey) and the cumulative difference between the cumulative mass of TOC retained for the PGDMF and GDMF systems (in light grey) with respect to time (Refer to Appendix D.3 for additional plots of cumulative TOC). Figure 8: Differences in TOC in the active layer and TOC retained in GDMF and PGDMF Paired t tests indicated that the differences between the two plots were not statistically significant (within 95% confidence level (P value = 1.70471x 10-8 < 0.05)). This indicates that the cumulative difference in the mass of TOC retained for the PGDMF and the GDMF system is similar to the cumulative difference in the TOC in the active layer of the PGDMF and GDMF systems. This indicates that the increase in TOC content of the active layer predominantly resulted from the retention and accumulation of organic material 0102030405060700 10 20 30 40 50 60 70Differneces cumulative TOC (mg/L)Time (days)Cumulative difference between mass of TOC in the active layer of PGDMF and GDMFCumulative difference between mass of TOC retained in PGDMF and GDMF systems31 originally present in the feed water, and that the passive fouling control measures could more effectively remove the retained organic material, reducing the extent to which it accumulated in the active layer. Moreover, because the throughput of the PGDMF was higher than the throughput of the GDMF, the mass of the TOC retained by the active layer would have been expected to be greater for the PGDMF than the GDMF. However, as previously discussed, the mass of TOC retained and accumulated on the active layer of the PGDMF system was less than that of the GDMF system. These results demonstrate the efficacy of passive fouling control measures at limiting the accumulation of the retained material in the active layer. 4.2.2.2 Correlation between the cumulative differences in TOC in the active layer and cumulative differences in the permeability for PGDMF and GDMF system Figure 9 illustrates the correlation between the cumulative differences in mass of the TOC in the active layer of PGDMF and GDMF system and the cumulative differences in the permeabilities for the PGDMF and GDMF system. The analysis reveals that before the active layer reaches steady state (i.e., <3 weeks of operation) as discussed in the previous section, no correlation is observed. After the steady state is reached (>3 weeks), a linear trend it observed. After reaching steady state, the cumulative difference in the permeability for PGDMF and GDMF system is directly related to the cumulative difference in mass of TOC retained in the active layer of the GDMF system and the PGDMF system. This suggests that, at steady state, the differences in permeability between the PGDMF and GDMF systems could result from the greater removal of retained organic material by passive fouling control measures in the PGDMF system. Figure 9: Correlation between cumulative differences in the permeability and mass of TOC retained in the active layer for PGDMF and GDMF system 0.E+002.E-124.E-126.E-128.E-121.E-111.E-111.E-112.E-112.E-112.E-110 10 20 30 40 50 60 70Cumulative differences in permeanbilities between PGDMF and GDMFCumulative differences in TOC of active layers of GDMF and PGDMF32 4.2.1 Summary: Effects of passive fouling controls on the properties of the active layer The magnitudes of the chemical and biological monitored parameters are not affected by the fouling controls after reaching steady state. However, the time to reach steady state is extended when passive foiling controls are implemented. The passive fouling control measures also effectively removed the accumulated organic material in the active layer, likely resulted in the observed differences in permeability between the PGDMF and GDMF systems, at steady state. Note that, differences between overall throughput of the PGDMF and GDMF systems were observed despite similarities observed in the active layer properties. This suggest that the chemical and biological parameters of the active layer do not govern the differences in the throughput of PGDMF and GDMF systems; rather the differences are likely governed by the ability of passive fouling control measures to limit the accumulation of material retained in the active layer. 33 4.3 Comparison of microbial properties of feed, reject and the active layer of the PGDMF system 4.3.1 Two component beta diversity analysis Figure 10 illustrates the beta diversity analysis of two principle components (PC 1 and PC 2) of feed, permeate, reject, and active layer of the PGDMF system. Each result on the plot corresponds to samples collected during different timelines of operation of the system. To interpret the results, it's important to note that, results from samples that are closer to each other on the plot indicate greater degree of similarity between the microbial communities in the samples. In Figure 10, results corresponding to the feed and reject for all timelines considered are generally clustered together, indicating that the microbial communities corresponding to feed and reject are similar and do not change over time. Initially (i.e., at day 4), the results from the active layer are clustered with the results for the feed and the reject. However, the results for the active layer then diverge from those of feed and reject, prior to forming a cluster that is distinct from that for the feed and the reject. Figure 10: Beta diversity plots of feed, reject and active layer of PGDMF system These results implies that initially, the microbial commuity present in the active layer is similar to that in the feed and the reject. With time, the communities become increasingly different. After approximatley 2 weeks, the microbial community in the active layer is distinct from that in the feed or the reject. These results indicate that other than initially following startup (>2 weeks), the microbial community developed on the active layer is distinct and neither the microbial community in the feed nor the reject can be considered as representative of that of the active layer. The distinct microbial communities in the active layer and the feed can also indicate that, although the feed is expected to influence the type of organisms retained in the active layer, it does not govern their accumulation and growth. The results also indicate that 4 days 1 week 2 weeks After 2 weeks 34 the microbial community, scoured from the active layer (i.e. the ones that are present in the rejcet) is similar to the microbial community present in the feed. Therefore, the majority of the microorganisms removed by the passive fouling control measures are likely those originally present in the feed and rather than those present in the active layer. 4.3.2 Summary: Comparison of microbial properties of feed, reject and the active layer of the PGDMF system Initially, the microbial community in the active layer is similar to the microbial communities in the feed and the reject. However, the microbial community in the active layer changes over time, eventually becoming distinct from that of the feed and the reject. 35 Chapter 5: Conclusions & future work 5.1 Conclusions I. Effect of passive fouling controls on permeability and overall throughput: The PGDMF system has lower rate and extent of rapid and sustained decline of permeability over time. Also, the cumulative throughput from the PGDMF system was higher (3.7x) than that of GDMF system. Employing passive fouling controls increases the overall permeability and cumulative throughput over time. II. Effects of passive fouling controls on the chemical and biological properties of the active layer: The passive fouling controls did not affect the magnitude of the chemical and biological monitored parameters of the active layer, except for the accumulation of organic material on the active layer. The difference between the accumulation of the organic material in the active layer between the PGDMF and GDMF systems could be correlated with the extent of organic material removed by passive fouling controls. The passive fouling control measures also effectively removed the accumulated organic material in the active layer, likely causing the observed differences in permeability between the PGDMF and GDMF systems, at steady state. The chemical and biological parameters of the active layer do not govern the differences in the throughput of PGDMF and GDMF systems; rather the differences are likely governed by the ability of passive fouling control measures to limit the accumulation of material retained in the active layer. III. Comparison of microbial properties of feed, reject and the active layer: The microbial community in the feed and the reject were similar to each other and did not change with respect to time. Meanwhile, the microbial community in the active layer initially evolved over time before reaching steady state. The microbial community present in the active layer after reaching steady state was distinct from that present in the feed or the reject from the PGDMF system. Therefore, the microbial community in the reject or the feed, cannot be used as a surrogate of that of the active layer. 36 5.2 Future work The present study laid the foundation for understanding the growth and establishment of the active layer in a gravity driven membrane filtration system with passive fouling controls, highlighting its relatively stable time-dependent development. However, there are opportunities for further exploration into the various factors that could influence active layer growth. The properties of the active layer could not be correlated with the properties of feed or the reject, with respect to the beta diversity analysis, therefore, other chemical and biological analyses could be carried out to establish a correlation. Factors such as feed water characteristics, temperature variations, external microbiome presence, altered hydrodynamic conditions (including air flow rate, hydrostatic pressure, and membrane configuration) could potentially impact the performance of PGDMF systems, further studies should be performed to investigate impact of each of these factors. Furthermore, the potential for engineering the active layer for specific applications presents an intriguing avenue for future research. Investigation could be done, exploring for the change in the composition or the concentration of various biopolymers in the EPS matrix, which is contributing to change in concentration of EPS with respect to time. 37 References Abkar, L., Taylor, A., Stoddart, A., & Gagnon, G. (2023). Microbiome and hydraulic performance changes of drinking water biofilters during disruptive events-media replacement, lake diatom bloom, and chlorination. 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Science of The Total Environment, 654, 10–18. https://doi.org/10.1016/j.scitotenv.2018.11.068 42 Appendix A: Introduction A.1. Hupacasath water treatment plant Hupacasath water treatment plant is a full-scale water treatment plant currently operational (as of December 2023) in Hupacasath First nation, near Port Alberni, Vancouver Island, BC, Canada (see Figure 11). This plant was commissioned as a pilot project to test the long-term performance and optimising the operating conditions of the first ever PGDMF plant. Currently the plant acts as water source for ~10 households living near the plant. The details regarding the operating conditions and water source are provided in the Table 9. Table 9: Properties for the full-scale PGDMF plant Parameter Value Units Comments Capacity of the plant (all 4 modules) 22.33 m3/d Capacity of reservoir 147.00 m3 Membrane modules ZW1500 No of modules 4 nos. Hydrostatic pressure for operation 21.5 to 32 cm Water source Sproat river Quality of water a) Turbidity b) TOC ~0.4 ~0.9 NTU mg/L Low Turbidity Low TOC Frequency of Passive Fouling Controls 12 & 24 hrs Air flow rate 8-10 L/min Estimated from time taken to empty the tank Figure 11: From left: Membrane modules inside the plant, the outside view of the plant and storage reservoir 43 Appendix B: Materials and methods B.1. Float switch interface The main purpose of the float switch interface is to maintain a constant level of feed in the feed tank. The float switch interface comprises of a float switch (Figure 12) linked to an electrical circuit board (Figure 12) present inside the interface, which activates the feed inlet valve connected to the feed tank. When the water level in the feed tank decreases, the float switch engages, completing the circuit. This action prompts the float switch interface to open the feed inlet from the main tank. As soon as the water level reaches the desired level (35 cm in this instance), the float switch disengages, breaking the circuit and halting the flow into the feed tank. Figure 12: From top left: Float switch interface box, circuits inside the Float switch interface, controller for maintaining cyclic operation of the system, constant level float switch 44 B.2. Membrane flow cell modification The modifications carried out on the cross-flow cells (Sterlitech® CF-042 cell assembly) are illustrated in Figure 13. The modified cell has two additional ports (one in the top for venting air and one in bottom for air intake) in addition to the hollow cavity. Figure 13: Modified and unmodified CF042 cells B.3. Air flow rate calculation For determining the air flow rate for operation, various factors including the air flow rate observed on the full scale PGMDF plant, scaling down factors, effect on the resistance of the membrane, visual inspection of size and the wake of the bubbles generated were considered. Since the full-scale setup has hollow fiber membranes and the bench scale set up employed flat sheet membranes, the air flow rate could not be simply scaled down and used. Differential air flow rate experiment was conducted to identify the optimal flow rate for the bench-scale setup. Three different flow rates (0.5, 1, and 2 L/min) were tested using the same experimental setup, with feed water collected from Spanish bank creeks. The observation revealed that the development of membrane resistance over time was minimal at 1 ml/min among the tested flow rates. Effects of aerators: Various custom-made aerators were tested to assess their impact on bubble size and distribution within the system. Visual bubble formation and wake patterns changes were observed for systems with different aerators and without aerators. It was observed that no changes were observed in aerating the systems with and without aerators. For the present study, 1 L/min was chosen as the operating flow rate for the bench scale setup with no aerators. Modified CF042 cell showing cavity and openings for aeration. Unmodified CF042 cell 45 B.4. Protocol for cleaning and clean water resistance tests for virgin membrane Membrane cleaning protocol for virgin membrane: Several different protocol for cleaning the virgin membrane were tested and the best practice was determined by observing the resistance of the membrane to DI (Millipore) water after cleaning. Membranes were tested with soaking in clean water, soaking in 500 ppm Sodium hypochlorite and also permeating clean water through it using the same set up, as the experiment described in section 3.1. It was observed that soaking in bleach for long term was detrimental to the membrane. And permeating clean water for 24 hrs was determined to be the best practice for cleaning the membrane. So, for all the experiments performed all the virgin membranes were cleaned by permeating DI water through it with a hydrostatic water pressure of 35 mbar for 24 hours. Clean water resistance tests: Clean water resistance tests were conducted on all virgin membranes before operation to evaluate their initial resistance. The resistance of the virgin membranes was assessed using the same setup employed for membrane cleaning. DI water was permeated through the membrane, and its resistance was measured. Only membranes falling within the range of the manufacturer’s suggested resistance (4 - 6 x 10+11 m) were retained for the study, and the rest were discarded. 46 B.5. Operational timelines The various timelines in which the system was operated summarised in Table 10 along with the dates of operation. Table 10: Different timelines of operation with dates 47 B.6. Extracted active layer samples The active layers developed on the membranes were harvested and cut into coupons. For analysing the TOC and other bulk properties, the active layer samples were made into a solution. The following protocol was followed for making a solution of the active layer: 1. The surface of the membrane to be sonicated is wetted with a known volume (2-3 ml) of PBS solution using a syringe. 2. The runoff is collected in a petri dish. 3. Active layer present on the harvested coupon is scraped off using a sterile scraper and placed into a sterile petri dish. 4. The scraper is cleaned with a known volume of PBS using a syringe and the solution obtained from cleaning the scraper is also added to the petri dish. 5. The scraped off (extracted) active layer in the petri dish and the membrane are transferred into a sterile falcon tube, and the volume is made up to 25 ml with PBS solution. 6. The solution in the falcon tube along with the membrane is sonicated using a sonicator (Digital Sonifier SFX 150 sonicator - Branson) at 40% intensity for 30 s with 5 x 10% intensity pulses. 7. The sonicated solution in the falcon tube is homogenized using a vortex mixer (Vortex Mixer, 0 to 3400 rpm, 115 VAC - Cole-Parmer®). 8. The membrane is carefully removed from the solution using a sterile tweezer after homogenizing and the remaining solution, which is known as extracted active layer sample, used for further analyses. 48 Appendix C: Quality control and quality assurance C.1 Construction and operation Water head measurements: To maintain a consistent hydrostatic water head of approximately 35 cm for permeation, digital level measurements were conducted. The laser level was used to measure the water level on top of the feed tank water line. This procedure was also conducted to guarantee that the provided driving force was equal for all six systems in operation. Figure 14 shows the digital level line (Shown by yellow line) on the top of the water level in the feed tank. Figure 14: Digital level measurements in the feed tank using a laser level Vent system: A "T" arrangement was implemented by adding a small pipe to the permeate side of the membrane flow cell. This was done to ensure uniformity in the height at which the flow cells were opened to the atmosphere. The reference point for establishing the zero-level, from which a 35 cm water head was measured and applied, was the junction of the pipe open to the atmosphere, as indicated by the yellow arrow in Figure 15. The permeate output from the flow cells followed the path highlighted by the red arrows in the Figure 15. 49 Figure 15: Vent system on the flow cell with open to atmosphere arrangement Flat sheet membrane selection: To select the suitable flat sheet membrane for system operation, membranes with resistances similar to ZW1500 (used in the full-scale treatment plant, see Table A.1) were chosen. Tests were performed on V5, V7, and UB70 membranes using Spanish bank creeks water as feed, permeating at 35 cm hydrostatic pressure for two weeks. Among the three membranes, the Trisep UB70 flat sheet UF membrane was selected, as the observed resistance during operation, closely matched ZW1500, after two weeks of operation. C.2 Sample collection and storage The procedures for sample collection, storage, and analysis were adapted from the guidelines outlined in Baird, R., & Bridgewater, L., 2013. Blanks: During all analytical procedures, a minimum of two blank samples, containing all typical components found in the samples except the material of interest, were analyzed concurrently to account for background levels. Multisampling: To ensure accuracy, each sample was subjected to multisampling, with at least two additional samples analyzed alongside. The reported result was based on the average of all three values, with outliers identified and excluded based on a 95% confidence interval. Standards: Standards were incorporated into the analyses as required, including the development of standard curves for specific analyses, such as TOC and ATP analysis. Multiple data measurements: To ensure the accuracy and reliability of the collected data, various quality control measures were implemented. This included the use of different set of equipment when available. For example, data obtained from the Total Organic Carbon (TOC) analyzer were validated by running the 50 same samples through a different TOC analyzer. This redundancy enhanced the robustness of the data collection process. Sanitization and cross-contamination prevention: Preventing cross-contamination was of paramount importance throughout the data collection process. To achieve this, a strict protocol as outlined in (Baird, R., & Bridgewater, L., 2013) was followed, ensuring that a specific type of container was used exclusively for each type of analysis. Rigorous sanitation procedures were adhered to when containers needed to be reused, further minimizing the risk of cross-contamination. C.3 Data Analysis and Statistics Correction for System-Induced TOC: In the analysis of samples for Total Organic Carbon (TOC), it was observed that the system (including piping, tanks, membrane) introduced a certain level of TOC into the water samples. To address this, each system component was assessed for its contribution to the overall TOC levels in the water. This evaluation revealed that the membranes, pipes, and membrane flow cells were sources of TOC contamination in the water samples. As a result, final reported values for all analyzed samples were corrected to account for this system-induced TOC, by subtracting the TOC induced by the system from the obtained results. Outlier detection and error bars: Data analysis excluded outliers. Outliers were defined as values exhibiting a deviation from the mean at 95% confidence interval. 51 Appendix D: Results D.1 Throughput calculation The calculation of the total volume of water filtered during the entire operation period of 70 days is done by integrating the flux equation over time. Flux (J) = (TMP ∗ P)/μ from Equation 4, Where, o TMP = Trans Membrane Pressure = 3431.45 (N/m2) o Area the membrane= 0.0036 (m2) o µ=Coefficient of dynamic viscosity =0.000931 (N*s/m2) o Q= Overall throughput over the operating period Flow = J *Area (m3), integrating the flow from day 0 to day 70 (Total time of operation of the system) Q = ∫ (TMP ∗ Area)/μt=70t=0 (P)dt Equation 6 Q = ∫ �3431.45 ∗ 0.00360.000931� ∗ (P)700 dt =∫ �3431.45 ∗ 0.00360.000931� ∗ (P)700 dt =∫ (13268.8) ∗ (P)700 dt = (13268.8) ∗ ∫ (P)700 dt Where, PGDMF=> PPGDMF = 9.99x 10-13 x e(-0.061*t) + 4.26x10-13 – 1.0x10-15 x t, And GDMF=> PGDMF = 12.00x 10-13 x e(-0.2512*t) + 4.26x10-13 – 4.0x10-15 x t, from Equation 5 PGDMF, = (13268.8) ∗ ∫(9.99x 10-13 x e(-0.061*t) + 4.26x10-13 – 1.0x10-15 x t)dt where t is in days, integrating from 0 to 70 = (13268.8) ∗ 8.425x10-11 (m3/s * days), = 0.09658656 (m3) 52 QPGDMF = 96.59 L GDMF, = (13268.8) ∗ ∫(12.00x 10-13 x e(-0.2512*t) + 4.26x10-13 – 4.0x10-15 x t)dt where t is in days, integrating from 0 to 70 = (13268.8) ∗ 2.479x10-11 (m3/s * days), = 0.02841986 (m3) QGDMF = 28.42 L D.2 FTIR of the active layer The absorbance vs wavenumber plot of different active layers at different time of operation is shown in Figure 16. As seen highlighted in Figure 16, there are distinct peaks observed near wavenumbers 1020, 1100, 1240, 1340, 1410, 1640, 1710 and 3360 cm-1. Note that, the peak near at 3360 cm-1 has a broad range with the highest absorbance seen at 3360 cm-1. Figure 16: Full spectra of absorbance vs wavenumber of various operational timelines 53 D.3 Mass balance of TOC Figure 17 shows the cumulative mass TOC over time, the difference in the TOC accumulated on the active layer on the PGDMF and GDMF systems. Figure 17: Cumulative TOC vs time (a: PGDMF, b:GDMF) 050100150200-10 10 30 50 70Mass of TOC (mg)Time (days)a:GDMF Permeate GDMF feedGDMF Active layer050100150200-10 10 30 50 70Mass of TOC (mg)Time (days)b:PGDMF Permeate PGDMF RejectPGDMF feed PGDMF Active layer54 D.4 SEM images SEM images provide a visual analysis of the active layer developed by PGDMF and GDMF systems. SEM images captured at a Horizontal Field Width (HFW) of 15 µm are presented for illustrative purposes in Figure 18. It is notable that after 4 days of operation, the active layer of PGDMF and the active layer of the GDMF systems exhibit similarities with mesh-like structures. However, at 10-weeks of operation, the active layer of the PGDMF and GDMF system shows filamentous structures, with less material in between the filamentous structure in the case of PGDMF system. This suggests that although the characteristics of the active and the beta diversity is similar, the accumulation and retention of various cells in the active layer may differ. PGDMF GDMF 4 days 10 weeks Figure 18: Illustrative SEM images of active layer taken on 4 days and 10 weeks of operation 55 D.5 Properties of the permeate and reject Properties of permeate: TOC of the permeate: The concentration of TOC in the permeate remains constant over time irrespective of the change in time (Figure 19). The TOC released into the permeate of the GDMF and the PGDMF systems are not statistically different from each other. This means that the passive fouling controls and increased water output of the PGDMF system does not affect the quality of the permeate. Figure 19: Properties of permeate over time *Represents TOC corrected for contributions from the system (refer to Appendix C.3) Table 11: Summary of statistical tests comparing permeate of PGMDF and GDMF Parameters compared Type of test Confidence interval P value Are the differences between PGDMF and GDMF statistically significant? TOC of the permeate Paired two sample t test 95% 0.3846 > 0.05 No Properties of reject: ATP of the reject: The ATP concentration of the reject remains constant over time irrespective of the change in time. (Figure 20) TOC of the reject: The concentration of TOC in the reject remains constant over time irrespective of the change in time.(Figure 20) 00.511.522.530 20 40 60TOC (mg/L)Time (days)TOC of permeate*PGDMF GDMF56 Beta diversity of the reject: The principal components PC 1 and 2 representing the beta diversity of the reject was plotted against time (Figure 20). It is observed that the beta diversity plot of PC 1 and PC 2 of the reject remained relatively parallel to the x axis. This suggests that the microbial community in the reject do not exhibit significant changes and remained constant over time. Figure 20: Properties of reject over time (a: ATP, b: TOC, c & d: PC1 and PC2 respectively) *Represents TOC corrected for contributions from the system (refer to section C3) D.6 Correlation analysis TOC of the active layer and reject: TOC released via a reject is plotted against the TOC that is accumulated on the active layer as illustrated in Figure 21. A positive correlation is noticeable between the increase in TOC of the active layer with increase in the TOC of the reject sample for the first 2 weeks of operation. After this period, the increase in the mass of TOC of the active layer is slower than the increase of TOC of the reject. ATP of the active layer & reject: Concentration of ATP in the reject is plotted against the Concentration of ATP on the active layer (Figure 21). No significant trends were observed in the data. Note that the first data 1101001000100000 20 40 60Conc. of ATP pg/mlTime (days)a:0246810120 20 40 60TOC* (mg/L)Time (days)b:0246810120 20 40 60Scaled values of PC 1 Time (days)c:05101520250 20 40 60Scaled values of PC 2 Time (days)d:57 point represents the ATP present in the virgin membrane it is not comparable with the ATP present in the active layer. Figure 21: Correlation plots of active layer vs reject (a: TOC, b: ATP) Microbial community of the reject and the active layer: A correlation plot between the beta diversity of the reject and beta diversity of the active layer is plotted in Figure 22 for PC 1 and 2. The beta diversity correlation plot remained relatively stable, parallel to x axis, indicating that the beta diversity of the active layer remained consistent, regardless of the change in beta diversity of the reject sample. It suggests that the changes in beta diversity of the microbial communitiy in the active layer is not represented by changes in the beta diversity of the microbial community in the reject. Figure 22: Beta diversity correlation between reject and the active layer 01234560 50 100 150 200 250Active layer TOC (mg/sq. cm)Reject TOC (mg/L)a:2 weeks1101001000100000 50000 100000 150000Conc. of ATP -Active layer (pg/L)Conc. of ATP- Reject (pg/L) b:0123456789100.5 6.2 7.75 8.1 9 9.1 9.2 9.1 9.05Scaled values of the rejectScaled values of the active layer PC 1 24% PC 2 11%
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Bérubé, Pierre R.
Comparative study of biofilm in GDMF and PGDMF systems
Text