International Construction Specialty Conference of the Canadian Society for Civil Engineering (ICSC) (5th : 2015)

Minimizing greenhouse gas emissions and water consumption of existing buildings Abdallah, Moatassem A.; El-Rayes, Khaled A.; Clevenger, Caroline M. Jun 30, 2015

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5th International/11th Construction Specialty Conference 5e International/11e Conférence spécialisée sur la construction    Vancouver, British Columbia June 8 to June 10, 2015 / 8 juin au 10 juin 2015   MINIMIZING GREENHOUSE GAS EMISSIONS AND WATER CONSUMPTION OF EXISTING BUILDINGS Moatassem A. Abdallah1,3, Khaled A. El-Rayes2 and Caroline M. Clevenger1 1 University of Colorado Denver, USA 2 University of Illinois at Urbana-Champaign, USA 3 moatassem.abdallah@ucdenver.edu Abstract: Buildings are responsible for 38% of all carbon emissions and 14% of water consumption in the United States. These negative environmental impacts can significantly be reduced by implementing green upgrade measures such as energy-efficient lighting and HVAC systems, motion sensors, photovoltaic systems, and water-saving plumbing fixtures. Building owners in the public and private sectors often search for an optimal set of upgrade measures that is capable of minimizing the negative environmental impacts of their buildings. This paper presents the development of an optimization model that is capable of identifying optimal selection of building upgrade measures to minimize greenhouse gas emission and water consumption of existing buildings while complying with limited upgrade budgets. The model is developed in four main development steps: metrics identification step that quantifies greenhouse gas emissions and water consumption of existing buildings; model formulation step that formulates the model decision variables, objective function, and constraints; implementation step that executes the model computations and specifies the model input and output data; and validation step that evaluates the model performance using a case study of an existing building. The results of the model illustrate its new and unique capabilities in providing detailed results, which include specifications for the recommended upgrade measures, their location in the building, and required upgrade cost to minimize greenhouse gas emissions and water consumption of existing buildings.  1 INTRODUCTION Buildings in the United States are responsible for significant GreenHouse Gas (GHG) emissions due to significant percentage of electricity consumption (72%), energy consumption (39%), and water consumption (13%)  (U.S. Environmental Protection Agency 2009). These GHG emissions due to energy and water consumption contribute to negative environmental impacts including, global warming, ozone depletion, and air pollution (ICF Incorporated 1995; U.S. Environmental Protection Agency 2009, 2012; United Nation Environment Program 2007). The negative environmental impacts of buildings can be reduced by implementing sustainable measures such as energy efficient building fixtures and equipment, water-saving plumbing fixtures, and installing renewable energy systems. These sustainable measures require high upgrade costs and decision makers are often confronted with a challenging task to identify optimal selection of building upgrade measures within their upgrade budget. In order to support decision makers in this challenging task, there is a pressing need to develop optimization models that are capable of identifying optimal selection of building upgrade measures to minimize negative environmental impacts of existing buildings within available budgets. 323-1 Several systems have been developed and studies conducted to estimate the negative environmental impacts of buildings during their construction, operation and demolishing such as Building for Environmental and Economic Sustainability (BEES) (Lippiatt et al. 2010), ATHENA impact estimator (The ATHENA Institute 2013), and envest2 (Building Research Establishment 2003). Other studies focused on evaluating the implementation of various sustainability measures such as energy-efficient lighting systems, energy-efficient HVAC systems and heat pumps (Bloomquist 2001; Chiasson 2006; Das et al. 2013; International and Conference 2003; Narendran and Gu 2005; Phetteplace 2007; RUUD LIGHTING 2010), renewable energy systems (Chapman and Wiczkowski 2009; James et al. 2011; Matthews et al. 2004; State Energy Conservation Office 2006), and water-saving plumbing fixtures (GAO 2000). Furthermore, several optimization models have been developed to minimize existing buildings’ operational costs (Abdallah et al. 2014), identify optimal selection of structural and architecture design of new buildings (Bichiou and Krarti 2011; Fialho et al. 2011), and identify optimal decisions of building renovations (Brandt and Rasmussen 2002; Juan et al. 2010; Kaklauskas et al. 2005). Despite the significant contribution of the existing research studies, limited or no optimization models exist that are capable of selecting building upgrade measures of existing buildings in order to minimize their negative environmental impacts of greenhouse gas (GHG) emissions and water consumption simultaneously. Furthermore, limited or no optimization models exist that consider various sustainability measures of building fixtures and equipment, renewable energy systems, and plans of managing solid waste simultaneously in order to minimize the negative environmental impacts of existing buildings while complying with a user-specified upgrade budget and building operational performance.  2 RESEARCH OBJECTIVE The objective of this research study is to develop an optimization model that is capable of minimizing negative environmental impacts of existing buildings. This optimization model is designed to provide the optimal selection of building upgrade measures to minimize GHG emissions and water consumption of existing buildings while complying with available upgrade budgets and specified building operational performance. This optimization model is expected to support decision makers and building owners in their ongoing efforts to minimize negative environmental impacts of their buildings by optimal allocation of their budgets. The optimization model is developed in four main steps: (1) metric identification step, which quantifies the negative environmental impacts of buildings; (2) model formulation step that formulates the model decision variables, objective function, and constraints; (3) implementation step that executes the model computations using Genetic Algorithms (GAs); and (4) evaluation step that validates the model performance using a case study of an existing building. The following sections describe the development steps of the optimization model to illustrate the capabilities of the model and demonstrate its use.  3 OPTIMIZATION MODEL DEVELOPMENT 3.1 Metric Identification  The optimization model is developed to quantify the negative environmental impacts of existing building in terms of GHG emissions that result from buildings energy consumption; energy use for water extraction, treatment, distribution, and wastewater treatment; and buildings solid waste (ENVIRON International Corporation 2013; Flager et al. 2012; ICLEI - Local Governments for Sustainability USA 2010, 2012; Kwok et al. 2012; Liu et al. 2013; Ordóñez and Modi 2011; Safaei et al. 2012; TranSystems|E.H. Pechan 2012; U.S. Environmental Protection Agency 2006; USGBC 2014a; Zhu et al. 2013); and water consumption from plumbing fixtures.   The GHG emissions from building operation consist of carbon dioxide (CO2), nitrous oxides (N2O), methane (CH4), and ozone (O3) (TranSystems|E.H. Pechan 2012). The global warming potential factors that are developed by the Intergovernmental Panel of Climate Change (IPCC) can be used to represent all GHG emissions in terms of equivalent quantities of CO2 emissions (Intergovernmental Panel on Climate Change 2007). The GHG emissions are quantified in the developed model based on the calculated (1) energy consumption of the building; (2) energy use during water extraction, treatment, 323-2 distribution, and waste water treatment; and (3) fugitive emissions of waste water, and solid waste. The GHG emissions of energy consumption are calculated based on the building electricity and natural gas consumption and the location of the building.  This accounts for the types of plants that are used to generate energy for the building and associated average transmission losses. For major electricity grids in the United States, the Environmental Protection Agency provides energy emission factors and average transmission loss percentages, which can be used to estimate emissions of energy use in buildings (TranSystems|E.H. Pechan 2012). To estimate GHG emission of buildings, electricity and natural gas need to be calculated. The developed optimization model calculates electricity and natural gas consumption of the buildings using energy simulation software packages such as QUick Energy Simulation Tool “eQUEST” (U.S. Department of Energy 2013).  In addition to the GHG emissions that are created by the direct building energy consumption, the water consumption of the building create additional GHG emissions due to energy used in water extraction, conveyance and supply, treatment, and distribution. These emissions can be calculated based on annual building water consumption; and energy intensity of water extraction, water supply and conveyance, water treatment, and water distribution (ICLEI - Local Governments for Sustainability USA 2012). Similarly, the GHG emissions from wastewater treatment can be calculated based on annual building waste water; and energy intensity of wastewater collection, aerobic digesters for wastewater treatment, lagoons for wastewater treatment, attached growth of wastewater treatment, and nitrification or nitrification/denitrification of wastewater treatment (ICLEI - Local Governments for Sustainability USA 2012).  Another source of GHG emissions in existing buildings is calculated based on solid waste sent to landfill, combustion, composition, or recycling. The United Stated Environmental Protection Agency provides emission factors for each of these methods of managing solid waste (U.S. Environmental Protection Agency 2006). Accordingly, the annual equivalent carbon dioxide of solid waste in buildings can be calculated in the model based on the weight of each solid waste material and the associated emission factor calculated according to solid waste management method. According to all the aforementioned sources of GHG emissions, the model calculates the total equivalent emissions of existing buildings by aggregating all sources of GHG emissions. Plumbing fixtures are responsible for the majority of water consumption in buildings. They include water faucets, showerheads, kitchen sinks, urinals, and toilets. Building water consumption can be calculated in the developed model based on type of building, type of plumbing fixtures, and number of occupants according the guidelines of the LEED rating system for existing buildings (USGBC 2014b). 3.2 Model Formulation The decision variables of the optimization model are designed to represent all feasible alternatives of building fixtures and equipment that consume energy or water using integer decision variables such as lighting fixtures and bulbs, HVAC systems, water heaters, refrigerators, vending machines, hand dryers, and water plumbing fixtures. The model is also designed to integrate energy saving measures using integer decision variables such as motion sensors, solar panels, inverters, and percentage of renewable energy that can be generated at the building site. In addition, the model is designed to integrate plans of managing solid waste using integer decision variables that represents the disposal of each solid waste using landfill, recycling, combustion, or composition.      The objective function of this optimization model minimizes the negative environmental impacts of existing buildings by minimizing GHG emissions and water consumption. The model accounts for GHG emissions and water consumption of buildings using Building Environmental Performance Index (BEPI). This index ranges from 0.0 which represent a fully sustainable building to 1.0 which represent no reduction in negative environmental impacts of the building as shown in Equation (1).      (1) 323-3 Where:  is building environmental Performance index;  is building GHG emissions after implementing upgrade measures;  is existing building GHG emissions; is building water consumption after upgrade measures; is existing building water consumption; and  are relative importance weights of GHG emissions and water consumption, respectively.  To ensure the practicality of this optimization model, it is designed to comply with two main constrains: (1) building performance constraints, and (2) upgrade budget constraint. The building performance constraint is integrated in the model to ensure that the required operational performance of the building will be maintained after replacing its fixtures and equipment, including space heating and cooling, water heating capacity, and light levels. The upgrade budget constraint is integrated in the model to ensure that the cost of upgrading the building fixtures and equipment, installing renewable energy systems, and managing solid waste will not exceed the specified upgrade budget.  3.3 Model Implementation The computations of the optimization model are executed using Genetic Algorithms (Gas) due to its (1) efficiency in modeling the optimization problem with the least number of decision variables, (2) capability to model non-linearity and step changes in the objective function and constrains that are caused by replacing building fixtures and equipment, (3) capability of identifying optimal solution within reasonable computational time  (Aytug and Koehler 1996; Goldberg 1989; Pendharkar and Koehler 2007).   The computation procedure of the developed model starts by searching an integrated databases in order to identify feasible replacements of HVAC systems and water heaters. The model then creates eQuest input files of feasible replacements and sends them to eQuest simulation environment to calculate their energy consumption. The model then stores the calculated energy consumption of HVAC equipment and water heaters in a database where it can be used during the optimization process. The GA computations start by generating random selection of building upgrade measures which represent the initial population. The fitness of this initial population is evaluated based on the index of negative environmental performance index and the model constraints. Solutions that satisfy all the constraints and achieve low values of negative environmental performance index are classified as solutions with high fitness values. On the other hand, solutions that achieve high values of negative environmental performance index or do not satisfy the model constraints are classified as solutions with low fitness values or infeasible solutions, respectively. Solutions with high fitness are then ranked based on their index of negative environmental performance index where the GA operators of selection, crossover, and mutation are applied to generate a new set of population. This process is iteratively repeated until no further improvements are achieved within a predefined number of iterations. It should be noted that the initial population of the model is set based on the GA string and possible values of the model decision variables (Reed et al. 2000; Thierens et al. 1998). The developed optimization model is integrated with databases of building fixtures and equipment, components of renewable energy systems, and various types of building solid waste. These databases are designed to include general product data, cost data, energy and water characteristics, and physical characteristics of building fixtures, and components of renewable energy systems, including lighting bulbs and fixtures, motion sensors, HVAC equipment, water heater, hand dryers, vending machines, refrigerators, PCs, water coolers, solar panels, solar inverters, water faucets, urinals, and toilets. The databases also include data on energy intensity of water extraction, conveyance, treatment, distribution, and waste water treatment; and emission factors of energy consumption and solid waste according to the location of buildings in the United States. For example, the equivalent emission factors of electricity consumption, electricity savings, and average transmission losses of all electricity grids are stored in the model databases, as shown in Table 1. 323-4 Table 1: Sample emission factors and average transmissions losses of electricity grids in USA (TranSystems|E.H. Pechan 2012) eGRID subregion name  Equivalent CO2 emission rate (lb/MWh) Equivalent non-baseload CO2 emission rate (lb/MWh) Power grid average transmission loss (%) SERC Virginia/Carolina 1,041.73 1,686.09 5.82% RFC West 1,528.76 2,012.22 5.82% WECC California 661.20 995.85 8.21% WECC Southwest 1,196.58 1,190.97 8.21% 4 CASE STUDY A rest area building was analyzed and optimized by the developed optimization model in order to illustrate the model capabilities and demonstrate its use. This rest area building is located in Illinois and it was selected due to its high levels of negative environmental impacts caused by its continuous operation throughout the year and its inefficient energy and water fixtures. The building was built in 1989 and renovated in 1992 with a total area of 2500 square foot. This rest area building includes men’s and women’s bathrooms, lobby, vending area, travel information desk, storage rooms, mechanical room, attic, and detached small garage. The rest area also has parking lots for visitors that accommodate cars and semi-trucks. The major contributors of energy consumption in the building include interior and exterior lighting systems, water heater, HVAC systems, six vending machines, four hand dryers, five water coolers, PC, surveillance system, and five code blue emergency phones. The major contributors of water consumption in the building include eight toilets, two urinals, and six water faucets.  In order to minimize the negative environmental impacts of the rest area building, the optimization model requires input data of (1) building characteristics, including building size, construction materials, air infiltration, doors and windows, operational schedule, allocation of building activities, temperature set points, and airflow, as shown in Table 2; (2) characteristics of building equipment and fixtures which can be selected from the model databases, as shown in Table 3; (3) amounts of building solid wastes, as shown in Table 4; and (4) importance weights of building negative environmental impacts which were specified at 80% and 20% for GHG emissions and water consumption, respectively. It should be noted that the importance weights can vary from one decision maker to another, and the model enables them to specify their own weights accordingly.   Table 2: Sample input data of the building characteristics  Description Building characteristics Building envelop (roof surfaces) Wood advanced frame 24’’ with dark brown shingles roofing and R-30 batt. Building envelop (above grade walls) 6’’ CMU with brick exterior finishing and perlite filling  Building infiltration 1.0 ACH for perimeter and core Building interior construction  Lay-in acoustic tiles flooring with R-11 batt, and mass interior walls.  Building operation schedule 24 hours  323-5 Table 3: Sample input data of the building fixtures  Building fixture   Input Data  Location Feasible Alternatives Description Number of fixtures Working hours per day  Men’s bathroom – Set 2  1 Square fluorescent fixture with 2 T8 U-shaped lamps of 28 W and 2265 lumens 3 24   2 Square fluorescent fixture with 2 T12 U-shaped lamps of 34 W and 2279 lumens   … ….   20 Square fluorescent fixture with 2 T12 U-shaped lamps of 35 W and 2235 lumens  Building HVAC System # 1  1 Electric HVAC system  1 24   2 Gas Energy Star rated HVAC system   3 Ground-source heat pump   4 Electric Energy Star rated HVAC system   Men’s bathroom – hand dryers  1 Hand dryer - 2300 W and 30 sec drying time 2 Per use   2 Hand dryer - 1100 W and 12 second drying time   3 Hand dryer - 1100 W and 15 second drying time   ….. …..   9 Touchless paper towel dispenser  Women’s bathroom - toilets  1 Electronic flushing toilet with 3.5 gallons per flush 8 Per use   2 Electronic flushing toilet with 1.6 gallons per flush   3 Electronic flushing toilet with 1.28 gallons per flush   Table 4: Sample of managing solid waste at the rest area building  Solid Waste Annual Weight (ton) Managing solid waste Aluminum cans 0.1 Landfill Newspaper 0.3 Landfill Food scraps 0.5 Landfill Mixed paper 0.3 Landfill Mixed plastics 0.3 Landfill The optimization model was used to minimize the negative environmental impacts of the rest area with various upgrade budgets that ranged from $10K to $100K. The model was able to identify the optimal upgrade decisions for all the specified upgrade budgets, as shown in Figure 1. For example, solution (a) in Figure 1 identified by the model as an optimal solution for an upgrade budget of $50K, and it provides a moderate reduction in the negative environmental performance index of (BEPI = 0.519) with an upgrade cost of $49,673. On the other hand, solution (b) is identified by the model as an optimal solution for an upgrade budget of $100K, and it provides minimum negative environmental performance index of (BEPI = 323-6 0.397) that caused reduction in GHG emissions by 58% and water consumption by 69%, as shown in Figure 1.  Figure 1: Results of minimizing negative environmental impacts of the rest area building The model is designed to provide an action report for the generated optimal solutions which include detailed information of all the recommended building upgrade measures. For example, the model generated the recommended upgrade measures and solid waste management plans for optimal solution (a) in Figure 1 as shown in Table 5 and Table 6, respectively. The results of the model identify the optimal selection of building upgrades based on an identified upgrade budget which helps decision makers and building owners in their ongoing task of maximizing the sustainability of their building while complying with their available budgets.   Table 5: Sample recommended replacements of the building fixtures for upgrade budget of $50K Room Recommended Replacements Men's & women’s bathrooms, lobby, & information rooms Replace 32 existing T12 U-shaped lamps of 35 W, 2235 lumens, and 18,000 hours life expectance with 22 T8 U-shaped lamps of 28 W, 2380 lumens, and 30,000 hours life expectancy.  Men's & women’s bathrooms, information, vending storage, & garage Replace 28 existing longitudinal fluorescent T12 lamps of 34 W, 2280 lumens, and 20,000 hours life expectance with 10 longitudinal fluorescent T8 lamps of 25 W, 2280 lumens, and 40,000 hours life expectancy. Building Replace existing HVAC equipment with EnergyStar rated gas furnace and EnergyStar rated condensing units. Vending storage Replace existing fridge with energy efficient unit. Men's & women’s bathrooms Replace existing hand dryers of 2300w and 30 sec. drying time with touchless paper towel dispenser. Building Install photovoltaic system to generate 8.5% of the total building energy demand. Men's & women’s bathrooms Replace 6 existing water faucets of 1.5 gallons per minute with electronic faucets of 0.5 gallons per minute. Men's & women’s bathrooms Replace 8 existing toilets of 3.5 gallons per flush with water efficient toilets of 1.28 gallons per flush. Men's bathroom Replace 2 existing urinals of 1.6 gallons per flush with water efficient urinals of 0.125 gallons per flush. Men's & women’s bathrooms Install motion sensors to turn off the lighting automatically in men’s and women’s bathrooms when there is no occupants.    323-7 Table 6: Sample recommendations of managing solid waste for upgrade budget of $50K  Solid Waste Recommendations Aluminum cans Collect aluminum cans and send them to recycling Newspaper Collect newspapers and send them to recycling Food scraps Collect food scraps and send them to composting Mixed paper Collect mixed paper and send it to recycling Mixed plastics Collect mixed plastics and send them to recycling 5 SUMMARY AND CONCLUSIONS  This paper presents the development of an optimization model that is capable of minimizing negative environmental impacts of existing buildings by minimizing their GHG emissions and water consumption. The model is designed to identify the optimal selection of building upgrade measures while complying with a specified upgrade budget and preferred building operational performance. The model was developed in four main steps: metric identification step, model formulation step, implementation step, and evaluation step. The metrics identification step identified novel metrics for quantifying the negative environmental impacts of existing buildings in terms of GHG emissions and water consumption. GHG emissions were calculated based on energy consumption, energy use of water extraction, treatment, distribution, and wastewater treatment; and buildings solid waste. The formulation step identified the model decision variables, objective function, and constraints. The model is designed to include decision variables that have impact on GHG emissions and water consumption including building fixtures and equipment, renewable energy systems, and water plumbing fixtures. The objective function is designed to minimize GHG emissions and water consumption using an index that account for these impacts using importance weights. The model integrated a number of constraints to comply with specified upgrade budgets and building operational performance.  The model implementation step include the execution of the model computations using Genetic Algorithms (GAs) and the development of databases to facilitate input and output data of the model. The evaluation step validated the model performance using a case study of a rest area building. The model was able to identify the optimal selection of building upgrade measures for various budgets that range from $10K to $100K. The model is designed to provide detailed results for the identified optimal solutions which include an action report that lists the details of the recommended upgrade measures. The new and novel capabilities of the developed optimization model provide needed support for decision makers and building owners in their ongoing efforts to minimize the negative environmental impacts of their existing buildings while complying with their limited upgrade budgets. Future expansion of the model and more in-depth analysis are needed to further study the impact of feasible upgrade measures of the building envelope such as type of insulation, windows, and doors to consider their effects on reducing the building negative environmental impacts especially for buildings that have their energy consumption dominated by HVAC systems. References Abdallah, M., El-Rayes, K., and Liu, L. (2014). “Optimal Selection of Sustainability Measures to Minimize Building Operational Costs.” Construction Research Congress (CRC), American Society of Civil Engineers, Atlanta, GA., 2205–2213. Aytug, H., and Koehler, G. J. 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S., Lv, S., and Wu, W. (2013). “Optimization method for building envelope design to minimize carbon emissions of building operational energy consumption using orthogonal experimental design (OED).” Habitat International, 37, 148–154.   323-10  5th International/11th Construction Specialty Conference 5e International/11e Conférence spécialisée sur la construction    Vancouver, British Columbia June 8 to June 10, 2015 / 8 juin au 10 juin 2015   MINIMIZING GREENHOUSE GAS EMISSIONS AND WATER CONSUMPTION OF EXISTING BUILDINGS Moatassem A. Abdallah1,3, Khaled A. El-Rayes2 and Caroline M. Clevenger1 1 University of Colorado Denver, USA 2 University of Illinois at Urbana-Champaign, USA 3 moatassem.abdallah@ucdenver.edu Abstract: Buildings are responsible for 38% of all carbon emissions and 14% of water consumption in the United States. These negative environmental impacts can significantly be reduced by implementing green upgrade measures such as energy-efficient lighting and HVAC systems, motion sensors, photovoltaic systems, and water-saving plumbing fixtures. Building owners in the public and private sectors often search for an optimal set of upgrade measures that is capable of minimizing the negative environmental impacts of their buildings. This paper presents the development of an optimization model that is capable of identifying optimal selection of building upgrade measures to minimize greenhouse gas emission and water consumption of existing buildings while complying with limited upgrade budgets. The model is developed in four main development steps: metrics identification step that quantifies greenhouse gas emissions and water consumption of existing buildings; model formulation step that formulates the model decision variables, objective function, and constraints; implementation step that executes the model computations and specifies the model input and output data; and validation step that evaluates the model performance using a case study of an existing building. The results of the model illustrate its new and unique capabilities in providing detailed results, which include specifications for the recommended upgrade measures, their location in the building, and required upgrade cost to minimize greenhouse gas emissions and water consumption of existing buildings.  1 INTRODUCTION Buildings in the United States are responsible for significant GreenHouse Gas (GHG) emissions due to significant percentage of electricity consumption (72%), energy consumption (39%), and water consumption (13%)  (U.S. Environmental Protection Agency 2009). These GHG emissions due to energy and water consumption contribute to negative environmental impacts including, global warming, ozone depletion, and air pollution (ICF Incorporated 1995; U.S. Environmental Protection Agency 2009, 2012; United Nation Environment Program 2007). The negative environmental impacts of buildings can be reduced by implementing sustainable measures such as energy efficient building fixtures and equipment, water-saving plumbing fixtures, and installing renewable energy systems. These sustainable measures require high upgrade costs and decision makers are often confronted with a challenging task to identify optimal selection of building upgrade measures within their upgrade budget. In order to support decision makers in this challenging task, there is a pressing need to develop optimization models that are capable of identifying optimal selection of building upgrade measures to minimize negative environmental impacts of existing buildings within available budgets. 323-1 Several systems have been developed and studies conducted to estimate the negative environmental impacts of buildings during their construction, operation and demolishing such as Building for Environmental and Economic Sustainability (BEES) (Lippiatt et al. 2010), ATHENA impact estimator (The ATHENA Institute 2013), and envest2 (Building Research Establishment 2003). Other studies focused on evaluating the implementation of various sustainability measures such as energy-efficient lighting systems, energy-efficient HVAC systems and heat pumps (Bloomquist 2001; Chiasson 2006; Das et al. 2013; International and Conference 2003; Narendran and Gu 2005; Phetteplace 2007; RUUD LIGHTING 2010), renewable energy systems (Chapman and Wiczkowski 2009; James et al. 2011; Matthews et al. 2004; State Energy Conservation Office 2006), and water-saving plumbing fixtures (GAO 2000). Furthermore, several optimization models have been developed to minimize existing buildings’ operational costs (Abdallah et al. 2014), identify optimal selection of structural and architecture design of new buildings (Bichiou and Krarti 2011; Fialho et al. 2011), and identify optimal decisions of building renovations (Brandt and Rasmussen 2002; Juan et al. 2010; Kaklauskas et al. 2005). Despite the significant contribution of the existing research studies, limited or no optimization models exist that are capable of selecting building upgrade measures of existing buildings in order to minimize their negative environmental impacts of greenhouse gas (GHG) emissions and water consumption simultaneously. Furthermore, limited or no optimization models exist that consider various sustainability measures of building fixtures and equipment, renewable energy systems, and plans of managing solid waste simultaneously in order to minimize the negative environmental impacts of existing buildings while complying with a user-specified upgrade budget and building operational performance.  2 RESEARCH OBJECTIVE The objective of this research study is to develop an optimization model that is capable of minimizing negative environmental impacts of existing buildings. This optimization model is designed to provide the optimal selection of building upgrade measures to minimize GHG emissions and water consumption of existing buildings while complying with available upgrade budgets and specified building operational performance. This optimization model is expected to support decision makers and building owners in their ongoing efforts to minimize negative environmental impacts of their buildings by optimal allocation of their budgets. The optimization model is developed in four main steps: (1) metric identification step, which quantifies the negative environmental impacts of buildings; (2) model formulation step that formulates the model decision variables, objective function, and constraints; (3) implementation step that executes the model computations using Genetic Algorithms (GAs); and (4) evaluation step that validates the model performance using a case study of an existing building. The following sections describe the development steps of the optimization model to illustrate the capabilities of the model and demonstrate its use.  3 OPTIMIZATION MODEL DEVELOPMENT 3.1 Metric Identification  The optimization model is developed to quantify the negative environmental impacts of existing building in terms of GHG emissions that result from buildings energy consumption; energy use for water extraction, treatment, distribution, and wastewater treatment; and buildings solid waste (ENVIRON International Corporation 2013; Flager et al. 2012; ICLEI - Local Governments for Sustainability USA 2010, 2012; Kwok et al. 2012; Liu et al. 2013; Ordóñez and Modi 2011; Safaei et al. 2012; TranSystems|E.H. Pechan 2012; U.S. Environmental Protection Agency 2006; USGBC 2014a; Zhu et al. 2013); and water consumption from plumbing fixtures.   The GHG emissions from building operation consist of carbon dioxide (CO2), nitrous oxides (N2O), methane (CH4), and ozone (O3) (TranSystems|E.H. Pechan 2012). The global warming potential factors that are developed by the Intergovernmental Panel of Climate Change (IPCC) can be used to represent all GHG emissions in terms of equivalent quantities of CO2 emissions (Intergovernmental Panel on Climate Change 2007). The GHG emissions are quantified in the developed model based on the calculated (1) energy consumption of the building; (2) energy use during water extraction, treatment, 323-2 distribution, and waste water treatment; and (3) fugitive emissions of waste water, and solid waste. The GHG emissions of energy consumption are calculated based on the building electricity and natural gas consumption and the location of the building.  This accounts for the types of plants that are used to generate energy for the building and associated average transmission losses. For major electricity grids in the United States, the Environmental Protection Agency provides energy emission factors and average transmission loss percentages, which can be used to estimate emissions of energy use in buildings (TranSystems|E.H. Pechan 2012). To estimate GHG emission of buildings, electricity and natural gas need to be calculated. The developed optimization model calculates electricity and natural gas consumption of the buildings using energy simulation software packages such as QUick Energy Simulation Tool “eQUEST” (U.S. Department of Energy 2013).  In addition to the GHG emissions that are created by the direct building energy consumption, the water consumption of the building create additional GHG emissions due to energy used in water extraction, conveyance and supply, treatment, and distribution. These emissions can be calculated based on annual building water consumption; and energy intensity of water extraction, water supply and conveyance, water treatment, and water distribution (ICLEI - Local Governments for Sustainability USA 2012). Similarly, the GHG emissions from wastewater treatment can be calculated based on annual building waste water; and energy intensity of wastewater collection, aerobic digesters for wastewater treatment, lagoons for wastewater treatment, attached growth of wastewater treatment, and nitrification or nitrification/denitrification of wastewater treatment (ICLEI - Local Governments for Sustainability USA 2012).  Another source of GHG emissions in existing buildings is calculated based on solid waste sent to landfill, combustion, composition, or recycling. The United Stated Environmental Protection Agency provides emission factors for each of these methods of managing solid waste (U.S. Environmental Protection Agency 2006). Accordingly, the annual equivalent carbon dioxide of solid waste in buildings can be calculated in the model based on the weight of each solid waste material and the associated emission factor calculated according to solid waste management method. According to all the aforementioned sources of GHG emissions, the model calculates the total equivalent emissions of existing buildings by aggregating all sources of GHG emissions. Plumbing fixtures are responsible for the majority of water consumption in buildings. They include water faucets, showerheads, kitchen sinks, urinals, and toilets. Building water consumption can be calculated in the developed model based on type of building, type of plumbing fixtures, and number of occupants according the guidelines of the LEED rating system for existing buildings (USGBC 2014b). 3.2 Model Formulation The decision variables of the optimization model are designed to represent all feasible alternatives of building fixtures and equipment that consume energy or water using integer decision variables such as lighting fixtures and bulbs, HVAC systems, water heaters, refrigerators, vending machines, hand dryers, and water plumbing fixtures. The model is also designed to integrate energy saving measures using integer decision variables such as motion sensors, solar panels, inverters, and percentage of renewable energy that can be generated at the building site. In addition, the model is designed to integrate plans of managing solid waste using integer decision variables that represents the disposal of each solid waste using landfill, recycling, combustion, or composition.      The objective function of this optimization model minimizes the negative environmental impacts of existing buildings by minimizing GHG emissions and water consumption. The model accounts for GHG emissions and water consumption of buildings using Building Environmental Performance Index (BEPI). This index ranges from 0.0 which represent a fully sustainable building to 1.0 which represent no reduction in negative environmental impacts of the building as shown in Equation (1).      (1) 323-3 Where:  is building environmental Performance index;  is building GHG emissions after implementing upgrade measures;  is existing building GHG emissions; is building water consumption after upgrade measures; is existing building water consumption; and  are relative importance weights of GHG emissions and water consumption, respectively.  To ensure the practicality of this optimization model, it is designed to comply with two main constrains: (1) building performance constraints, and (2) upgrade budget constraint. The building performance constraint is integrated in the model to ensure that the required operational performance of the building will be maintained after replacing its fixtures and equipment, including space heating and cooling, water heating capacity, and light levels. The upgrade budget constraint is integrated in the model to ensure that the cost of upgrading the building fixtures and equipment, installing renewable energy systems, and managing solid waste will not exceed the specified upgrade budget.  3.3 Model Implementation The computations of the optimization model are executed using Genetic Algorithms (Gas) due to its (1) efficiency in modeling the optimization problem with the least number of decision variables, (2) capability to model non-linearity and step changes in the objective function and constrains that are caused by replacing building fixtures and equipment, (3) capability of identifying optimal solution within reasonable computational time  (Aytug and Koehler 1996; Goldberg 1989; Pendharkar and Koehler 2007).   The computation procedure of the developed model starts by searching an integrated databases in order to identify feasible replacements of HVAC systems and water heaters. The model then creates eQuest input files of feasible replacements and sends them to eQuest simulation environment to calculate their energy consumption. The model then stores the calculated energy consumption of HVAC equipment and water heaters in a database where it can be used during the optimization process. The GA computations start by generating random selection of building upgrade measures which represent the initial population. The fitness of this initial population is evaluated based on the index of negative environmental performance index and the model constraints. Solutions that satisfy all the constraints and achieve low values of negative environmental performance index are classified as solutions with high fitness values. On the other hand, solutions that achieve high values of negative environmental performance index or do not satisfy the model constraints are classified as solutions with low fitness values or infeasible solutions, respectively. Solutions with high fitness are then ranked based on their index of negative environmental performance index where the GA operators of selection, crossover, and mutation are applied to generate a new set of population. This process is iteratively repeated until no further improvements are achieved within a predefined number of iterations. It should be noted that the initial population of the model is set based on the GA string and possible values of the model decision variables (Reed et al. 2000; Thierens et al. 1998). The developed optimization model is integrated with databases of building fixtures and equipment, components of renewable energy systems, and various types of building solid waste. These databases are designed to include general product data, cost data, energy and water characteristics, and physical characteristics of building fixtures, and components of renewable energy systems, including lighting bulbs and fixtures, motion sensors, HVAC equipment, water heater, hand dryers, vending machines, refrigerators, PCs, water coolers, solar panels, solar inverters, water faucets, urinals, and toilets. The databases also include data on energy intensity of water extraction, conveyance, treatment, distribution, and waste water treatment; and emission factors of energy consumption and solid waste according to the location of buildings in the United States. For example, the equivalent emission factors of electricity consumption, electricity savings, and average transmission losses of all electricity grids are stored in the model databases, as shown in Table 1. 323-4 Table 1: Sample emission factors and average transmissions losses of electricity grids in USA (TranSystems|E.H. Pechan 2012) eGRID subregion name  Equivalent CO2 emission rate (lb/MWh) Equivalent non-baseload CO2 emission rate (lb/MWh) Power grid average transmission loss (%) SERC Virginia/Carolina 1,041.73 1,686.09 5.82% RFC West 1,528.76 2,012.22 5.82% WECC California 661.20 995.85 8.21% WECC Southwest 1,196.58 1,190.97 8.21% 4 CASE STUDY A rest area building was analyzed and optimized by the developed optimization model in order to illustrate the model capabilities and demonstrate its use. This rest area building is located in Illinois and it was selected due to its high levels of negative environmental impacts caused by its continuous operation throughout the year and its inefficient energy and water fixtures. The building was built in 1989 and renovated in 1992 with a total area of 2500 square foot. This rest area building includes men’s and women’s bathrooms, lobby, vending area, travel information desk, storage rooms, mechanical room, attic, and detached small garage. The rest area also has parking lots for visitors that accommodate cars and semi-trucks. The major contributors of energy consumption in the building include interior and exterior lighting systems, water heater, HVAC systems, six vending machines, four hand dryers, five water coolers, PC, surveillance system, and five code blue emergency phones. The major contributors of water consumption in the building include eight toilets, two urinals, and six water faucets.  In order to minimize the negative environmental impacts of the rest area building, the optimization model requires input data of (1) building characteristics, including building size, construction materials, air infiltration, doors and windows, operational schedule, allocation of building activities, temperature set points, and airflow, as shown in Table 2; (2) characteristics of building equipment and fixtures which can be selected from the model databases, as shown in Table 3; (3) amounts of building solid wastes, as shown in Table 4; and (4) importance weights of building negative environmental impacts which were specified at 80% and 20% for GHG emissions and water consumption, respectively. It should be noted that the importance weights can vary from one decision maker to another, and the model enables them to specify their own weights accordingly.   Table 2: Sample input data of the building characteristics  Description Building characteristics Building envelop (roof surfaces) Wood advanced frame 24’’ with dark brown shingles roofing and R-30 batt. Building envelop (above grade walls) 6’’ CMU with brick exterior finishing and perlite filling  Building infiltration 1.0 ACH for perimeter and core Building interior construction  Lay-in acoustic tiles flooring with R-11 batt, and mass interior walls.  Building operation schedule 24 hours  323-5 Table 3: Sample input data of the building fixtures  Building fixture   Input Data  Location Feasible Alternatives Description Number of fixtures Working hours per day  Men’s bathroom – Set 2  1 Square fluorescent fixture with 2 T8 U-shaped lamps of 28 W and 2265 lumens 3 24   2 Square fluorescent fixture with 2 T12 U-shaped lamps of 34 W and 2279 lumens   … ….   20 Square fluorescent fixture with 2 T12 U-shaped lamps of 35 W and 2235 lumens  Building HVAC System # 1  1 Electric HVAC system  1 24   2 Gas Energy Star rated HVAC system   3 Ground-source heat pump   4 Electric Energy Star rated HVAC system   Men’s bathroom – hand dryers  1 Hand dryer - 2300 W and 30 sec drying time 2 Per use   2 Hand dryer - 1100 W and 12 second drying time   3 Hand dryer - 1100 W and 15 second drying time   ….. …..   9 Touchless paper towel dispenser  Women’s bathroom - toilets  1 Electronic flushing toilet with 3.5 gallons per flush 8 Per use   2 Electronic flushing toilet with 1.6 gallons per flush   3 Electronic flushing toilet with 1.28 gallons per flush   Table 4: Sample of managing solid waste at the rest area building  Solid Waste Annual Weight (ton) Managing solid waste Aluminum cans 0.1 Landfill Newspaper 0.3 Landfill Food scraps 0.5 Landfill Mixed paper 0.3 Landfill Mixed plastics 0.3 Landfill The optimization model was used to minimize the negative environmental impacts of the rest area with various upgrade budgets that ranged from $10K to $100K. The model was able to identify the optimal upgrade decisions for all the specified upgrade budgets, as shown in Figure 1. For example, solution (a) in Figure 1 identified by the model as an optimal solution for an upgrade budget of $50K, and it provides a moderate reduction in the negative environmental performance index of (BEPI = 0.519) with an upgrade cost of $49,673. On the other hand, solution (b) is identified by the model as an optimal solution for an upgrade budget of $100K, and it provides minimum negative environmental performance index of (BEPI = 323-6 0.397) that caused reduction in GHG emissions by 58% and water consumption by 69%, as shown in Figure 1.  Figure 1: Results of minimizing negative environmental impacts of the rest area building The model is designed to provide an action report for the generated optimal solutions which include detailed information of all the recommended building upgrade measures. For example, the model generated the recommended upgrade measures and solid waste management plans for optimal solution (a) in Figure 1 as shown in Table 5 and Table 6, respectively. The results of the model identify the optimal selection of building upgrades based on an identified upgrade budget which helps decision makers and building owners in their ongoing task of maximizing the sustainability of their building while complying with their available budgets.   Table 5: Sample recommended replacements of the building fixtures for upgrade budget of $50K Room Recommended Replacements Men's & women’s bathrooms, lobby, & information rooms Replace 32 existing T12 U-shaped lamps of 35 W, 2235 lumens, and 18,000 hours life expectance with 22 T8 U-shaped lamps of 28 W, 2380 lumens, and 30,000 hours life expectancy.  Men's & women’s bathrooms, information, vending storage, & garage Replace 28 existing longitudinal fluorescent T12 lamps of 34 W, 2280 lumens, and 20,000 hours life expectance with 10 longitudinal fluorescent T8 lamps of 25 W, 2280 lumens, and 40,000 hours life expectancy. Building Replace existing HVAC equipment with EnergyStar rated gas furnace and EnergyStar rated condensing units. Vending storage Replace existing fridge with energy efficient unit. Men's & women’s bathrooms Replace existing hand dryers of 2300w and 30 sec. drying time with touchless paper towel dispenser. Building Install photovoltaic system to generate 8.5% of the total building energy demand. Men's & women’s bathrooms Replace 6 existing water faucets of 1.5 gallons per minute with electronic faucets of 0.5 gallons per minute. Men's & women’s bathrooms Replace 8 existing toilets of 3.5 gallons per flush with water efficient toilets of 1.28 gallons per flush. Men's bathroom Replace 2 existing urinals of 1.6 gallons per flush with water efficient urinals of 0.125 gallons per flush. Men's & women’s bathrooms Install motion sensors to turn off the lighting automatically in men’s and women’s bathrooms when there is no occupants.    323-7 Table 6: Sample recommendations of managing solid waste for upgrade budget of $50K  Solid Waste Recommendations Aluminum cans Collect aluminum cans and send them to recycling Newspaper Collect newspapers and send them to recycling Food scraps Collect food scraps and send them to composting Mixed paper Collect mixed paper and send it to recycling Mixed plastics Collect mixed plastics and send them to recycling 5 SUMMARY AND CONCLUSIONS  This paper presents the development of an optimization model that is capable of minimizing negative environmental impacts of existing buildings by minimizing their GHG emissions and water consumption. The model is designed to identify the optimal selection of building upgrade measures while complying with a specified upgrade budget and preferred building operational performance. The model was developed in four main steps: metric identification step, model formulation step, implementation step, and evaluation step. The metrics identification step identified novel metrics for quantifying the negative environmental impacts of existing buildings in terms of GHG emissions and water consumption. GHG emissions were calculated based on energy consumption, energy use of water extraction, treatment, distribution, and wastewater treatment; and buildings solid waste. The formulation step identified the model decision variables, objective function, and constraints. The model is designed to include decision variables that have impact on GHG emissions and water consumption including building fixtures and equipment, renewable energy systems, and water plumbing fixtures. The objective function is designed to minimize GHG emissions and water consumption using an index that account for these impacts using importance weights. The model integrated a number of constraints to comply with specified upgrade budgets and building operational performance.  The model implementation step include the execution of the model computations using Genetic Algorithms (GAs) and the development of databases to facilitate input and output data of the model. The evaluation step validated the model performance using a case study of a rest area building. The model was able to identify the optimal selection of building upgrade measures for various budgets that range from $10K to $100K. The model is designed to provide detailed results for the identified optimal solutions which include an action report that lists the details of the recommended upgrade measures. 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(2013). “Optimization method for building envelope design to minimize carbon emissions of building operational energy consumption using orthogonal experimental design (OED).” Habitat International, 37, 148–154.   323-10  Presenter: Caroline Clevenger, PhDJune 8th 2015Authors:Moatassem Abdallah, University of Colorado at DenverKhaled El-Rayes, University of Illinois at Urbana-ChampaignCaroline Clevenger, University of Colorado at DenverILLINOISUNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN&1Outline IntroductionResearch DevelopmentCase StudyConclusions 2Motivation71%40% 39% 30%13%(USGBC 2012) Buildings in U.S. account forThis requires buildings to promote - Energy efficiency - Water conservation- Recycling materials- Longer lifecycle3Sustainability and Green Building Measures  Building owners are demanding sustainability and green measures in their buildingsEnergy Efficient Measures Water saving measures Renewable Measures4Problem Statement Decision makers are often confronted with a challenging task to identify optimal selection of building upgrade measures within their upgrade budgetUpgrade  Cost GHG and Water consumption5???? ? ? ???5Objective Identifying the optimal selection of building upgrade measures to minimize the carbon emissions and energy consumption of existing buildings.Building Environmental Performance Index (BEPI)GHG and Water Consumption6Model Development PhasesMetrics Identification Formulation Implementation EvaluationGenetic AlgorithmsModel Databases7Metrics Identification Extraction TreatmentConveyanceWater Treatment & DistributionDistribution81. Greenhouse gas (GHG) emissionsWaste water treatmentRecycleCombustionLandfillCompositionSolid Waste Management Electricity Natural GasLossesEnergy ProductionLossesLosses2. Water Consumption Plumbing fixtures8Model Formulation - Decision Variables Building fixtures and equipment𝑋𝑋𝑖𝑖,𝑗𝑗Integer decision variablesLocation of fixture or equipmentType of fixture or equipment9Model Formulation - Decision Variables Percentage of renewable electricity and solid waste management  10Model Formulation - Objective Function Minimizing Building Carbon Emissions𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩 =𝑊𝑊𝐺𝐺𝐺𝐺𝐺𝐺 ∗ 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑅𝑅𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑒𝑒 +𝑊𝑊𝑊𝑊𝑊𝑊 ∗𝑊𝑊𝑊𝑊𝑅𝑅𝑊𝑊𝑊𝑊𝑒𝑒Building Environmental Performance IndexRatio of building GHG emissions before and after implementing upgrade measuresImportance weight of GHG emissionsImportance weight of water consumptionRatio of building water consumption before and after implementing upgrade measuresReducedExisting11Model Formulation - Constraints  Upgrade budget constraint Operational performance constraints Light luminanceSpace heating and cooling capacitiesWater heating capacity Photovoltaic system constraints12Model Implementation Model input Data Building geometry and characteristics. Characteristics of building fixtures and their operational schedule. Energy consumption and rates of a previous year. Importance weights of GHG emissions and building water consumption Upgrade budget. Model Output Data Required upgrade cost and reeducation in GHG emissions and water consumption of the optimal solutions. Action report of recommended upgrade measures.13 Model Databases Model Implementation14 Model Databases Model Implementation Model Databases15 Energy Characteristics  Physical Properties  Lamp ID #  Fixture Group ID #  Brand Name  Model Name  Lamp Type  Mean Lumens (Lumens)  Color Temperature (Kelvin )  Color Rendering Index (CRI)  Life Expectancy (hours) Mercury (mg) Lamp Cost ($)  Installation Cost ($)  Annual Maintenance Cost($)  Consumption (Watt)  Length (ft)  Vendor Name  Websie  Location  Updated on 1 4 Philips F32T8/TL950 T8 1860 5000 98 20000 3.5 $7 $0 $0 32 4 1000Bulbs bulbs.com/product/4722/F- Online 12-Jul-12 General Product Characteristics  Cost Data  Vendor Characteristics Time CharacteristicsPhysical PropertiesFixture ID #Fixture Group ID # Grpoup Name Fixture NameFixture SocketNumber of Bulbs per FixtureBalast Effect on Luminance (%)Life Expectancy (years)Fixture Cost ($)Installation Cost ($)Annual Matenience Cost ($)Consumption (Watt)Additional Consumption due to Balast Efficiency (watt)Power Factor Dimensions (length)Vendor Name Websie LocationUpdated on8 4Longitudinal fluorescent lamp, 4 feet with bi-pin socketTCP WL4WA232UNIN - Wet Location Fluorescent Fixture (2) Lamp bi-pin socket 2 0% 10 81.41 60 0 0 0 0.97 4 Online http://www.1000bulbs.c Online 20-Jul-13Product Characteristics Cost Vendor CharacteristicsEnergy Characteristics15Model Implementation Model computations were executed using GAsSearching feasible alternativesModel DatabasesGenerating initial populationCalculate BEPIEvaluating fitness of solutionsGenerating new pop using GA operatorsYesConvergenceNo16Evaluation  Case Study Description Built in 1980  839,000 annual visitors Building size is 3575 square feet Major contributors of energy and water consumption Exterior and interior lighting HVAC system Water heater Hand dryers Vending machines Faucets, urinals, and toilets17Evaluation – Model Results Model Results Decision Variables: 42Search space: 6.6*103018Evaluation – Model ResultsRoom Recommended ReplacementsMen's & women’s bathrooms, lobby, & information roomsReplace 32 existing T12 U-shaped lamps of 35 W, 2235 lumens, and 18,000 hours life expectance with 32 T8 U-shaped lamps of 28 W, 2380 lumens, and 30,000 hours life expectancy. Building Replace existing HVAC equipment with EnergyStar rated gas furnace and EnergyStar rated condensing units.Vending storage Replace existing fridge with energy efficient unit.Men's & women’s bathrooms Replace existing hand dryers of 2300w and 30 sec. drying time with touchless paper towel dispenser.Building Install photovoltaic system to generate 8.5% of the total building energy demand.…. …. Model Results (50K upgrade budget) Sample of Model Recommendations19Evaluation – Model Results Model Results (50K upgrade budget) Sample of Solid Waste Management RecommendationsSolid Waste RecommendationsAluminum cans Collect aluminum cans and send them to recyclingNewspaper Collect newspapers and send them to recyclingFood scraps Collect food scraps and send them to compostingMixed paper Collect mixed paper and send it to recyclingMixed plastics Collect mixed plastics and send them to recycling20Conclusions An optimization model was developed that is capable of identifying the optimal selection of sustainability measures to minimize GHG and water consumption of existing buildings.  The application of this optimization model can support decision makers in their ongoing efforts to  Promote the use of sustainable measures Reduce building energy consumption, carbon emissions, and water consumption Minimize annual operational costs and environmental impacts of existing buildings21Conclusions  This model is part of ongoing research which include LEED Optimization Model Environmental optimization model Economic optimization model Sustainability optimization model22Thank YouQuestionsILLINOISUNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN23

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