Cooperative Water Network System to Reduce Carbon Footprint

Jul 19, 2008 - Much effort has been made in reducing the carbon footprint to mitigate climate change. However, water network synthesis has been focuse...
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Environ. Sci. Technol. 2008, 42, 6230–6236

Cooperative Water Network System to Reduce Carbon Footprint SEONG-RIN LIM† AND J O N G M O O N P A R K * ,‡ Department of Bioproducts and Biosystems Engineering, University of Minnesota, 1390 Eckles Avenue, St. Paul, Minnesota 55108, and Advanced Environmental Biotechnology Research Center, Department of Chemical Engineering, School of Environmental Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Pohang 790-784, South Korea

Received January 24, 2008. Revised manuscript received May 18, 2008. Accepted June 12, 2008.

Much effort has been made in reducing the carbon footprint to mitigate climate change. However, water network synthesis has been focused on reducing the consumption and cost of freshwater within each industrial plant. The objective of this study is to illustrate the necessity of the cooperation of industrial plants to reduce the total carbon footprint of their water supply systems. A mathematical optimization model to minimize global warming potentials is developed to synthesize (1) a cooperative water network system (WNS) integrated over two plants and (2) an individual WNS consisting of two WNSs separated for each plant. The cooperative WNS is compared to the individual WNS. The cooperation reduces their carbon footprint and is economically feasible and profitable. A strategy for implementing the cooperation is suggested for the fair distribution of costs and benefits. As a consequence, industrial plants should cooperate with their neighbor plants to further reduce the carbon footprint.

1. Introduction Climate change has been a big issue faced by sustainable development. Much effort has been made to reduce the emissions of greenhouse gases (1, 2). For instance, renewable energy sources are being developed and used to replace fossil fuels in the electricity and transportation industries (3), and processes and systems are being improved to enhance their energy efficiency and mitigate the emissions of greenhouse gases (4). The Annex I Parties in the Kyoto Protocol should commit to reduce greenhouse gases by an average of 5.2% below the year 1990 levels during the 2008-2012 period (5). The carbon footprint is used for CO2 abatement efforts as an indicator to quantify the global warming potential (GWP) of total greenhouse gases incurred throughout the life cycle and associated supply chains. The global warming intensity of transportation fuels has been measured with the carbon footprint (6). New technologies, recycling, and demand management have been analyzed to reduce the carbon footprint of the whole copper cycle (7). The effects of international trade and income level on the carbon footprint of American households have been evaluated to design the policies needed to reduce consumer impacts on * Corresponding author phone: +82-54-279-2275; fax: +82-54279-2699; e-mail: [email protected]. † University of Minnesota. ‡ Pohang University of Science and Technology. 6230

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global warming (8). Companies are trying to minimize the carbon footprint of products, systems, processes, and services in the stages of research and development, design, production, transportation, use, and disposal in the context of environmental management. Leading retailers in the United Kingdom have used the carbon footprint for their business: they measure the carbon footprint of products and inform and drive customers to choose a product with a small carbon footprint, in line with environmental labeling (9). This marketdriven force can accelerate the conversion of systems and processes used for production into environmentally friendly ones to minimize the carbon footprint of final products. The carbon footprint of water supply systems has significant impacts on the carbon footprint of final products. A water supply system is an essential utility required for operating systems and processes in almost all industrial plants. The GWPs from all water supply systems associated with the supply chains of a final product are accumulated into the carbon footprint of the final product, as shown in Figure 1. Moreover, the carbon footprint of a water supply system in a plant has wide impacts on those of the various products produced in the plant. Therefore, it should be mentioned that water supply systems are important targets to be improved with a high priority in order to effectively reduce the carbon footprint of products, and the carbon footprint of water supply systems should be minimized to produce environmentally competitive products. Traditional water network synthesis has focused on minimizing the freshwater consumption and associated costs of a water supply system via economic aspects. The synthesis is defined as the optimization of a water supply system by connecting water sources (e.g., wastewater and/or treated wastewater) to water sinks (e.g., unit processes) for water reuse in case the properties of the water sources meet the water quality requirement of the water sinks (10). Most previous studies have focused on solving mathematical optimization models such as nonlinear programming (NLP) and mixed-integer nonlinear programming with deterministic approaches to find global optima minimizing freshwater consumption or cost, because of nonconvexities from bilinear variables in the mass balances of the models (10–12). Genetic algorithms have been applied as a stochastic approach to search global optima (13). Pinch analysis technologies have been studied to graphically analyze and identify the minimum freshwater consumption flowrate of a water supply system and then to heuristically generate a water network system (WNS) (10). A graphical method for pinch analysis has been developed to target the minimum flowrate and design a WNS simultaneously (14). Life-cycle costing has been employed for evaluating the economic feasibility and profitability of a WNS (15), and for developing the mathematical optimization model to synthesize an economically friendly WNS (16). The paradigm of sustainable development has driven water network synthesis to minimize environmental impacts as well as economic costs. Multiobjective optimization has been studied to minimize a total annualized cost and environmental impacts (17). Life-cycle assessment and lifecycle costing on a WNS have been studied to evaluate the environmental and economic performance of the WNS (18). However, the effort to reduce the carbon footprint of a WNS has not been performed in water network synthesis. Industrial plants should explore neighbor plants and cooperate with one another to find more and better opportunities to reduce the carbon footprint and economic costs. Until now, many plants have made much effort within the system boundary of each individual plant to reduce 10.1021/es800243e CCC: $40.75

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FIGURE 1. Effect of greenhouse gases (GHG) from water supply systems existing throughout the supply chains on the carbon footprint of a final product. environmental impacts and economic costs using water network synthesis. However, limits of further improvement have been reached as a result of the full utilization of all of the possible opportunities to increase water reuse within each plant. It should be mentioned that the opportunities can be increased by extending the system boundary of water network synthesis and including more unit processes with various water qualities and flowrates. Therefore, the cooperation of industrial plants for extending the system boundary can break through the limits by creating new opportunities to reduce the carbon footprint and economic costs. The possibility for successful cooperation through networking can be increased by determining the fair distribution of economic and environmental costs and benefits for each participating industrial plant. However, the fair distribution on a quantitative basis is impossible because a network system for plants is such an integrated one for interdependency that the network system cannot be separated into an individual part of each plant. The difference between the costs and benefits of plants from the cooperation can induce the dissatisfaction of some plants with more costs or less benefits. These problems can cause a delay and even nullification of the cooperation because of the failure of mutual agreement for the distribution of economic and environmental costs and benefits. Therefore, a solution of these problems is required for the practical implementation of the cooperative WNS to reduce the total carbon footprint of plants. The objective of this study is to illustrate that the cooperation of industrial plants is needed to reduce the total carbon footprint of their water supply systems. Two plants are employed to demonstrate the effect of the cooperation on the carbon footprint and economic cost of their water supply systems. A mathematical optimization model to minimize the GWP of a WNS is developed to synthesize individual and cooperative WNSs: the individual WNS consists of two WNSs separated for each plant, and the cooperative WNS is one WNS integrated with the unit processes in the two plants. The configuration, design characteristics, the GWPs of principal contributors, and the carbon footprint of the cooperative WNS are compared to those of the individual WNS. The economic performance of the cooperative WNS is evaluated to examine whether the

cooperative WNS is economically feasible and profitable. A strategy for practically implementing the cooperative WNS is suggested as a solution for the fair distribution of the costs and benefits incurred in constructing and managing the cooperative WNS.

2. Methods 2.1. Mathematical Optimization Model. The mathematical optimization model to minimize the carbon footprint of a WNS was developed by formulating the GWPs of principal contributors to its carbon footprint and the mass balances and constraints based on a superstructure model. 2.1.1. Superstructure Model. A superstructure model was used to develop the mathematical optimization model to minimize the carbon footprint of a WNS, as shown in Figure 2. The superstructure model is used to take into account all of the opportunities affecting the value of the objective function and obtain all of the feasible solutions of the mathematical model. This superstructure model includes all possible interconnections between water sources and sinks, such as those from the outlet of a unit process to the inlet of the others, as well as between freshwater sources and unit processes, to fully utilize opportunities of water reuse. However, local recycling from the outlet to the inlet within a unit process was not allowed to avoid excessive electricity consumption and costs derived from pumping with a high flowrate (15). It was assumed that a mixer combines many streams into a single stream and that a splitter divides one stream into all possible streams to water sinks. 2.1.2. Principal Contributors and Their Unit Global Warming Potentials. The GWPs of the principal contributors to the carbon footprint of a WNS were formulated in the objective function of the mathematical optimization model to minimize its carbon footprint. The principal contributors are the primary targets, so-called “hot spots”, and are focused on to effectively minimize the carbon footprint. The employment of principal contributors simplifies a mathematical optimization model by excluding minor contributors, which makes it easy to apply the model to the real situations of industrial plants. The consumptions of freshwater and electricity were selected as the principal contributors for the model with respect to the results of a life-cycle assessment (LCA) on a WNS: the proportion of the GWP of freshwater VOL. 42, NO. 16, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Superstructure model used to synthesize a cooperative water network system (WNS).

FIGURE 3. Concept of life-cycle impact assessment to evaluate the total amount of greenhouse gases (GHGs) associated with a reference flow of water and electricity: GHGs are emitted throughout the supply chains of water and electricity. consumption to the total GWP of the WNS was found to be account in this model because the total contaminant loads 68%, and that of electricity consumption was 28% (18). in wastewater are not changed through water network The unit GWPs of the principal contributors were evalusynthesis. In other words, the GWPs from wastewater were ated on the basis of LCA databases to be incorporated into regarded as a baseline for this model. the mathematical optimization model. The LCA data take The objective function is the sum of the GWPs of the into account the GWP associated with the supply chains and principal contributors, which is minimized to generate a WNS life-cycle stages of a principal contributor. The reference flows with the smallest carbon footprint. The carbon footprint of of the principal contributors were set to 1 m3 of industrial a WNS in the model is expressed as the amount of all of the water, and 1 kW · h of electricity in order to calculate their GWPs of the principal contributors generated per hour. The unit GWPs. The unit GWP of the consumption of industrial carbon footprint of the objective function is minimized by water was 0.302 kg CO2-eq/m3 according to the Ecoinvent optimizing the tradeoff between the GWPs from the conv.1.2 database (19). These data include the GWP associated sumption of industrial water and from electricity consumpwith infrastructure and energy use for water treatment and tion. The GWP of the consumption of industrial water is transportation to the end user, as shown in Figure 3a. The calculated from its flowrate and unit GWP. The GWP of unit GWP of electricity consumption was 0.495 kg CO2-eq/ electricity consumption for pumping is estimated from its kW · h according to the LCA results from the PASS (20). This unit GWP and the power requirement calculated from the data includes the extraction of raw materials, transportation, flowrate of industrial water and the pressure requirement. electricity generation, and waste treatment, as shown in The pressure is the sum of the head loss through the pipeline Figure 3b, and the electricity generation mix consists of 43.3% and the additional head required to meet water pressure at nuclear, 37.5% coal, 12.3% natural gas, 5.1% oil, and 1.8% the end of the pipeline. The head loss is calculated by using hydro. the Darcy-Weisbach equation (21). The optimal velocity in 2.1.3. Mathematical Formulation. The mathematical opthe pipeline is assumed to be proportional to the flowrate timization model consists of (1) an objective function to simplify the mathematical model (12). The mass balances generated by formulating the total GWP of the principal and constraints are formulated on the basis of the supercontributors and (2) mass balances and constraints needed structure model. The detailed formulation of the mathto represent the superstructure model and real situations in ematical optimization model is presented in the Supporting industrial plants. It should be mentioned that the GWPs Information. associated with wastewater from a WNS were not taken into 6232

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FIGURE 4. Configurations of the individual and cooperative water network systems (WNSs): (a) individual WNS, (b) cooperative WNS (IW, industrial water; WW, wastewater; units, m3/h). 2.2. Water Network Synthesis. Five processes of two prevention of climate change. The GWPs of the principal industrial plants were employed as water sources and sinks contributors during the lifetime were evaluated using equafor water network synthesis: three processes for cleaning, tions in the mathematical optimization model. The lifetime cooling, and brushing are used for plant “A” producing was assumed to be 15 years with respect to the lifetime of galvanized and aluminized steel sheets, and two cleaning pipelines. The carbon footprint of the individual and processes for copper-coated and tin-coated plates are used cooperative WNSs during the lifetime was calculated by for plant “B” producing electrolytic steel plates used in summing the GWPs of the principal contributors. manufacturing bottles and cans. The process limiting data The economic performances of the individual and cofor water network synthesis are presented in the Supporting operative WNSs were estimated to examine whether the Information, showing operational conditions required for cooperative WNS was economically feasible and profitable. each unit process. Chemical oxygen demand (COD) and The costs of construction, operations and maintenance, and suspended solid (SS) were used as water quality indicators disposal of the two WNSs were estimated, and then the because these concentrations represent the amount of incremental costs and benefits of the cooperative WNS were organic contaminants and particles. The distance matrix obtained by subtracting the costs of the cooperative WNS between the water sources and sinks is presented in the from the costs of the individual WNS (15). The net present Supporting Information, which was used to estimate the value of the cooperative WNS was evaluated from the length of pipelines. incremental costs and benefits to measure its economic The individual and cooperative WNSs were synthesized feasibility, and its payback period and internal rate of return using the mathematical optimization model to minimize the were evaluated to measure its economic profitability. The carbon footprint. The individual WNS consisted of two WNSs, detailed equations related to the economic evaluation are which were individually synthesized by using the unit presented in the Supporting Information. processes within each plant: WNS “A” was networked with the three processes within plant “A”, and WNS “B” with the 3. Results and Discussion two processes within plant “B”. The cooperative WNS was The configuration, design characteristics, carbon footprint, synthesized with the five processes in the two plants to and economic costs of the cooperative WNS were compared increase the opportunities of water reuse. The WNSs were to those of the individual WNS, and the economic feasibility generated from the optimal solutions to the mathematical and profitability of the cooperative WNS was evaluated, in optimization model. GAMS/MINOS (22) was used as an NLP order to estimate costs and benefits incurred from the solver to find the optimal solutions. The configurations of cooperation of the two industrial plants. the individual and cooperative WNSs were embodied from 3.1. Configurations and Design Characteristics. The their own optimal solutions. cooperative WNS utilized more opportunities of water reuse 2.3. Estimation of Carbon Footprint and Economic than the individual WNS. The configurations of the individual Performance. The carbon footprints of the individual and and cooperative WNSs are shown in Figure 4. The total cooperative WNSs were estimated to examine which WNS number of interconnections for water reuse in the cooperative was more environmentally friendly in the context of the VOL. 42, NO. 16, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Design Characteristics of the Individual and Cooperative Water Network Systems (WNSs)

industrial water consumption

flowrate flowrate

wastewater generation

COD load SS load

pipeline construction number of interconnections for water reuse

length intraplant interplant

electricity consumption

plant “A” plant “B” total plant “A” plant “B” total plant “A” plant “B” total plant “A” plant “B” total total plant “A” plant “B” plants “A” and “B”

total power requirement total

WNS was 40% greater than that in the individual WNS. It should be mentioned that the interconnections between plant “A” and plant “B” were generated in the cooperative WNS by creating more opportunities for water reuse, even though the number of the interconnections within plant “A” or plant “B” in the cooperative WNS was less than that in the individual WNS. Pumps for the interconnections between plant “A” and plant “B” were required to be operated in one plant to reduce the consumption of industrial water in the other plant. The cooperative WNS decreased the consumption of industrial water but increased the power requirement for pumping and the total pipe length, when compared to the individual WNS. The design characteristics of the individual and cooperative WNSs are summarized in Table 1. The total flowrate of industrial water consumed in the cooperative WNS was 7.4% less than that in the individual WNS because the cooperation of the two plants increased the opportunities of water reuse and the flowrate of reused water. The consumption of industrial water in plant “A” was decreased by 51.3%, and that in plant “B” was decreased by 1.3%: the cooperation induced higher benefits in plant “A” than in plant “B”. The total wastewater generation rate in the cooperative WNS was 8.0% less than that in the individual WNS because the consumption of industrial water in the cooperative WNS was less than that in the individual WNS. However, the flowrate and the COD and SS loads of wastewater in plant “A” were increased by 37.9%, 28.6% and 30.8%, respectively, as a result of the cooperation, while those in plant “B” were decreased by 13.9%, 10.8%, and 13.3%, respectively. The interconnections from plant “B” to plant “A” in the cooperative WNS increased the flowrate and contaminant loads of wastewater treated in plant “A” but decreased those treated in plant “B”: the variation of the flowrate and contaminant loads treated in each plant engendered another issue on the fair distribution of costs and benefits incurred from the cooperation of the two plants. The pipe length of the cooperative WNS was 114.8% greater than that of the individual WNS because of the interconnections between the two plants for water reuse. The power requirement for pumping in the cooperative WNS was 3.9% greater than that in the individual WNS because the flowrate of reused water and the pipe length related to head losses were increased as a result of the cooperation, even though the consumption of industrial water was decreased. It should be mentioned that the costs of pipeline construction and electricity for pumping cannot be easily distributed to the two plants because of the interdependency of the two plants. 6234

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units

individual WNS

cooperative WNS

m3/h

7.6 54.7 62.3 6.6 51.0 57.6 1.41 3.71 5.12 1.27 3.04 4.31 1350 3 2 0

3.7 54.0 57.7 9.1 43.9 53.0 1.82 3.30 5.12 1.67 2.64 4.31 2900 0 2 5

5 4.11

7 4.27

m3/h kg/h kg/h m

kW

In other words, the interconnections were shared by the two plants, and all of the components of the cooperative WNS were integrated for the cooperation. Therefore, the fair distribution of the costs and benefits to the two plants is important for successful construction, operation and maintenance, and disposal of the cooperative WNS. A strategy for the fair distribution is mentioned later, to take into account the practical implementation of the cooperative WNS. 3.2. Carbon Footprint. The cooperation of the two plants reduced the total carbon footprint of their water supply systems. Figure 5 shows the GWPs of the principal contributors and the carbon footprint of the individual and cooperative WNSs incurred during their lifetimes (15 years). The carbon footprint of the cooperative WNS was 6.3% less than that of the individual WNS. The decrease of the GWP from the reduction of industrial water outweighed the increase of the GWP from the increase of electricity consumption, when the cooperative WNS was compared to the individual WNS: the GWP from the consumption of industrial water in the cooperative WNS was 7.4% less than that in the individual WNS, while the GWP from the electricity consumption in the cooperative WNS was 4.0% greater. This means that the cooperation of industrial plants to construct a cooperative

FIGURE 5. Global warming potentials (GWPs) of the principal contributors, that is, the consumptions of industrial water and electricity, and carbon footprint, that is, the sum of the GWPs of the principal contributors, of the water network systems (WNSs) during their lifetimes (15 years).

FIGURE 6. Economic evaluation of the cooperative water network system (WNS): (a) construction, operations and maintenance, and disposal costs of the WNSs; (b) incremental costs and benefits of the cooperative WNS; (c) net present value (NPV) and payback period of the cooperative WNS; and (d) internal rate of return (IRR) of the cooperative WNS. WNS can be extended until the increase of GWP from electricity consumption for water reuse equals the decrease of GWP from the reduction of industrial water, in terms of the system boundary of a cooperative WNS. 3.3. Economic Evaluation of the Cooperative Water Network System. The cooperative WNS was more economically feasible and profitable than the individual WNS. Figure 6 shows the economic evaluation of the cooperative WNSs: the cost comparison between the individual and cooperative WNSs in their life cycle stages, the incremental costs and benefits of the cooperative WNS, and the economic feasibility and profitability of the cooperative WNS. The cooperation of the two plants decreased the annual operations and maintenance cost by 6.9% because of the reduction of industrial water but increased the construction and disposal cost by 101.5% and 102.8%, respectively, because of the interconnections between plant “A” and plant “B”, when the cooperative WNS was compared to the individual WNS. The incremental benefits of the cooperative WNS were incurred from the reduction of the operations and maintenance cost, and its incremental costs were incurred from the increase of the construction and disposal costs. The net present value of the cooperative WNS was 132,000 USD during its lifetime, which shows that the cooperative WNS is economically feasible. The payback period was 3 years, and the internal rate of return was 42.3%, which shows that the cooperative WNS is highly profitable. This means that the cooperation of industrial plants to reduce the carbon footprint can contribute to an increase in their economic benefits, which is in line with the concept of environmental management. 3.4. Strategy for Practical Implementation. The fair distribution of costs and benefits plays a significant role in the practical implementation of the cooperative WNS: the costs and benefits include the economic costs and benefits from its construction, operations and maintenance, and disposal, and the environmental costs and benefits such as the decrease of the carbon footprint. However, the costs and benefits cannot be easily distributed into the two plants because the cooperative WNS is integrated with the unit

processes in the two plants and so cannot be decomposed to parts of each plant on a quantitative basis. A strategy for the practical implementation of the cooperative WNS is to establish a joint venture company invested by the two plants for the fair distribution of costs and benefits, in order to deal with the cooperative WNS as a unit to construct, operate, and manage the system, and to treat wastewater from the system. The costs that would be needed for the individual WNS can be invested in the joint venture company, and the additional costs required for the cooperative WNS can be invested from the two plants with the aim to obtain economic and environmental benefits incurred from the cooperative WNS. All benefits from the joint venture company can be fairly distributed to the two plants on a quantitative basis of the share of their additional investments. Wastewater from the two plants in the cooperative WNS can be treated by the joint venture company, irrespective of the origins of wastewater. The costs that would be needed to treat wastewater in the case of the individual WNS can be periodically paid to the joint venture company. Increased or reduced costs for wastewater treatment in the cooperative WNS affect the profit of the joint venture company, which will be returned to the two plants.

Acknowledgments This work was supported by the Korea Research Foundation Grant (KRF-2007-357-D00150) funded by the Korean Government (MOEHRD). This work was also supported in part by the Korean Science and Engineering Foundation (R112003-006) through the Advanced Environmental Biotechnology Research Center at Pohang University of Science and Technology and in part by the program for advanced education of chemical engineers (second stage of BK21).

Supporting Information Available Formulation of the mathematical optimization model, equations for economic evaluation and their related nomenclature, process limiting data, distance matrix, and freshwater VOL. 42, NO. 16, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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concentrations. These materials are available free of charge via the Internet at http://pubs.acs.org.

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