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Emergy-based fuzzy optimization approach for water reuse in an eco-industrial park
Resources, Conservation and Recycling 55 (2011) 730–737
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Resources, Conservation and Recycling journal homepage: www.elsevier.com/locate/resconrec
Emergy-based fuzzy optimization approach for water reuse in an eco-industrial park Mohammad Sadegh Taskhiri a , Raymond R. Tan b,c,∗ , Anthony S.F. Chiu a,b a b c
Industrial Engineering Department, De La Salle University, Manila, Philippines Center for Engineering and Sustainable Development Research, De La Salle University, Manila, Philippines Chemical Engineering Department, De La Salle University, Manila, Philippines
a r t i c l e
i n f o
Article history: Received 21 October 2010 Received in revised form 5 March 2011 Accepted 8 March 2011 Keywords: Emergy Eco-industrial park Interplant water reuse Fuzzy mixed integer linear programming
a b s t r a c t The establishment of an eco-industrial park (EIP) provides opportunity for individual plants to cooperate with each other in order to utilize resources efficiently and thus reduce waste. The goal of an EIP is to “close the loop” through recycling and reuse of material and energy streams. Studies show with current freshwater consumption trends there would be water stress aggravated by global warming in the near future. This paper presents a model to design an EIP water reuse network that considers overall system sustainability as measured with emergy, as well as cost saving desired by individual plants. Case studies from literature are then solved to illustrate the advantage of this method in decision making. The illustrative examples show how the model achieves a compromise among the potentially conflicting fuzzy goals of the various EIP stakeholders. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Recent research shows that with current trends, global freshwater consumption may soon approach the sustainable limit, and thus lead to water stress in many countries in the near future (Rockstrom et al., 2009). Lack of freshwater could have significant impact on the industrial sector, since it might affect the supply chain management of the companies. It is thus important for companies to reduce their freshwater consumption. Water reuse in industrial plant is a strategy which has been considered by many studies in recent decades. Although it is possible to conserve freshwater by direct water reuse, further savings can be realized by regenerating wastewater prior to reuse. However, there would be other environmental impacts due to energy consumption and greenhouse gas (GHG) emissions for building and operating water reuse networks (Ku-Pineda and Tan, 2006; Tan et al., 2007; Lim and Park, 2008; Taskhiri et al., in press). Thus, it is necessary to balance water conservation goals with other environmental aspects, in order to optimize the sustainability of an industrial system. The eco-industrial park (EIP) is a concept that could takes advantage of cooperation among companies to achieve environmental
∗ Corresponding author at: Center for Engineering and Sustainable Development Research, De La Salle University, Manila, Philippines. Tel.: +63 2 536 0260; fax: +63 2 536 0260. E-mail addresses: m [email protected] (M.S. Taskhiri), [email protected], [email protected] (R.R. Tan), [email protected] (A.S.F. Chiu). 0921-3449/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.resconrec.2011.03.001
improvements. The concept has been implemented in many countries (Ehrenfeld and Gertler, 1997; Schwarz and Steininger, 1997; Chiu and Yong, 2004; Heeres et al., 2004; Roberts, 2004; Gibbs and Deutz, 2005; Park et al., 2008; Geng and Zhao, 2009; Geng et al., 2010; Zhang et al., 2010). According to Lowe (2001), an EIP is “characterized by closely cooperating manufacturing and service businesses that work together to improve their environmental and economic performance by reducing waste and increasing resource efficiency. More specifically, EIP can be composed of firms or facilities that coordinate their activities to increase efficient use of raw materials, reduce outputs of waste, conserve energy and water resources, and reduce transportation requirements.” According to Chertow (2007), wastewater exchange in an EIP is often a starting point for sharing other resources among the participating plants. For this reason, water exchange is the main focus of this work. Secondly, statistical data shows that with current trend of freshwater withdrawal coupled with climate change, there would be water stress in many countries in the near future. Recent research illustrates the advantages of water exchange in EIP’s (Geng et al., 2007; Chew et al., 2008; Lim and Park, 2008; Tian, 2008; Chew and Foo, 2009; Chew et al., 2009; Lovelady and El-Halwagi, 2009; Lovelady et al., 2009; Chew et al., 2010a,b; Aviso et al., 2010a,b, 2011; Tan et al., 2011). Note that water integration in an EIP differs from conventional plant integration because it involves firms and facilities which are not owned by a single entity. There is hence the need for cooperation among the plants involved, often facilitated by a higher level authority such as the government, industrial park owner, or tenant association. In a typical scenario, the plants are concerned with minimizing internal
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Nomenclature Subscripts i index for water sources j index for water sinks k index for regenerators Parameters A emergy for capital goods per unit volume of water flowrate per unit distance of pipe length (sej/m4 ) B emergy for materials in capital goods per unit distance of pipe length (sej/m) Cout,i outlet concentration of source i (mg/1) u Cin,j inlet concentration limit of sink j (mg/1) u inlet concentration limit of regenerator k (mg/1) Cin,k Cout ,k outlet concentration of regenerator k (mg/1) CFW cost of freshwater ($/m3 ) CWW cost of wastewater disposal ($/m3 ) cost of contaminant removal in regeneration plant CkR k per unit mass ($/g) annualized pipeline cost for per cubic meter interCI plant water flowrate for per meter ($/m4 ) CW emergy per unit volume of fresh water supply (sej/m3 ) flowrate of water sink j (m3 /day) Dj eL lower limit of EIP emergy fuzzy goal (sej/y) eU upper limit of EIP emergy fuzzy goal (sej/y) Eik emergy for pumping between source i and regenerator k per unit of water (sej/m3 ) Ekj emergy for pumping between regenerator k and sink j per unit of water (sej/m3 ) Eij emergy for pumping between source i and sink j per unit of water (sej/m3 ) M arbitrary large constant distance between source i to sink j (m) MDij MDik distance between source i and regeneration plant k (m) distance between regeneration plant k and sink j (m) MDkj N total annual working days P emergy for pumping per unit of water per unit distance (sej/m4 ) flowrate of water source i (m3 /day) Si Tk emergy per unit volume of wastewater regeneration (sej/m3 ) lower limit of fuzzy goal for total annual cost TCLi incurred by plant i ($/y) TCU upper limit of fuzzy goal for total annual cost i incurred by plant i ($/y) fixed emergy for regeneration process (sej/day) Uk WW emergy per unit volume of wastewater (sej/m3 ) Variables bik binary variable denoting the existence of a link from source i to regeneration process k binary variable denoting the existence of a link from bkj regeneration process k to sink j bij binary variable denoting the existence of a link from source i to sink j binary variable denoting the existence of regenerabk tion process k CostFW total freshwater consumption cost by sink j ($/y) j
total wastewater regeneration cost ($/y) CostR CostWW total wastewater generation cost by source i ($/y) i total interplant water flowrate cost by plant i ($/y) CostIi
e e1 e2 e3 e4 e5 Fj Iik Ikj Iij Rik rik rkj rij TCi Wi
731
rate of total emergy use (sej/y) rate of emergy inputs arising from freshwater usage (sej/y) rate of emergy inputs arising from electrical power usage (sej/y) rate of emergy use for capital goods installation (sej/y) rate of emergy use for regenerator process operation (sej/y) rate of emergy use for environmental impact of waste generation (sej/y) freshwater consumption by sink j (m3 /day) emergy for capital goods between source i and regenerator k (sej/day) emergy for capital goods between regenerator k and sink j (sej/day) emergy for capital goods between source i and sink j (sej/day) total contaminant mass load of plant i in regenerator k (g/day) flowrate variable for the link from source i to regeneration process k (m3 /day) flowrate variable for the link from regenerator k to sink j (m3 /day) flowrate variable for the link from source i to sink j (m3 /day) total annual cost incurred by plant i ($/y) wastewater discharge from source i (m3 /day)
(i.e., monetary) costs, while the authority also wishes to reduce externalities. An ideal EIP achieves a closed-loop structure in which all demands could be met by existing resources without environmental discharge (Chiu, 2009). However, there is a gap in previous studies to achieve optimum sustainability of EIPs since most previous studies used water flowrate or financial parameters as the objective function. Externalities of the EIP system such as natural resources inputs and environmental impact are neglected by such approaches. Externalities refer to real costs that are not accounted for using conventional economic computations. Such externalities can be estimated by the computation of total cumulative energy which has been used previously to make such resource or commodity. This concept is referred to as emergy (Odum, 1996) and acts as a link between environment and economic measures. As most naturally occurring resources and processes are driven by solar radiation, emergy is usually measured in terms of equivalent solar energy. The typical unit used in the solar emjoule (sej), corresponding to 1 J of sunlight. Fig. 1 illustrates the role of the emergy in the evaluation and design of an EIP water reuse network. Many studies have been done to show the advantages of emergy as means of environmental resource measurement (Odum, 1996; Lou et al., 2003; Brown and Ulgiati, 2004; Wang et al., 2005, 2006; Lei and Wang, 2008; Lv and Wu, 2009; Ulgiati and Brown, 2009; Geng et al., 2010). While some authors criticize emergy as a misleading and poor concept (Ayres, 1998; Cleveland et al., 2000), it nevertheless remains a useful metric since natural resources are the basic components of any process or commodity (Hau and Bakshi, 2004). As shown in Fig. 1, the attributes of an EIP water network are environmental and ecological inputs of natural resources which are consumed by the system. The design of EIP water reuse network may be formulated into a problem similar to the optimization of resource consumption in a single plant, such that the overall objective is to minimize fresh-
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External Costs
- Freshwater - Capital good - Regenerator Plant (RP) - Electrical power - Wastewater
Resource input for Fresh Water
Resource input for Pipeline
Environmental Impact ofWastewater
EIP Water Reuse Network
-(e.g.,BOD,COD, TDS,TSS)
Resource input for RP
Internal costs
Resource input for Electrical Power
- Freshwater - Wastewater - Wastewater regeneration - Interplant water flowrate Fig. 1. Total emergy flows in an EIP.
water consumption as well as waste generation. In our previous paper, we developed an approach for designing EIP water reuse networks based on the minimization of emergy (Taskhiri et al., in press); however, the main limitation of this approach is that it assumes the EIP to be a monolithic entity under centralized control. In practice, an EIP consists of different plants that are managed by different companies, each of which has its own profit-oriented goals. Thus, it is necessary to model such individual interests in order to develop an appropriate network design (Aviso et al., 2010a, 2011). Furthermore, government may participate in the development of an EIP, with the goal of protecting the welfare of the general public through efficient use of natural resources and through minimizing generation of waste streams (Aviso et al., 2010b; Tan et al., 2011). Hence, the satisfaction of the desired cost saving of the individual plants should also be considered in conjunction with the government’s sustainability goals. The main contribution of this paper is the development of an improved methodology that takes these issues into account. In this study, an emergy-based fuzzy MILP model is presented to show the influence of the emergy in EIP water network with emergy indicators to qualify the externalities of the system. The overall objective is to maximize the EIP satisfaction level by minimizing the overall sustainability, as measured using emergy, while satisfaction level of individual plant based on their profits or savings is considered as well. As in previous work (Aviso et al., 2010a,b, 2011; Tan et al., 2011), each plant is assumed to have a predefined fuzzy goal, which represents a range of costs within which the owner or management is willing to cooperate with the other plants through the joint implementation of an EIP water network. The rest of this paper is organized as follows. A formal problem statement is first given. The optimization model is described next, and then demonstrated using two case studies. Finally, conclusions and recommendations for further research are given.
while each plant may be represented schematically as in Fig. 3. The number of total sources and sinks are NS and ND , respectively. There is a specific flowrate (Si ) and quality or concentration (Cout,i ) defined for each source i, and corresponding characteristics (Dj and u ) for each sink j. There is a provision for installation of a set of Cin,j processes to regenerate water prior to recycle or reuse in the EIP, or prior to discharge to the environment; for these processes there are NI options available to choose from. The number of the regenerators can be decided upon by the model. Also, it is assumed that u ) as well an upper limit for the concentration of contaminant (Cin,k as fixed outlet concentration (Cout,k ) is specified for each regeneration process k. The problem is to determine the water network that maximizes the overall satisfaction () of the fuzzy goals of stakeholders in the EIP. In the case of EIP authority, the fuzzy goal (G ) is to maximize sustainability by minimizing total emergy of the system. On the other hand, the fuzzy goal of each individual company i (i ) is to minimize cost.
2. Problem statement
The fuzzy emergy goal for the entire EIP may is shown graphically in Fig. 4a, and is given by:
In this paper, the EIP system is defined as a set of plants that act as water sources and/or sinks. The EIP structure is shown in Fig. 2
3. Optimization model The objective function of the model is to maximize the overall degree of satisfaction of the EIP fuzzy goals: max
(1)
The fuzzy goal G of the EIP authority must also be at least equal to : G ≥
(2)
Likewise, every fuzzy goal i of each plant in the EIP must be satisfied to at least the degree : i ≥ ∀i
e − eU = G (eL − eU )
(3)
(4)
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Fig. 2. Superstructure of water integration approach in EIP.
Freshwater
Wastewater Water exchange to other plants
Plant i
Water exchange from other plants
Water to regenerators
Water from regenerators
Fig. 3. Input and output water streams for a given plant in an EIP.
where eL and eU are the lower and upper limits, respectively, of the EIP annual emergy fuzzy goal, while e is the total annual emergy consumption: e = e1 + e2 + e3 + e4 + e5
e1 is the annual emergy for freshwater, which may be obtained from the product of total freshwater consumption and the emergy per unit of externally sourced freshwater: e1 = ˙j Fj × CW × N
a
Partially Satisfied
Satisfied
Not Satisfied
1
λG
λ 0
e e
b
L
emergy e
U
Partially Satisfied
Satisfied
Not Satisfied
1
λi
λ 0 TC
L i
TC i
cost TC iU
Fig. 4. Fuzzy linear membership function of (a) EIP authority and (b) plants goals.
(5)
(6)
where Fj is the daily fresh water consumption of sink j, CW is the emergy coefficient per unit of fresh water, and N is the number of operating days per year. e2 is annual the emergy for electrical power which can be obtained from the summation of emergy of electrical power consumption for pumping water between a source-sink pairs or to/from a regenerator process: e2 = (˙i ˙k rik Eik + ˙j ˙k rkj Ekj + ˙i ˙j rij Eij ) × N
(7)
where rik is the daily water flowrate from source i to regenerator k, rkj is the daily water flowrate from regenerator k to sink j and rij is the daily water flowrate from source i to sink j; while Eik , Ekj and Eij are the respective emergy coefficients for pumping water. Note that this formulation assumes a fixed, lumped-parameter emergy coefficient per unit of water delivered. A similar approach is used by Aviso et al. (2010b, 2011). The coefficient values already take into account geometric considerations such as distance, elevation and pipe diameter. In general they may be computed as follows: Eij = MDij P Eik = MDik P
∀i, j ∀i, k
(8) (9)
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Ejk = MDjk P
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∀j, k
(10)
where MDij , MDik and MDkj are the distances from sources to sinks, from sources to regenerators, and from regenerators to sinks, respectively, and P is the emergy coefficient per unit of water pumped per unit distance. e3 is the annual emergy for capital goods which can be obtained from summation of emergy of investment for the pipeline between plant to/from the regeneration process: e3 = (˙i ˙k Iik + ˙j ˙k Ikj + ˙i ˙j Iij ) × N
(11)
Iij = Arij MDij
(12)
Iik = Arik MDik
(13)
Ikj = Arkj MDkj
(14)
where Iij , Ijk and Ikj is the equivalent daily emergy of the piping from sources to sinks, from sources to regenerators, and from regenerators to sinks, respectively; A is the emergy coefficient for the capital goods in the pipeline. e4 is the annual emergy for the regenerator plant which is assumed to be modelled as a linear function of capacity plus a fixed component: e4 = (˙k ˙i (Tk × rik + Uk × bk )) × N
(15)
where Tk and Uk are the coefficients of the variable and fixed components of the emergy of the regenerator units, on a per day basis, and bk is a binary variable denoting the existence of the kth regenerator. e5 is the annual emergy of the environmental impact of wastewater discharge which can be obtained from product of total wastewater generation by emergy index per unit of wastewater. e5 = (˙i Wi × WW) × N
(16)
where the coefficient WW accounts for the emergy of treatment of the wastewater by a public treatment facility, as well as the emergy associated with environmental damage caused by its eventual discharge into the environment. The level of satisfaction each plant i is based on monetary cost, as illustrated in Fig. 4b; it may be expressed as: L U TCi − TCU i = i (TCi − TCi ) ∀i
(17)
where TCi is the total annual cost of individual plant i in the EIP system, while TCLi and TCU i are the corresponding lower and upper limits of the fuzzy cost goals of the respective plants. The total cost includes operating expenses (i.e., those incurred from fresh water consumption, water regeneration and reuse, and wastewater disposal) as well as annualized capital costs: TCi = CostFW + CostWW + CostRi + CostIi i i
∀i = j
(18)
CostFW is the total annual cost of freshwater consumption by j sink j that could be obtained from product of freshwater unit cost (CFW ) and freshwater consumption per cubic meter: CostFW = ˙j Fj × C FW × N j
∀i = j
(19)
is the total annual cost of environmental discharge CostWW i from source i that could be obtained from product of wastewater flowrate and wastewater disposal cost per cubic meter (CWW ): CostWW = ˙i Wi × C WW × N i
∀i
(20)
We use a fixed unit cost for wastewater treatment since we assume that the EIP already has an existing wastewater treatment system, where tenant companies are charged in proportion to the flowrate of their effluent discharge. A similar assumption is used in Aviso et al. (2010b, 2011). In this work, it is also assumed that water regeneration cost is borne by the exporting plant, and that the fees
already account for both operating and capital expenses associated with regeneration. CostRi is the total cost of wastewater regeneration for a given plant i, which can be obtained from product of contaminant removal cost per unit mass (CkR ) and the contaminant mass load (Rik ): CostRi = ˙k Rik × CkR × N
∀i
(21)
Rik = ˙k rik (Cout,i − Cout,k ) ∀i
(22)
Cout,i is the concentration of water from source i entering the regenerator k, while Cout,k is the concentration of the water exiting the regenerator unit itself. Hence, it can be seen that Rik is the share of plant source i out of the total mass load removed by regenerator k. CostIi is the total annual cost of water reuse among participating plant within EIP that could be obtained from product of annualized pipeline cost (i.e., for pipelines, pumps and valves plus energy for pumping), water flowrate and distance: CostIi = ˙j rij C I MDij + ˙k rik C I MDik + ˙k rki C I MDki
∀i = j
(23)
where CI
is the annualized pipeline cost for per cubic meter of water flowrate for per meter of distance. The water balance for each sink is: ˙i rij + ˙k rkj + Fj = Dj
∀j
(24)
The water quality restriction for each sink is: u ˙i rij Cout,i + ˙k rkj Cout,k + Fj Cf ≤ Dj Cin,j
∀j
(25)
where Cout ,k and Cf are the concentrations of the kth regenerated water and freshwater, respectively. Both are assumed to be constant. The water balance for each source is: ˙j rij + ˙k rik + wi = Si
∀i
(26)
The water quality restriction for the inlet of each regeneration process is: u ˙i rik Cout,i ≤ ˙i rik Cin,k
∀k
(27)
u is the regenerator inlet concentration limit. where Cin,k The water balance for each regeneration process is:
∀k
˙i rik = ˙j rkj
(28)
The existence of links between sources, regeneration processes and sinks are indicated by binary variables: rik ≤ Mbik
∀i, k
(29)
rkj ≤ Mbkj
∀k, j
(30)
rij ≤ Mbij
∀i, j
(31)
where M is an arbitrarily large constant. The existence of regeneration processes are also indicated by binary variables: ˙i rik ≤ Mbk
∀k
(32)
These binary variables, which may also be used to impose additional case-specific topological constraints (e.g., limitations on network complexity), assume values of one or zero only: bk , bik , bkj , bij ∈ [0, 1]
∀i, j, k
(33)
Finally, all flowrates are non-negative: rik , rij , rkj ≥ 0 ∀i, j, k
(34)
It can be seen that this is a fuzzy mixed integer linear programming (FMILP) model, for which the global optimum can be determined without any significant computational difficulties if such a solution exists. In this work, the commercial software Lingo 11.0 is used to solve the case studies in the sections that follow.
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Table 1 Limiting data for the case study. Sink
Flowrate, Dj (t/day)
Concentration limit, Cin,j (mg/l)
Source
Flowrate, Si (t/day)
Concentration, Cout,i (mg/l)
1 2 3 4
100 20 80 60
10 100 20 50
1 2 3 4
100 20 50 100
100 250 80 200
Adapted from Aviso et al. (2010b).
Table 5 Optimal network for Case 1.
Table 2 Distance between source/sink pairs (MDij ) in meters. Source/sink
j=1
j=2
j=3
i=1 i=2 i=3 i=4
0 950 1850 1900
950 0 900 2850
1850 900 0 1950
j=4 1900 2850 1950 0
Table 3 Emergy indices for the study. Parameter
Value
Reference
P A B CW WW Tk Uk
6 × 107 sej/m4 1.1 × 107 sej/m4 1.4 × 108 sej/m 1.09 × 1010 sej/m3 2.86 × 1013 sej/m3 7.67 × 1011 sej/m3 6.67 × 107 sej/day
Odum (1996) Odum (1996) Odum (1996) Odum (1996) Björklund et al. (2001) Björklund et al. (2001) Björklund et al. (2001)
3.1. Case 1—direct reuse without regeneration Table 1 shows the limiting data of the case study in an EIP where water is to be shared among four plants acting as four sources and four sinks. The wastewater which is discharged from each plant could be sent for direct reuse to another plant, provided that the quality criterion of the sink is met. The distance between each source/sink pair is shown in Table 2. It is also assumed here that the freshwater source is free of contaminant (Cf = 0). The demand of each sink could be fulfilled with freshwater, or reused water from different sources. The cost of freshwater supply (CFW ) and wastewater disposal (CWW ) are $0.6/m3 and $0.1/m3 , respectively. Table 3 indicates the emergy indices that are used for this study. In this scenario, no regeneration plant is considered, such that all rik = 0 and all rkj = 0. It is also assumed that the total working days in a year (N) is 300 days. The upper and lower limits of fuzzy goals of the four L U L plants (TCU i and TCi , respectively) and the EIP authority (e and e , respectively) are given in Table 4. These goals are assumed to be based on a priori decisions made before negotiations begin among the parties involved (Aviso et al., 2010a, 2011; Tan et al., 2011). A minimum emergy solution of 5.28 × 1015 sej/y is generated when the model is solved. The corresponding water network is shown in Table 5, where the total freshwater and wastewater flowrates are 174 and 184 t/day, respectively. Of the four plants in the EIP, Sink 2 does not receive any freshwater, while Source 3 does not discharge any wastewater. It can also be seen that the optimal network has a total of four water reuse streams; none of the water from Sources 2 and 4 are reused in the system. Table 4 Fuzzy goals of EIP authority and plants. Party
Upper limit (eU or TCU i )
Lower limit (eL or TCLi )
EIP authority Plant 1 Plant 2 Plant 3 Plant 4
3.0 × 1018 sej/y $400,000/y $400,000/y $160,000/y $800,000/y
0 $200,000/y $100,000/y $80,000/y $400,000/y
Sink 1 Sink 2 Sink 3 Sink 4 Wastewater (184 t/day)
Freshwater (174 t/day)
Source 1
Source 2
Source 3
Source 4
87.5 0 64 22.5 n/a
0 20 16 0 64
0 0 0 0 20
12.5 0 0 37.5 0
0 0 0 0 100
Table 6 Decomposition of emergy contributions in case studies. Case 1
Case 2
Emergy (sej/y) e1 e2 e3 e4 e5
5.70 × 1014 2.61 × 1015 7.56 × 1014 0 1.58 × 1018
Total
1.584 × 1018
Share (%) 0.04 0.16 0.05 0 99.7
Emergy (sej/y) 3.21 × 1014 4.5 × 1015 1.90 × 1015 2.74 × 1016 9.3 × 1017 1.584 × 1018
100
Share (%) 0.03 0.47 0.20 2.84 96.5 100
Table 7 Decomposition of plant costs for two cases. Case 1
Plant 1 Plant 2 Plant 3 Plant 4
Case 2
Cost ($/y)
i
Cost ($/y)
i
256,350 150,000 107,770 604,050
0.72 0.83 0.65 0.49
263,127 150,000 105,250 526,254
0.68 0.83 0.68 0.68
The maximum level of satisfaction of the EIP authority or government’s emergy goal (G ) is 0.48, corresponding to a total emergy of 1.584 × 1018 sej/y. A detailed decomposition of emergy contributions is given in Table 6, where it can be seen that wastewater generation has the highest emergy share, because of the large value of the coefficient WW (Björklund et al., 2001). The levels of satisfaction for the cost goals of the participating plants (i ) are 0.72, 0.83, 0.65 and 0.49, respectively, with cost details being shown in Table 7. Note that each plant acts as both source and sink, and its total cost is the sum of the costs it incurs in each role. For this example, the overall satisfaction level () is equivalent to 0.48. This solution is optimal, since any change in network configuration may result in Table 8 Distance in meters between EIP plants and alternative regeneration plant for Case 2 (MDik or MDkj ). Source/sink
Regenerator option k=1
i or j = 1 i or j = 2 i or j = 3 i or j = 4
1350 1500 600 1350
k=2 2400 2150 1250 700
k=3 600 750 1250 2100
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Table 9 Optimal network for Case 2.
Sink 1 Sink 2 Sink 3 Sink 4 To regenerator (k = 3) Wastewater (108.4 t/day)
Freshwater (98.4 t/day)
From regenerator (k = 3)
Source 1
Source 2
Source 3
Source 4
0 0 70.4 28 n/a n/a
97.4 0 1.6 0 n/a n/a
0 0 0 22.1 72.9 5
0 0 0 0 0 20
0 16.7 0 9.9 0 23.4
2.6 3.3 8 0 26.2 60
at least one of the five parties involved getting a level of satisfaction of less than 0.48. 3.2. Case 2—reuse with regeneration Suppose that, upon assessment of the optimal solution in Case 1, it is deemed necessary to establish regeneration plant in order to further improve the degree of satisfaction of the EIP authority’s sustainability goal. Water streams from the sinks may thus undergo regeneration by means of a physical or chemical regeneration process; however, they can also be discharged to as waste from the EIP. The price of contaminant removal is $0.05/g, and the regenerated water has a concentration (Cout,k ) of 5 mg/l. All model parameters except those pertaining to regeneration are the same as in Case 1. The model is optimized by minimizing total cost of individual plant by establishment of a regeneration plant with optimum location in the EIP. As shown in Table 8, there are three regeneration plant options at different locations. The model can automatically select the optimal regeneration plant based on emergy and cost considerations. In this case, when the model is solved, the final network that results is shown in Table 9. The freshwater and wastewater flowrates are 98.4 and 108.4 t/day, respectively. The model also selects the third option (k = 3) for the regenerator with a flowrate of 99 t/day. In this network, only Sinks 3 and 4 use fresh water, while all sources discharge at least some of their water as waste (in the case of Source 2, all of the water is sent for discharge). Sources 1 and 4 feed into the regenerator, while Sinks 1 and 3 consume the regenerated water. There are also six direct interplant connections for water reuse. The level of satisfaction for the EIP authority emergy goal (G ) is 0.68, which is much higher than in the previous result. This corresponds to an emergy value of 9.64 × 1017 sej/y. Details of the contributions are again given in Table 6. The satisfaction levels of the four participating plants (i ) are 0.68. 0.83, 0.68 and 0.68, respectively, with the cost details being shown in Table 7. This time, the overall degree of satisfaction for the EIP () is also 0.68. The model is thus able to achieve a compromise among the conflicting goals of the five parties in the system. By comparison, if the only the EIP authority’s emergy goal is optimized using our previous approach (Taskhiri et al., in press) without regard for the individual plants’ economic interests, the resulting benefits would be less equitably distributed, with the sustainability goal being highly satisfied (G = 0.95), while the cost goals (i ) are inconsistently met, with satisfaction values ranging from 0.22 to 0.91. Note that, in such a scenario, the overall satisfaction level () is equivalent to that of the least satisfied party, which is 0.22. Furthermore, in practice, such inequitable distribution of benefits is likely to prove unattractive, and could result in the failure of the EIP initiative (Aviso et al., 2010a, 2011). 4. Conclusion Establishment of an EIP involves multiple stakeholders each with its own goals. From the government perspective, one of the main goals is to adopt policies to enhance sustainability through
conservation of natural resources and reduction of wastes. On the other hand, individual companies in an EIP seek mainly to maximize profit or minimize costs. This paper has presented a fuzzy mixed integer linear programming (FMILP) model for an emergy-based EIP water reuse network. In this paper, a comprehensive methodology has been developed using emergy as means of measuring the overall sustainability of the EIP. Although the model takes into account the sustainability of the EIP as a single unit, there are also fuzzy goals to reflect the satisfaction of the cost savings of the individual plants. The approach developed is illustrated using two case studies from literature. Future work should focus on the development of a bi-level optimization variant to allow the government or EIP authority to develop incentive mechanisms that induce desirable behaviour from the plants in the EIP. Multi-period and robust optimization variants may also be developed. Finally, alternative physical models of the water network components (e.g., regenerators with fixed removal ratio) may be incorporated in the general model to fit specific applications. References Aviso KB, Tan RR, Culaba AB. Designing eco-industrial water exchange networks using fuzzy mathematical programming. Clean Technologies and Environmental Policy 2010a;12:353–63. Aviso KB, Tan RR, Culaba AB, Cruz Jr JB. Bi-level fuzzy optimization approach for water exchange in eco-industrial parks. Process Safety and Environmental Protection 2010b;88:31–40. Aviso KB, Tan RR, Culaba AB, Foo DCY, Hallale N. Fuzzy optimization of topologically constrained eco-industrial resource conservation networks with incomplete information. Engineering Optimization 2011;43:257–79. Ayres RU. Ecology vs. economics: confusing production and consumption. France: Center of the Management of Environmental Resources, INSEAD, Fontainebleau; 1998. Björklund J, Geber U, Rydberg T. Emergy analysis of municipal wastewater treatment and generation of electricity by digestion of sewage sludge. Resources, Conservation and Recycling 2001;31:293–316. Brown MT, Ulgiati S. Energy quality, emergy, and transformity: H.T. Odum’s contributions to quantifying and understanding systems. Ecological Modelling 2004;178:201–13. Chertow MR. Uncovering industrial symbiosis. Journal of Industrial Ecology 2007;11:11–30. Chew IML, Tan R, Ng DKS, Foo DCY, Majozi T, Gouws J. Synthesis of direct and indirect interplant water network. Industrial & Engineering Chemistry Research 2008;47:9485–96. Chew IML, Foo DCY. Automated targeting for inter-plant water integration. Chemical Engineering Journal 2009;153:23–36. Chew IML, Tan RR, Foo DCY, Chiu ASF. Game theory approach to the analysis of interplant water integration in an eco-industrial park. Journal of Cleaner Production 2009;17:1611–9. Chew IML, Foo DCY, Ng DKS, Tan RR. Flowrate targeting algorithm for interplant resource conservation network. Part 1: Unassisted integration scheme. Industrial & Engineering Chemistry Research 2010a;49:6439–55. Chew IML, Foo DCY, Tan RR. Flowrate targeting algorithm for interplant resource conservation network. Part 2: Assisted integration scheme. Industrial & Engineering Chemistry Research 2010b;49:6456–68. Chiu ASF. The emerging role of eco-industrial development in the Asia Pacific: towards national and regional green economies. Global change and sustainable development: Asia-Pacific perspectives. Cambridge University Press; 2009. Chiu ASF, Yong G. On the industrial ecology potential in Asian Developing Countries. Journal of Cleaner Production 2004;12:1037–45. Cleveland CJ, Kaufmann RK, Stern DI. Aggregation and the role of energy in the economy. Ecological Economics 2000;32:301–17. Ehrenfeld J, Gertler N. Industrial ecology in practice: The evolution of interdependence at Kalundborg. Journal of Industrial Ecology 1997;1:67–79.
M.S. Taskhiri et al. / Resources, Conservation and Recycling 55 (2011) 730–737 Geng Y, Zhao HX. Industrial park management in the Chinese environment. Journal of Clenaer Production 2009;17:1289–94. Geng Y, Cote R, Fujita T. A quantitative model for optimizing water resources at the industrial park level. Regional Environmental Change 2007;7:123–35. Geng Y, Zhang P, Ulgiati S, Sarkis J. Emergy analysis of an industrial park: the case of Dalian, China. Science the Total Environment 2010;408:5273–83. Gibbs D, Deutz P. Implementing industrial ecology? Planning for eco-industrial parks in the USA. Geoforum 2005;36:452–64. Hau J, Bakshi BR. Promise and problems of emergy analysis. Ecological Modelling 2004;178:215–25. Heeres R, Vermeulen W, Dewalle F. Eco-industrial park initiatives in the USA and the Netherlands: first lessons. Journal of Cleaner Production 2004;12:985–95. Ku-Pineda V, Tan RR. Environmental performance optimization using process water integration and Sustainable Process Index. Journal of Cleaner Production 2006;14:1586–92. Lei K, Wang Z. Emergy synthesis and simulation for Macao. Energy 2008;33:613–25. Lim S-R, Park JM. Cooperative Water Network System to Reduce Carbon Footprint. Environmental Science & Technology 2008;42:6230–6. Lou H, Kulkarni MA, Singh A, Huang Y. A game theory based approach for emergy analysis of industrial ecosystem under uncertainty. Clean Technologies and Environmental Policy 2003;6:156–61. Lovelady EM, El-Halwagi MM. Design and integration of eco-industrial parks for managing water resources. Environmental Progress & Sustainable Energy 2009;28:265–72. Lovelady EM, El-Halwagi MM, Chew IML, Ng DKS, Foo DCY, Tan RR. A propertyintegration approach to the design and integration of eco-industrial parks. In: Proceedings of the 7th International Conference on Foundations of ComputerAided Process Design (FOCAPD); 2009. Lowe EA. Eco-industrial park handbook for Asian developing countries a report to Asian development bank. Oakland, CA: Environment Department, Indigo Development; 2001. Lv C, Wu Z. Emergy analysis of regional water ecological–economic system. Ecological Engineering 2009;35:703–10. Odum HT. Environmental accounting: emergy and environmental decision making. New York: Wiley; 1996.
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Park H, Rene ER, Choi S-M, Chiu ASF. Strategies for sustainable development of industrial park in Ulsan, South Korea—from spontaneous evolution to systematic expansion of industrial symbiosis. Journal of Environmental Management 2008;87:1–13. Roberts B. The application of industrial ecology principles and planning guidelines for the development of eco-industrial parks: an Australian case study. Journal of Cleaner Production 2004;12:997–1010. Rockstrom J, Steffen W, Noone K, Persson A, Chapin FS, Lambin EF, et al. A safe operating space for humanity. Nature 2009;461:472–5. Schwarz EJ, Steininger KW. Implementing nature’s lesson: the industrial recycling network enhancing regional development. Journal of Cleaner Production 1997;5:47–56. Tan RR, Foo DCY, Ng DKS, Chiang CL, Hul S, Ku-Pineda V. An approximate mixed integer linear programming (MILP) model for the design of water reuse/recycle networks with minimum emergy. Asia-Pacific Journal of Chemical Engineering 2007;2:566–74. Tan RR, Aviso KB, Cruz JB, Culaba AB. A note on an extended fuzzy bi-level optimization approach for water exchange in eco-industrial parks with hub topology Process Safety and Environmental Protection; 2011;89:106–11. Taskhiri MS, Tan RR, Chiu ASF. MILP model for emergy optimization in EIP water networks. Clean Technology and Environmental Policy; doi:10.1007/s10098-010-0314-1, in press. Tian J. A process integration approach to industrial water conservation: a case study for a Chinese steel plant. Journal of Environmental Management 2008;86: 682–7. Ulgiati S, Brown M. Emergy and ecosystem complexity. Communications in Nonlinear Science and Numerical Simulation 2009;14:310–21. Wang L, Zhang J, Ni W. Emergy evaluation of eco-industrial park with power plant. Ecological Modelling 2005;189:233–40. Wang L, Ni W, Li Z. Emergy evaluation of combined heat and power plant eco-industrial park (CHP plant EIP). Resources, Conservation and Recycling 2006;48:56–70. Zhang L, Yuan Z, Bi J, Zhang B, Liu B. Eco-industrial parks: national pilot practices in China. Journal of Cleaner Production 2010;18:504–9.
Contents lists available at ScienceDirect
Resources, Conservation and Recycling journal homepage: www.elsevier.com/locate/resconrec
Emergy-based fuzzy optimization approach for water reuse in an eco-industrial park Mohammad Sadegh Taskhiri a , Raymond R. Tan b,c,∗ , Anthony S.F. Chiu a,b a b c
Industrial Engineering Department, De La Salle University, Manila, Philippines Center for Engineering and Sustainable Development Research, De La Salle University, Manila, Philippines Chemical Engineering Department, De La Salle University, Manila, Philippines
a r t i c l e
i n f o
Article history: Received 21 October 2010 Received in revised form 5 March 2011 Accepted 8 March 2011 Keywords: Emergy Eco-industrial park Interplant water reuse Fuzzy mixed integer linear programming
a b s t r a c t The establishment of an eco-industrial park (EIP) provides opportunity for individual plants to cooperate with each other in order to utilize resources efficiently and thus reduce waste. The goal of an EIP is to “close the loop” through recycling and reuse of material and energy streams. Studies show with current freshwater consumption trends there would be water stress aggravated by global warming in the near future. This paper presents a model to design an EIP water reuse network that considers overall system sustainability as measured with emergy, as well as cost saving desired by individual plants. Case studies from literature are then solved to illustrate the advantage of this method in decision making. The illustrative examples show how the model achieves a compromise among the potentially conflicting fuzzy goals of the various EIP stakeholders. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Recent research shows that with current trends, global freshwater consumption may soon approach the sustainable limit, and thus lead to water stress in many countries in the near future (Rockstrom et al., 2009). Lack of freshwater could have significant impact on the industrial sector, since it might affect the supply chain management of the companies. It is thus important for companies to reduce their freshwater consumption. Water reuse in industrial plant is a strategy which has been considered by many studies in recent decades. Although it is possible to conserve freshwater by direct water reuse, further savings can be realized by regenerating wastewater prior to reuse. However, there would be other environmental impacts due to energy consumption and greenhouse gas (GHG) emissions for building and operating water reuse networks (Ku-Pineda and Tan, 2006; Tan et al., 2007; Lim and Park, 2008; Taskhiri et al., in press). Thus, it is necessary to balance water conservation goals with other environmental aspects, in order to optimize the sustainability of an industrial system. The eco-industrial park (EIP) is a concept that could takes advantage of cooperation among companies to achieve environmental
∗ Corresponding author at: Center for Engineering and Sustainable Development Research, De La Salle University, Manila, Philippines. Tel.: +63 2 536 0260; fax: +63 2 536 0260. E-mail addresses: m [email protected] (M.S. Taskhiri), [email protected], [email protected] (R.R. Tan), [email protected] (A.S.F. Chiu). 0921-3449/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.resconrec.2011.03.001
improvements. The concept has been implemented in many countries (Ehrenfeld and Gertler, 1997; Schwarz and Steininger, 1997; Chiu and Yong, 2004; Heeres et al., 2004; Roberts, 2004; Gibbs and Deutz, 2005; Park et al., 2008; Geng and Zhao, 2009; Geng et al., 2010; Zhang et al., 2010). According to Lowe (2001), an EIP is “characterized by closely cooperating manufacturing and service businesses that work together to improve their environmental and economic performance by reducing waste and increasing resource efficiency. More specifically, EIP can be composed of firms or facilities that coordinate their activities to increase efficient use of raw materials, reduce outputs of waste, conserve energy and water resources, and reduce transportation requirements.” According to Chertow (2007), wastewater exchange in an EIP is often a starting point for sharing other resources among the participating plants. For this reason, water exchange is the main focus of this work. Secondly, statistical data shows that with current trend of freshwater withdrawal coupled with climate change, there would be water stress in many countries in the near future. Recent research illustrates the advantages of water exchange in EIP’s (Geng et al., 2007; Chew et al., 2008; Lim and Park, 2008; Tian, 2008; Chew and Foo, 2009; Chew et al., 2009; Lovelady and El-Halwagi, 2009; Lovelady et al., 2009; Chew et al., 2010a,b; Aviso et al., 2010a,b, 2011; Tan et al., 2011). Note that water integration in an EIP differs from conventional plant integration because it involves firms and facilities which are not owned by a single entity. There is hence the need for cooperation among the plants involved, often facilitated by a higher level authority such as the government, industrial park owner, or tenant association. In a typical scenario, the plants are concerned with minimizing internal
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Nomenclature Subscripts i index for water sources j index for water sinks k index for regenerators Parameters A emergy for capital goods per unit volume of water flowrate per unit distance of pipe length (sej/m4 ) B emergy for materials in capital goods per unit distance of pipe length (sej/m) Cout,i outlet concentration of source i (mg/1) u Cin,j inlet concentration limit of sink j (mg/1) u inlet concentration limit of regenerator k (mg/1) Cin,k Cout ,k outlet concentration of regenerator k (mg/1) CFW cost of freshwater ($/m3 ) CWW cost of wastewater disposal ($/m3 ) cost of contaminant removal in regeneration plant CkR k per unit mass ($/g) annualized pipeline cost for per cubic meter interCI plant water flowrate for per meter ($/m4 ) CW emergy per unit volume of fresh water supply (sej/m3 ) flowrate of water sink j (m3 /day) Dj eL lower limit of EIP emergy fuzzy goal (sej/y) eU upper limit of EIP emergy fuzzy goal (sej/y) Eik emergy for pumping between source i and regenerator k per unit of water (sej/m3 ) Ekj emergy for pumping between regenerator k and sink j per unit of water (sej/m3 ) Eij emergy for pumping between source i and sink j per unit of water (sej/m3 ) M arbitrary large constant distance between source i to sink j (m) MDij MDik distance between source i and regeneration plant k (m) distance between regeneration plant k and sink j (m) MDkj N total annual working days P emergy for pumping per unit of water per unit distance (sej/m4 ) flowrate of water source i (m3 /day) Si Tk emergy per unit volume of wastewater regeneration (sej/m3 ) lower limit of fuzzy goal for total annual cost TCLi incurred by plant i ($/y) TCU upper limit of fuzzy goal for total annual cost i incurred by plant i ($/y) fixed emergy for regeneration process (sej/day) Uk WW emergy per unit volume of wastewater (sej/m3 ) Variables bik binary variable denoting the existence of a link from source i to regeneration process k binary variable denoting the existence of a link from bkj regeneration process k to sink j bij binary variable denoting the existence of a link from source i to sink j binary variable denoting the existence of regenerabk tion process k CostFW total freshwater consumption cost by sink j ($/y) j
total wastewater regeneration cost ($/y) CostR CostWW total wastewater generation cost by source i ($/y) i total interplant water flowrate cost by plant i ($/y) CostIi
e e1 e2 e3 e4 e5 Fj Iik Ikj Iij Rik rik rkj rij TCi Wi
731
rate of total emergy use (sej/y) rate of emergy inputs arising from freshwater usage (sej/y) rate of emergy inputs arising from electrical power usage (sej/y) rate of emergy use for capital goods installation (sej/y) rate of emergy use for regenerator process operation (sej/y) rate of emergy use for environmental impact of waste generation (sej/y) freshwater consumption by sink j (m3 /day) emergy for capital goods between source i and regenerator k (sej/day) emergy for capital goods between regenerator k and sink j (sej/day) emergy for capital goods between source i and sink j (sej/day) total contaminant mass load of plant i in regenerator k (g/day) flowrate variable for the link from source i to regeneration process k (m3 /day) flowrate variable for the link from regenerator k to sink j (m3 /day) flowrate variable for the link from source i to sink j (m3 /day) total annual cost incurred by plant i ($/y) wastewater discharge from source i (m3 /day)
(i.e., monetary) costs, while the authority also wishes to reduce externalities. An ideal EIP achieves a closed-loop structure in which all demands could be met by existing resources without environmental discharge (Chiu, 2009). However, there is a gap in previous studies to achieve optimum sustainability of EIPs since most previous studies used water flowrate or financial parameters as the objective function. Externalities of the EIP system such as natural resources inputs and environmental impact are neglected by such approaches. Externalities refer to real costs that are not accounted for using conventional economic computations. Such externalities can be estimated by the computation of total cumulative energy which has been used previously to make such resource or commodity. This concept is referred to as emergy (Odum, 1996) and acts as a link between environment and economic measures. As most naturally occurring resources and processes are driven by solar radiation, emergy is usually measured in terms of equivalent solar energy. The typical unit used in the solar emjoule (sej), corresponding to 1 J of sunlight. Fig. 1 illustrates the role of the emergy in the evaluation and design of an EIP water reuse network. Many studies have been done to show the advantages of emergy as means of environmental resource measurement (Odum, 1996; Lou et al., 2003; Brown and Ulgiati, 2004; Wang et al., 2005, 2006; Lei and Wang, 2008; Lv and Wu, 2009; Ulgiati and Brown, 2009; Geng et al., 2010). While some authors criticize emergy as a misleading and poor concept (Ayres, 1998; Cleveland et al., 2000), it nevertheless remains a useful metric since natural resources are the basic components of any process or commodity (Hau and Bakshi, 2004). As shown in Fig. 1, the attributes of an EIP water network are environmental and ecological inputs of natural resources which are consumed by the system. The design of EIP water reuse network may be formulated into a problem similar to the optimization of resource consumption in a single plant, such that the overall objective is to minimize fresh-
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External Costs
- Freshwater - Capital good - Regenerator Plant (RP) - Electrical power - Wastewater
Resource input for Fresh Water
Resource input for Pipeline
Environmental Impact ofWastewater
EIP Water Reuse Network
-(e.g.,BOD,COD, TDS,TSS)
Resource input for RP
Internal costs
Resource input for Electrical Power
- Freshwater - Wastewater - Wastewater regeneration - Interplant water flowrate Fig. 1. Total emergy flows in an EIP.
water consumption as well as waste generation. In our previous paper, we developed an approach for designing EIP water reuse networks based on the minimization of emergy (Taskhiri et al., in press); however, the main limitation of this approach is that it assumes the EIP to be a monolithic entity under centralized control. In practice, an EIP consists of different plants that are managed by different companies, each of which has its own profit-oriented goals. Thus, it is necessary to model such individual interests in order to develop an appropriate network design (Aviso et al., 2010a, 2011). Furthermore, government may participate in the development of an EIP, with the goal of protecting the welfare of the general public through efficient use of natural resources and through minimizing generation of waste streams (Aviso et al., 2010b; Tan et al., 2011). Hence, the satisfaction of the desired cost saving of the individual plants should also be considered in conjunction with the government’s sustainability goals. The main contribution of this paper is the development of an improved methodology that takes these issues into account. In this study, an emergy-based fuzzy MILP model is presented to show the influence of the emergy in EIP water network with emergy indicators to qualify the externalities of the system. The overall objective is to maximize the EIP satisfaction level by minimizing the overall sustainability, as measured using emergy, while satisfaction level of individual plant based on their profits or savings is considered as well. As in previous work (Aviso et al., 2010a,b, 2011; Tan et al., 2011), each plant is assumed to have a predefined fuzzy goal, which represents a range of costs within which the owner or management is willing to cooperate with the other plants through the joint implementation of an EIP water network. The rest of this paper is organized as follows. A formal problem statement is first given. The optimization model is described next, and then demonstrated using two case studies. Finally, conclusions and recommendations for further research are given.
while each plant may be represented schematically as in Fig. 3. The number of total sources and sinks are NS and ND , respectively. There is a specific flowrate (Si ) and quality or concentration (Cout,i ) defined for each source i, and corresponding characteristics (Dj and u ) for each sink j. There is a provision for installation of a set of Cin,j processes to regenerate water prior to recycle or reuse in the EIP, or prior to discharge to the environment; for these processes there are NI options available to choose from. The number of the regenerators can be decided upon by the model. Also, it is assumed that u ) as well an upper limit for the concentration of contaminant (Cin,k as fixed outlet concentration (Cout,k ) is specified for each regeneration process k. The problem is to determine the water network that maximizes the overall satisfaction () of the fuzzy goals of stakeholders in the EIP. In the case of EIP authority, the fuzzy goal (G ) is to maximize sustainability by minimizing total emergy of the system. On the other hand, the fuzzy goal of each individual company i (i ) is to minimize cost.
2. Problem statement
The fuzzy emergy goal for the entire EIP may is shown graphically in Fig. 4a, and is given by:
In this paper, the EIP system is defined as a set of plants that act as water sources and/or sinks. The EIP structure is shown in Fig. 2
3. Optimization model The objective function of the model is to maximize the overall degree of satisfaction of the EIP fuzzy goals: max
(1)
The fuzzy goal G of the EIP authority must also be at least equal to : G ≥
(2)
Likewise, every fuzzy goal i of each plant in the EIP must be satisfied to at least the degree : i ≥ ∀i
e − eU = G (eL − eU )
(3)
(4)
M.S. Taskhiri et al. / Resources, Conservation and Recycling 55 (2011) 730–737
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Fig. 2. Superstructure of water integration approach in EIP.
Freshwater
Wastewater Water exchange to other plants
Plant i
Water exchange from other plants
Water to regenerators
Water from regenerators
Fig. 3. Input and output water streams for a given plant in an EIP.
where eL and eU are the lower and upper limits, respectively, of the EIP annual emergy fuzzy goal, while e is the total annual emergy consumption: e = e1 + e2 + e3 + e4 + e5
e1 is the annual emergy for freshwater, which may be obtained from the product of total freshwater consumption and the emergy per unit of externally sourced freshwater: e1 = ˙j Fj × CW × N
a
Partially Satisfied
Satisfied
Not Satisfied
1
λG
λ 0
e e
b
L
emergy e
U
Partially Satisfied
Satisfied
Not Satisfied
1
λi
λ 0 TC
L i
TC i
cost TC iU
Fig. 4. Fuzzy linear membership function of (a) EIP authority and (b) plants goals.
(5)
(6)
where Fj is the daily fresh water consumption of sink j, CW is the emergy coefficient per unit of fresh water, and N is the number of operating days per year. e2 is annual the emergy for electrical power which can be obtained from the summation of emergy of electrical power consumption for pumping water between a source-sink pairs or to/from a regenerator process: e2 = (˙i ˙k rik Eik + ˙j ˙k rkj Ekj + ˙i ˙j rij Eij ) × N
(7)
where rik is the daily water flowrate from source i to regenerator k, rkj is the daily water flowrate from regenerator k to sink j and rij is the daily water flowrate from source i to sink j; while Eik , Ekj and Eij are the respective emergy coefficients for pumping water. Note that this formulation assumes a fixed, lumped-parameter emergy coefficient per unit of water delivered. A similar approach is used by Aviso et al. (2010b, 2011). The coefficient values already take into account geometric considerations such as distance, elevation and pipe diameter. In general they may be computed as follows: Eij = MDij P Eik = MDik P
∀i, j ∀i, k
(8) (9)
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Ejk = MDjk P
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∀j, k
(10)
where MDij , MDik and MDkj are the distances from sources to sinks, from sources to regenerators, and from regenerators to sinks, respectively, and P is the emergy coefficient per unit of water pumped per unit distance. e3 is the annual emergy for capital goods which can be obtained from summation of emergy of investment for the pipeline between plant to/from the regeneration process: e3 = (˙i ˙k Iik + ˙j ˙k Ikj + ˙i ˙j Iij ) × N
(11)
Iij = Arij MDij
(12)
Iik = Arik MDik
(13)
Ikj = Arkj MDkj
(14)
where Iij , Ijk and Ikj is the equivalent daily emergy of the piping from sources to sinks, from sources to regenerators, and from regenerators to sinks, respectively; A is the emergy coefficient for the capital goods in the pipeline. e4 is the annual emergy for the regenerator plant which is assumed to be modelled as a linear function of capacity plus a fixed component: e4 = (˙k ˙i (Tk × rik + Uk × bk )) × N
(15)
where Tk and Uk are the coefficients of the variable and fixed components of the emergy of the regenerator units, on a per day basis, and bk is a binary variable denoting the existence of the kth regenerator. e5 is the annual emergy of the environmental impact of wastewater discharge which can be obtained from product of total wastewater generation by emergy index per unit of wastewater. e5 = (˙i Wi × WW) × N
(16)
where the coefficient WW accounts for the emergy of treatment of the wastewater by a public treatment facility, as well as the emergy associated with environmental damage caused by its eventual discharge into the environment. The level of satisfaction each plant i is based on monetary cost, as illustrated in Fig. 4b; it may be expressed as: L U TCi − TCU i = i (TCi − TCi ) ∀i
(17)
where TCi is the total annual cost of individual plant i in the EIP system, while TCLi and TCU i are the corresponding lower and upper limits of the fuzzy cost goals of the respective plants. The total cost includes operating expenses (i.e., those incurred from fresh water consumption, water regeneration and reuse, and wastewater disposal) as well as annualized capital costs: TCi = CostFW + CostWW + CostRi + CostIi i i
∀i = j
(18)
CostFW is the total annual cost of freshwater consumption by j sink j that could be obtained from product of freshwater unit cost (CFW ) and freshwater consumption per cubic meter: CostFW = ˙j Fj × C FW × N j
∀i = j
(19)
is the total annual cost of environmental discharge CostWW i from source i that could be obtained from product of wastewater flowrate and wastewater disposal cost per cubic meter (CWW ): CostWW = ˙i Wi × C WW × N i
∀i
(20)
We use a fixed unit cost for wastewater treatment since we assume that the EIP already has an existing wastewater treatment system, where tenant companies are charged in proportion to the flowrate of their effluent discharge. A similar assumption is used in Aviso et al. (2010b, 2011). In this work, it is also assumed that water regeneration cost is borne by the exporting plant, and that the fees
already account for both operating and capital expenses associated with regeneration. CostRi is the total cost of wastewater regeneration for a given plant i, which can be obtained from product of contaminant removal cost per unit mass (CkR ) and the contaminant mass load (Rik ): CostRi = ˙k Rik × CkR × N
∀i
(21)
Rik = ˙k rik (Cout,i − Cout,k ) ∀i
(22)
Cout,i is the concentration of water from source i entering the regenerator k, while Cout,k is the concentration of the water exiting the regenerator unit itself. Hence, it can be seen that Rik is the share of plant source i out of the total mass load removed by regenerator k. CostIi is the total annual cost of water reuse among participating plant within EIP that could be obtained from product of annualized pipeline cost (i.e., for pipelines, pumps and valves plus energy for pumping), water flowrate and distance: CostIi = ˙j rij C I MDij + ˙k rik C I MDik + ˙k rki C I MDki
∀i = j
(23)
where CI
is the annualized pipeline cost for per cubic meter of water flowrate for per meter of distance. The water balance for each sink is: ˙i rij + ˙k rkj + Fj = Dj
∀j
(24)
The water quality restriction for each sink is: u ˙i rij Cout,i + ˙k rkj Cout,k + Fj Cf ≤ Dj Cin,j
∀j
(25)
where Cout ,k and Cf are the concentrations of the kth regenerated water and freshwater, respectively. Both are assumed to be constant. The water balance for each source is: ˙j rij + ˙k rik + wi = Si
∀i
(26)
The water quality restriction for the inlet of each regeneration process is: u ˙i rik Cout,i ≤ ˙i rik Cin,k
∀k
(27)
u is the regenerator inlet concentration limit. where Cin,k The water balance for each regeneration process is:
∀k
˙i rik = ˙j rkj
(28)
The existence of links between sources, regeneration processes and sinks are indicated by binary variables: rik ≤ Mbik
∀i, k
(29)
rkj ≤ Mbkj
∀k, j
(30)
rij ≤ Mbij
∀i, j
(31)
where M is an arbitrarily large constant. The existence of regeneration processes are also indicated by binary variables: ˙i rik ≤ Mbk
∀k
(32)
These binary variables, which may also be used to impose additional case-specific topological constraints (e.g., limitations on network complexity), assume values of one or zero only: bk , bik , bkj , bij ∈ [0, 1]
∀i, j, k
(33)
Finally, all flowrates are non-negative: rik , rij , rkj ≥ 0 ∀i, j, k
(34)
It can be seen that this is a fuzzy mixed integer linear programming (FMILP) model, for which the global optimum can be determined without any significant computational difficulties if such a solution exists. In this work, the commercial software Lingo 11.0 is used to solve the case studies in the sections that follow.
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Table 1 Limiting data for the case study. Sink
Flowrate, Dj (t/day)
Concentration limit, Cin,j (mg/l)
Source
Flowrate, Si (t/day)
Concentration, Cout,i (mg/l)
1 2 3 4
100 20 80 60
10 100 20 50
1 2 3 4
100 20 50 100
100 250 80 200
Adapted from Aviso et al. (2010b).
Table 5 Optimal network for Case 1.
Table 2 Distance between source/sink pairs (MDij ) in meters. Source/sink
j=1
j=2
j=3
i=1 i=2 i=3 i=4
0 950 1850 1900
950 0 900 2850
1850 900 0 1950
j=4 1900 2850 1950 0
Table 3 Emergy indices for the study. Parameter
Value
Reference
P A B CW WW Tk Uk
6 × 107 sej/m4 1.1 × 107 sej/m4 1.4 × 108 sej/m 1.09 × 1010 sej/m3 2.86 × 1013 sej/m3 7.67 × 1011 sej/m3 6.67 × 107 sej/day
Odum (1996) Odum (1996) Odum (1996) Odum (1996) Björklund et al. (2001) Björklund et al. (2001) Björklund et al. (2001)
3.1. Case 1—direct reuse without regeneration Table 1 shows the limiting data of the case study in an EIP where water is to be shared among four plants acting as four sources and four sinks. The wastewater which is discharged from each plant could be sent for direct reuse to another plant, provided that the quality criterion of the sink is met. The distance between each source/sink pair is shown in Table 2. It is also assumed here that the freshwater source is free of contaminant (Cf = 0). The demand of each sink could be fulfilled with freshwater, or reused water from different sources. The cost of freshwater supply (CFW ) and wastewater disposal (CWW ) are $0.6/m3 and $0.1/m3 , respectively. Table 3 indicates the emergy indices that are used for this study. In this scenario, no regeneration plant is considered, such that all rik = 0 and all rkj = 0. It is also assumed that the total working days in a year (N) is 300 days. The upper and lower limits of fuzzy goals of the four L U L plants (TCU i and TCi , respectively) and the EIP authority (e and e , respectively) are given in Table 4. These goals are assumed to be based on a priori decisions made before negotiations begin among the parties involved (Aviso et al., 2010a, 2011; Tan et al., 2011). A minimum emergy solution of 5.28 × 1015 sej/y is generated when the model is solved. The corresponding water network is shown in Table 5, where the total freshwater and wastewater flowrates are 174 and 184 t/day, respectively. Of the four plants in the EIP, Sink 2 does not receive any freshwater, while Source 3 does not discharge any wastewater. It can also be seen that the optimal network has a total of four water reuse streams; none of the water from Sources 2 and 4 are reused in the system. Table 4 Fuzzy goals of EIP authority and plants. Party
Upper limit (eU or TCU i )
Lower limit (eL or TCLi )
EIP authority Plant 1 Plant 2 Plant 3 Plant 4
3.0 × 1018 sej/y $400,000/y $400,000/y $160,000/y $800,000/y
0 $200,000/y $100,000/y $80,000/y $400,000/y
Sink 1 Sink 2 Sink 3 Sink 4 Wastewater (184 t/day)
Freshwater (174 t/day)
Source 1
Source 2
Source 3
Source 4
87.5 0 64 22.5 n/a
0 20 16 0 64
0 0 0 0 20
12.5 0 0 37.5 0
0 0 0 0 100
Table 6 Decomposition of emergy contributions in case studies. Case 1
Case 2
Emergy (sej/y) e1 e2 e3 e4 e5
5.70 × 1014 2.61 × 1015 7.56 × 1014 0 1.58 × 1018
Total
1.584 × 1018
Share (%) 0.04 0.16 0.05 0 99.7
Emergy (sej/y) 3.21 × 1014 4.5 × 1015 1.90 × 1015 2.74 × 1016 9.3 × 1017 1.584 × 1018
100
Share (%) 0.03 0.47 0.20 2.84 96.5 100
Table 7 Decomposition of plant costs for two cases. Case 1
Plant 1 Plant 2 Plant 3 Plant 4
Case 2
Cost ($/y)
i
Cost ($/y)
i
256,350 150,000 107,770 604,050
0.72 0.83 0.65 0.49
263,127 150,000 105,250 526,254
0.68 0.83 0.68 0.68
The maximum level of satisfaction of the EIP authority or government’s emergy goal (G ) is 0.48, corresponding to a total emergy of 1.584 × 1018 sej/y. A detailed decomposition of emergy contributions is given in Table 6, where it can be seen that wastewater generation has the highest emergy share, because of the large value of the coefficient WW (Björklund et al., 2001). The levels of satisfaction for the cost goals of the participating plants (i ) are 0.72, 0.83, 0.65 and 0.49, respectively, with cost details being shown in Table 7. Note that each plant acts as both source and sink, and its total cost is the sum of the costs it incurs in each role. For this example, the overall satisfaction level () is equivalent to 0.48. This solution is optimal, since any change in network configuration may result in Table 8 Distance in meters between EIP plants and alternative regeneration plant for Case 2 (MDik or MDkj ). Source/sink
Regenerator option k=1
i or j = 1 i or j = 2 i or j = 3 i or j = 4
1350 1500 600 1350
k=2 2400 2150 1250 700
k=3 600 750 1250 2100
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Table 9 Optimal network for Case 2.
Sink 1 Sink 2 Sink 3 Sink 4 To regenerator (k = 3) Wastewater (108.4 t/day)
Freshwater (98.4 t/day)
From regenerator (k = 3)
Source 1
Source 2
Source 3
Source 4
0 0 70.4 28 n/a n/a
97.4 0 1.6 0 n/a n/a
0 0 0 22.1 72.9 5
0 0 0 0 0 20
0 16.7 0 9.9 0 23.4
2.6 3.3 8 0 26.2 60
at least one of the five parties involved getting a level of satisfaction of less than 0.48. 3.2. Case 2—reuse with regeneration Suppose that, upon assessment of the optimal solution in Case 1, it is deemed necessary to establish regeneration plant in order to further improve the degree of satisfaction of the EIP authority’s sustainability goal. Water streams from the sinks may thus undergo regeneration by means of a physical or chemical regeneration process; however, they can also be discharged to as waste from the EIP. The price of contaminant removal is $0.05/g, and the regenerated water has a concentration (Cout,k ) of 5 mg/l. All model parameters except those pertaining to regeneration are the same as in Case 1. The model is optimized by minimizing total cost of individual plant by establishment of a regeneration plant with optimum location in the EIP. As shown in Table 8, there are three regeneration plant options at different locations. The model can automatically select the optimal regeneration plant based on emergy and cost considerations. In this case, when the model is solved, the final network that results is shown in Table 9. The freshwater and wastewater flowrates are 98.4 and 108.4 t/day, respectively. The model also selects the third option (k = 3) for the regenerator with a flowrate of 99 t/day. In this network, only Sinks 3 and 4 use fresh water, while all sources discharge at least some of their water as waste (in the case of Source 2, all of the water is sent for discharge). Sources 1 and 4 feed into the regenerator, while Sinks 1 and 3 consume the regenerated water. There are also six direct interplant connections for water reuse. The level of satisfaction for the EIP authority emergy goal (G ) is 0.68, which is much higher than in the previous result. This corresponds to an emergy value of 9.64 × 1017 sej/y. Details of the contributions are again given in Table 6. The satisfaction levels of the four participating plants (i ) are 0.68. 0.83, 0.68 and 0.68, respectively, with the cost details being shown in Table 7. This time, the overall degree of satisfaction for the EIP () is also 0.68. The model is thus able to achieve a compromise among the conflicting goals of the five parties in the system. By comparison, if the only the EIP authority’s emergy goal is optimized using our previous approach (Taskhiri et al., in press) without regard for the individual plants’ economic interests, the resulting benefits would be less equitably distributed, with the sustainability goal being highly satisfied (G = 0.95), while the cost goals (i ) are inconsistently met, with satisfaction values ranging from 0.22 to 0.91. Note that, in such a scenario, the overall satisfaction level () is equivalent to that of the least satisfied party, which is 0.22. Furthermore, in practice, such inequitable distribution of benefits is likely to prove unattractive, and could result in the failure of the EIP initiative (Aviso et al., 2010a, 2011). 4. Conclusion Establishment of an EIP involves multiple stakeholders each with its own goals. From the government perspective, one of the main goals is to adopt policies to enhance sustainability through
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