Involving Acceptability in the Optimal Synthesis of Water Networks in

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Process Systems Engineering

Involving Acceptability in the Optimal Synthesis of Water Networks in Eco-Industrial Parks Guillermo Aguilar-Oropeza, Eusiel Rubio-Castro, and José María Ponce-Ortega Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b04419 • Publication Date (Web): 16 Jan 2019 Downloaded from http://pubs.acs.org on January 17, 2019

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Industrial & Engineering Chemistry Research

Involving Acceptability in the Optimal Synthesis of Water Networks in EcoIndustrial Parks Guillermo Aguilar-Oropeza,a Eusiel Rubio-Castro,b José María Ponce-Ortega a* a Chemical

Engineering Department, Universidad Michoacana de

San Nicolás de Hidalgo, Francisco J. Mujica S/N, Ciudad Universitaria, 58060, Morelia, Michoacán, México. b

Chemical and Biological Sciences Department, Universidad

Autónoma de Sinaloa, Av. de las Américas S/N, 8001000, Culiacán, Sinaloa, México.

* Corresponding author: Prof. José María Ponce-Ortega E-mail: [email protected] Phone: +52 443 3223500 ext. 1277 Fax: +52 443 3273584

ACS Paragon Plus Environment

Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Abstract This paper proposes a superstructure that considers the recycling and reuse of water in an eco-industrial park, where different industries share wastewater as well as a treatment system to meet their water needs in their process units. The involved flows as well as the treatment system are optimization variables and the objective is to synthesize the network that allows satisfying water requirements at the lowest possible cost for each of the involved industries, thus approaching to the individual utopian point of each participating industry. Based on the proposed superstructure, a mathematical model was implemented that involves mass and property balances of the involved streams. Also, the equations corresponding to the water treatment equipment as well as the economic objective functions for each of the involved industries are considered. Subsequently, the individual objectives of each industry are determined to find the utopian point. With this information, a strategy is implemented to allow obtaining a trade-off solution that is closest to the utopian point. The proposed strategy also allows the generation of a set of solutions that trade-off the different objectives to identify the weight of each industry involved in the eco-industrial park. A case study is presented to show the applicability of the proposed approach, where is possible to identify solutions that minimize the dissatisfaction of each of the involved plants.

Keywords: Eco-Industrial Parks, Optimization, Water Integration, Dissatisfaction, MultiStakeholder Approach.


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Introduction The chemical and process industry consumes large amounts of fresh water.1 This way, water for industrial use is treated and reused for other internal tasks, which allows saving fresh water and reducing its associated cost.2 In the different industrial areas, water plays a fundamental role for treating, processing and conditioning raw materials and products.3 In the last decades, the availability of this indispensable resource has diminished significantly, increasing its industrial price.4 This way, several works have proposed approaches based on process integration for the proper use of water in the industrial processes.5 One important alternative is the synthesis of water networks through recycling and reusing strategies.6 Considering the important economic and environmental benefits associated to the recycling and reuse networks,7 recently there has been considered the idea of synthesizing networks into an Eco-Industrial Park (EIP).8 The importance and improvements associated to EIPs have been highlighted in the one of Kalundborg in Denmark, which is described in Figure 1.9 Several approaches have been reported for synthesizing water networks in EIPs. In this context, Olsen and Polley reported a targeting approach for water networks involving multiple industrial plants.10 Liao et al. incorporated the idea of flexibility in the synthesis of water networks inside of an EIP.11 Lovelady and El-Halwagi presented an approach for synthesizing water and reuse networks in EIP.8 Aviso et al. reported a fuzzy mathematical programming approach for designing water networks in EIPs.12 Chew et al. reported a targeting approach for interplant water integration,13 which was then extended to include assisted schemes by Chew et al.14 Rubio-Castro et al. presented a discretization approach for synthesizing water networks in EIPs,15 then Rubio-Castro et al. extended this approach for retrofitting water networks in EIPs.16 Rubio-Castro et al. reported a global optimization approach for synthesizing water networks in EIP based on property constraints. Lee et al. incorporated batch operation in the synthesis of water networks in EIP.18 Boix et al. reviewed some methods for designing EIPs.19 López-Díaz et al. incorporated the effect of the surrounded watershed in the synthesis of EIPs.20 Kolluri et al. reported a robust approach for identifying alternative solutions in the synthesis of EIPs involving future events.21 Alnouri et al. incorporated water mains in the synthesis of water networks in EIP,22 and Alnouri et al. presented an approach for identifying the optimal layout in the synthesis of water networks ACS Paragon Plus Environment


in EIPs.23 Leong et al. incorporated a multi-objective optimization model for synthesizing water networks in EIPs.24 Ibrić et al. presented a mathematical optimization approach for synthesizing water networks in EIP for single pollutants and involving non-isothermal processes.25 Zhang et al. applied the idea of water networks in interplant integration for the steel industry,26 and Zhuming et al. reported a simplified method for optimization of interplant water networks.27 Tiu et al. involved the water quality for considering the water exchange in an EIP.28 Liu et al. incorporated predictive variations in a multi-period framework for synthesizing an EIP.29 Fadzil et al. included a centralized water reuse header in minimizing water in an EIP.30 Liu et al. incorporated heat integration in synthesizing water networks in EIP.31 Alnouri et al. reported an approach for using principal pipes during the synthesis of water networks in an EIP.32 It should be noticed that all the methodologies reported for water integration between different plants have considered the minimization of a single economic objective (i.e., total cost) for the entire EIP. Thus, previous methodologies put emphasis intrinsically on the bigger industrial plants involved in the EIP, because these have a bigger impact in the total economic objective function, which is associated to the bigger water needs. This way, the small plants involved in the EIP cannot obtain significant economic benefits in the obtained solutions considering the minimization of the total cost of the EIP as objective function, or even these small plants can obtain economic loses at expenses that the bigger plants can obtain better economic benefits with a greater impact in the total cost. This impacts drastically in the acceptability of these small plants to participate in the integrated EIP. Furthermore, if these small plants are not willing to participate in the EIP, this cannot be integrated, and this is equivalent to the case of integrating only single plants separately. Therefore, in order to promote the implementation of integrated EIP, it is needed that all the involved participants can obtain a solution near to the independent best solution, which is called the utopian point because usually when one plant is in its best solution, the others are far from their best solutions, and vice versa.33 Therefore, there is needed to develop a proper optimization strategy that allows synthesizing integrated EIPs that exchange water resources to minimize the fresh water consumption and at the same time each induvial plant operates near to the lowest possible cost. Thus, this paper presents a formal mathematical programming approach for


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synthesizing integrated eco-industrial plants for the proper use of water minimizing simultaneously the fresh water consumption and the individual cost for each involved plant, providing solutions that minimize the dissatisfaction for the entire problem as can be seen in Figure 2.

Addressed Problem The proposed model is based on a multi-objective optimization framework for the optimal integration of water networks in eco-industrial parks using the methodology proposed by Rubio-Castro et al.16 In the superstructure shown in Figure 3, which takes into consideration the recycling and reuse of water from the sources to the process sinks of the plant, besides contemplating the processes of the other participating plants, in addition there are shared treatment units which will treat the properties of the water flowrate to meet the requirements in the process sinks and the environmental specifications during the discharge to a natural water body, by all the involved plants within the superstructure. It should be noticed that each process source can send water to each process sink in the same plant or by interacting with the other plants, each interceptor and the waste that is discharged into the environment; the existence of a fictitious interceptor is considered, which is used when treatment is not required, and direct recycling of the sources of the process is also taken into account. The objective of the proposed model consists in a new proposal that involves a multiobjective procedure that contemplates the different objectives of the participating industries, which minimizes the total annual costs of each industry in such a way that it approaches to the individual utopian point. It should be noticed that this is the first work that addresses the acceptability of the involved plants for synthesizing an EIP; therefore, a proper optimization approach is proposed to trade-off the contradicting objectives.

Mathematical Model The proposed mathematical model consists of the following relationships.  Objective function The objective function of the proposed model is focused on the minimization of the total annual cost ( TACSP ) for each one of the participants within the EIP, which is based on



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the cost of fresh water for each plant ( WCSP ), the cost of regeneration of fresh water for each plant ( RCSP ) and the piping and pumping costs for each participating plant ( PCSP ). The calculation of each individual TAC for each participating plant ( TACSP ) is done with the following equation:

TACSP  WCSP  RCSP  PCSP , SP

(1)

It should be noticed that in all eco-industrial parks, the shared facilities always represent an important issue. We have considered here to share the associated costs through a proportional approach in the involved plants when there is a link between different plants.  Cost of fresh water The cost of fresh water for each plant is related to the hours of operation per year (

H Y ) and is determined with the following relationship: WCSP  H Y



jJ p wW p

fwsw, j CU w , SP

(2)

Where fwsw, j is the fresh water flow rate ( w ) in the process sink ( j ) and ( CU w ) is the established cost for fresh water.  Capital costs for the pipes The capital cost for the pipelines is determined by the length of the pipe that exists between the participating plants, in addition to other characteristics of the pipeline, such as the type of material, among others.13 In this equation, the capital cost for the pipelines is related with the cross-sectional area of the pipeline and includes the fixed and variable costs for each of the segments of the pipeline.   Di1, j fssi , j   D 2 fsi    xi1, j Di1, j CU P   P   i ,r i ,r  xi2,r Di2,r CU P     P     iI P r 1  3600  v  iI P jJ P  3600  v    PCSP  K F   3 5  Dr , j fisr , j   Di fsei    3 3 5 5  P    3600  v  xr , j Dr , j CU P   P   3600  v  xi Di CU P   iIP     r 1 jJ P  


(3)

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Where K F is the factor used to annualize capital costs, P represents the parameter for the capital cost of the pipeline between plants, Di1, j is the distance between sources and sinks,

Di2,r represents the distance between sources and interceptors, Dr,3 j indicates the distance 5 between interceptors and sinks, Di is the distance between the source and the discharge to

the environment,  is the density of water,

v is the speed of water and CU P

is the unit cost

of the pipe. It should be noticed that the pumping cost is proportional to the pumped flowrates; therefore, these costs have not been included because the solution does not change (due to Equation (3)) when these are included, and the mathematical model is complicated significantly when these are considered. This approach has been considered previously in the synthesis of eco-industrial parks.13-17  Cost of wastewater regeneration For the cost of wastewater regeneration, the capital and operating cost of the property interceptors for each plant are calculated as follows:

RCSP  K F  CU r FRr  H Y  fisr , j CUM r , L R

R

J

(4)

Where CU r is the investment cost coefficient, CUM r is the unit cost of mass removed in each interceptor and FRr is the water flow in the interceptors.  Mass balance for each process source The flow of each process source (𝐹𝑆𝑖) can be segregated and directed to each process sink (𝑓𝑠𝑠𝑖,𝑗), to each interceptor (𝑓𝑠𝑖𝑖,𝑟), and the environmental discharge (𝑓𝑠𝑒𝑖).

FSi   fssi , j   fsi i ,r  fsei ,i  I j J

(5)

r R

Equation 4 represents the reuse and recycling of water sources within the same plant and other plants, as well as in the interceptors, which are shared by the participating plants, in addition to consider the flow of wastewater discharged into the environment.  Mass balances and property mixing rules for each process sink



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The flow rate required by the process sinks (𝐹𝑈𝑗) is generated by the process sources sent to them (𝑓𝑠𝑠𝑖,𝑗), the interceptor flows (𝑓𝑖𝑠𝑟,𝑗), and fresh water (𝑓𝑤𝑠𝑤,𝑗) needed 𝑚𝑖𝑛 to satisfy the restrictions regarding to the lower and upper limits (𝜓𝑝(𝑃𝑢𝑚𝑎𝑥 𝑝,𝑗 ,𝜓𝑝(𝑃𝑢𝑝,𝑗 ) (Note

that to avoid numerical complications due to non-linear mixing relationships, the property operator is the optimization variable instead of the properties).

FU j   fisr , j   fssi , j   fwsw, j , j  J rR

iI

   Pi  fis rR

out p ,r

p

(6)

wW

    p  Ps p ,i  fssi , j     p  Pwp , w  fwsw, j     p  Pu max p . j  FU j , j  J ; p  P  wW    iI 

r, j

(7)

   Pi  fis rR

out p ,r

p

r, j

    p  Ps p ,i  fssi , j     p  Pwp , w  fwsw, j    p  Pu min p , j  FU j , j  J ; p  P  wW    iI 

(8) The terms 𝜓𝑝(𝑃𝑠𝑝,𝑖) and (𝜓𝑝(𝑃𝑤𝑝,𝑤) are the property operators for each process source (i) and each fresh water ( w ), respectively. The output property operator for each interceptor 𝜓𝑝(𝑃𝑖𝑜𝑢𝑡 𝑝,𝑟 ) and the flows that are sent to each sink are optimization variables; therefore, the term 𝜓𝑝(𝑃𝑖𝑜𝑢𝑡 𝑝,𝑟 )𝑓𝑖𝑠𝑟,𝑗 is bilinear and, consequently, Equations 7 and 8 are nonlinear and non-convex relationships.  Mass balances and property mixing rules for property interceptors A set of shared interceptors is used to satisfy the property constraints of the sinks in each of the participating plants and for the wastewater discharged into the environment. The conditions in terms of the flow rate and property operators at the input of each interceptor (𝐹𝑅𝑟,𝜓𝑝(𝑃𝑃𝑖𝑛 𝑝,𝑟) are determined by the flows that are sent from the process sources (𝑓𝑖𝑠𝑖,𝑟) and other interceptors (𝑓𝑖𝑠𝑟1,𝑟).

FR r   fsii ,r   fiir1 ,r , r  R iI

(9)

r1R r1  r

  P  Pi inp ,r  FRr    p  Ps p ,i  fsii ,r     p  Pi out p , r  fiir1, r  , r  R; p  P iI

riR r1 r

1


(10)

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Because the input and output conditions for the interceptors and the flow rates between them are unknown variables, then Equation 10 is a non-linear and non-convex relationship. Finally, the flow rate of each interceptor can be segregated and sent to the process sinks (𝑓𝑖𝑠𝑟,𝑗), the environmental discharge (𝑓𝑖𝑒𝑟), and other interceptors (𝑓𝑖𝑖𝑟,𝑟1). In addition, the operators of the output properties are determined by the efficiency factor for each interceptor (𝑅𝑅𝑟𝜓𝑝(𝑃𝑝)).

FRr   fisr , j   fiir ,r1  fier , r  R jJ

(11)

r1R r1  r

in  p  Pi out p , r    p  Pi p , r  RRr ,  P  p

(12)

p

This efficiency factor depends on the type, configuration and design of the treatment unit r, and it is determined before the optimization process by simulations or empirically to avoid additional numerical complexities. In this sense, in this work the efficiency factors are manipulated as constants to avoid additional numerical complications. This assumption works correctly because several interceptors with a given configuration and operating conditions can be simulated before optimization, providing a good correlation for their efficiency, while the associated costs depend only on the treated flow rate. To take into account limits for the flow rates and properties to obtain an adequate function for the treatment units, the model may include property and flow rate constraints for the inlet flow rate to each treatment unit. In this way, the optimization model must select the interceptor to be used and the treated flow rate. In addition, the efficiency factor could be positive or negative depending on the type of interceptor and the treated property.  Mass balance and mixing rules of properties for the mixer before the waste stream discharged to the environment The flow rate discharged to the environment (𝐹𝐸) and the value for the property 𝑚𝑖𝑛 operators (𝜓𝑝(𝑃𝑒𝑚𝑎𝑥 𝑝 ,𝜓𝑝(𝑃𝑒𝑝 ) in the waste stream discharged into the environment is

formed by the portions of the flows from the process sources (𝑓𝑠𝑒𝑖) and from the property interceptors (𝑓𝑖𝑒𝑟). The last term is a variable that multiplies the operator of the output property of the interceptors, which produces bilinear terms in Equations 14 and 15.



FE   fsii   fier iI

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(13)

rR

   Ps  fse      Pi  fie     Pe  FE , p  P

(14)

   Ps  fse      Pi  fie     Pe  FE , p  P

(15)

iI

p

p

iI

p ,i

p ,i

i

i

rR

rR

p

p

out p ,r

out p ,r

r

r

p

p

max p

min p

 Determination of pipe segments To take into account the unit cost of the pipe segments required in the optimal configuration, the following disjunction is included to activate the binary variables x associated with the existence of any pipe segment in the superstructure.  X m,n     X m , n  min  f m,n  M m,n       f m,n  0  max   f m ,n  M m ,n 

Where, 𝑋𝑚,𝑛 is a Boolean variable used to select the unit cost of any pipe segment, 𝑓𝑚,𝑛 is the flow rate in any pipe segment that starts at m and ends at n. Therefore, m could be a process source or a treatment unit, while n could be a process sink, a treatment unit or the 𝑚𝑎𝑥 waste that is discharged into the environment. In addition, 𝑀𝑚𝑖𝑛 𝑚,𝑛 and 𝑀𝑚,𝑛 are lower and

upper limits for the flow rate to determine the existence of any pipe segment.

f m,n  xm,n M mmin,n , m  M ; n  N

(16)

f m,n  xm,n M mmax ,n , m  M ; n  N

(17)

Equations 16 and 17 determine the existence of the pipe segment for any of the locations considered within the superstructure. Note that the binary variable 𝑥𝑚,𝑛 is a general representation of the binary variables (𝑥1𝑖,𝑗,𝑥2𝑖,𝑟,𝑥3𝑟,𝑗,𝑥5𝑖 ) used to determine any pipe segment in the superstructure and its associated cost (see Equation 3). 

Discharged flow rates fspSP    fsii ,r SP iI p

r


(18)

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Where fsii ,r is the flow rate from process source i to the interceptor for each plant within the EIP.  Treated water flow rate To determine the flow rate of the water that is treated, the following equation is needed, which is function of the flow rate from the process source to the interceptors.

fst   fsii ,r iI

(19)

r

 Water consumption within the EIP For calculating the water consumption within the EIP, it is determined as follows:

TOTALFRESW   fwsw, j W

(20)

J

Where fwsw, j is the fresh water flow rate ( w ) in the process sink ( j ).  Individual objectives for each of the participant plants For calculating the individual objectives for each of the participant plants, it is determined by the individua annual cost ( TACSP ):

OBJIND  TACSP

(21)

 Multi-stakeholder approach First, the following relationships are needed to use dimensionless variables and in the same order:

  SP

LOW TACSP  TACSP SP UP LOW  TACSP TACSP

(22)

Multi-criteria decision making allows taking decisions to achieve optimal results. First, the coordinates of the utopia point are used to represent the lower limit TAC LOW of the individual considered objective functions. The upper limit TAC UP represents the nadir solution for the different objective functions obtained in the previous solutions.  Compromise solution



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The compromise solution is the sum of each dimensionless variable of each of the involved industrial plants, obtained in Equation 22. This means that the gap between the solution for each objective function and its upper limit must be reduced (i.e., each objective function will be as close as possible to its maximum benefit), and there is no preference among the interested parties.

CS    sp

(23)

 Feasible solutions The calculation of a set of feasible solutions, the following relationship is used, where 10 scenarios with different weights are proposed for each participant, which are randomly generated. It is assumed that each objective function represents a different stakeholder and that these stakeholders have a different level of influence in the decisionmaking process; therefore, different weights (k) are assigned for each of the users ( PLANT ). If the feasible solution ( FCS ) is minimized, each of the individual goals will try to reach its lower limit. In this scenario, they must be analyzed. Therefore, for different weights, there are different individual solutions and different types of configurations.

FCS ( K )   ( NK K , PLANT )(TAC ), K

(24)

Results A case study is presented to show the applicability of the proposed approach, which has been previously addressed by Rubio-Castro et al.15.17 The data correspond to typical values found in the process industry (see Table 1).15 The values for the hours of operation of the plant (HY) are 8000 h/year, the factor used to annualize the capital costs (KF) for each year is 0.231/year (considering an annual interest rate of 10% and a horizon of 15 years), the density of the water is 1000 kg/m3 and the water speed is 2 m/s in the pipes.16 The material for the pipes is carbon steel with the parameters of the capital cost of the pipe between plants and inside a plant are equal to $7200 and $250, respectively. The distance for pipe segments between sources and sinks in the same plant is 50 m for the rest of the pipe segments is 200 m.15 This example consists of three plants with three process sources and three process sinks each one, whose data are shown in Table 1. In addition, two properties (composition of phenol and toxicity) are considered and a type of pure fresh water is available with a unit cost


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of 0.009 $/kg.16 For this example, the lower limits for property operators are 0.01 ppm for composition and 0.5% for toxicity, while the upper limits for the previous property operators are 0.22 ppm and 2.3%, respectively. For treatment, an electrocoagulation unit at different operating conditions was considered.34 The removal ratio (RR) for the considered interceptors are 0.02 and 0.15 for the property composition in interceptors 1 and 2, and 0.00 for toxicity in interceptor 3.17 The domains for each property operator are 0.01 ≤ 𝜑𝑐 ≤ 0.022 and 0.50 ≤ 𝜑𝑡𝑜𝑥 ≤ 2.300, and a partition was made in the value of 0.115 for the composition and 1.400 for the toxicity. Then, the original domain for the composition is covered by two new subdomains: 0.01 ≤ 𝜑𝑐 ≤ 0.1150 and 0.115 ≤ 𝜑𝑐 ≤ 0.220, while the original domain of toxicity is covered by the following two subdomains 0.50 ≤ 𝜑𝑡𝑜𝑥 ≤ 1.400 and 1.40 ≤ 𝜑𝑡𝑜𝑥 ≤ 2.30; and their possible combinations including the original domain.17 As mentioned above, the proposed model was coded on the GAMS software.35 The resulting optimization problem consists of 425 variables and 442 constraint and 76 binary variables. The DICOPT solver was used for solving this model and it required between 3 and 4 minutes for its solution using a computer with an Intel Core i7 processor at 2.40 GHz and 8 GB of RAM. It should be noticed that different global solvers like BARON and LINDO GLOBAL were used to solve the addressed model; however, the computation time increases a lot in solving each point of the proposed multi-stake holder approach, and the obtained results do not improve significantly. Therefore, there were provided proper initial guesses to obtain good solutions and to obtain solutions is a reasonable computation time. The solution of the problem was through the consideration of different factors which were the minimization of the total annual consumption for each of the participants (TACSP), the minimization of the compromise solution (CS), the minimization of water consumption (TOTALFRESH) and the minimization of the total water cost of the users within the EIP, the results of the minimization of each of the objectives are shown in Table 2. Minimizing the total annual cost for user 1 (TAC1) As can be seen in Table 2, when the TAC1 is minimized, the TAC obtained for this plant is $1,688,300 per year, which is generated by the associated costs of WCPSP with ACS Paragon Plus Environment


$1,972,400, PCPSP with $49,088.12, and the RCPSP with $1,442,000, while the TAC3 reaches a maximum value of $1,790,800 per year, the water consumption is 8,871.48 t/h and the total annual cost of the EIP considering the 3 industrial plants is $5,260,500 per year. The configuration for the EIP minimizing the TAC1 is presented in Figure 4. Minimizing the annual cost for user 2 (TAC2) By minimizing the TAC2, WCPSP costs are generated with $4,050,900 and PCPSP with $4,908,700 and the RCPSP with $1,562,000, thus giving the minimum value for the TAC of the industrial plant 2, which corresponds to $1,605,100 per year, and the maximum value for the total annual cost of the industrial plant 1 by $1,973,900 per year, with a water consumption within the eco-industrial park of 9,399.77 t/h and a total annual cost for the 3 plants of $5,293,700 for each year. Figure 5 shows the configuration of the EIP minimizing only the TAC2. Minimizing the total annual cost for industrial plant 3 (TAC3) The minimization of TAC3 results in a total annual cost of $1,507,100 for each year for the industrial plant 3 generated from the costs of WCPSP with $1,507,100, PCPSP with $463525.584 and the RCPSP with $1,443,600, however, for plant 1 the maximum total annual cost is $1,967,500 per year, with a consumption of fresh water within the EIP of 8886.02 t/h and a total annual cost of $5,250,600 (see Figure 6). Minimizing the water consumption for users of the industrial eco-park By minimizing the total water consumption for all EIP users, higher values for the TACSP of each participant are obtained, this is due to there is a greater cost of wastewater regeneration, which has an important impact on the total annual cost of the users. Where the highest TACSP is for industrial plant 2, being $7,623,700, the lowest cost is for industrial plant 1 with $7,734,100, whereas the total annual cost within the EIP is $22,778,100 for each year. It should be noticed that in the solution for the minimization of water consumption for the EIP (see Figure 7) that there is no discharge of waste water into the environment, this is because the configuration that is presented for EIP gives greater priority to reuse and recycling of water, for this reason the regeneration cost (RCPSP) as well as the piping costs increase significantly.


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Minimizing the compromise solution (CS) By minimizing the compromise solution for all the industrial plants of the EIP (see Figure 8), the TACSP obtained represents high costs for most of the participating plants, this is because the water costs, the cost of regeneration and the cost of the pipelines are incremented and thus the total annual cost for each user increases. Being industrial plant 1 the one with the highest cost with $16,003,000 for each year and the lowest cost for industrial plant 3 with an annual cost of $15,361,000, and with a global TAC within the eco-industrial park of $47,112,000, this being the highest total annual cost in all the previously presented EIP configurations. Minimizing the total TAC for the entire EIP Minimizing the total annual cost for the entire eco-industrial park (see Figure 9) yields a solution with lower individual costs (TACSP) for the involved plants than the case of the compromise solution. This way, the cost for industrial plant 1 is $1,745,100, for industrial plant 2 is $1,765,700, and for industrial plant 3 is $1,600,400, yielding a total annual cost for the entire EIP of $5,111,200, thus being the lowest global cost for the EIP for all the possible configurations previously proposed. The water consumption for the minimization of the total TAC of the EIP is 8,602.159 t/h, being the lowest total annual cost for the minimization of the individual TACSP. Multi-stakeholder approach For the multi-stakeholder approach, the generation of different weights is carried out in MATLAB® through sampling using a random distribution, which provides different priorities to each of the industrial plants involved in the eco-industrial park, this in turn determines the level of dissatisfaction that each user has by giving priority to one of the three plants involved in the EIP and the total annual costs for each company that are generated. Table 3 shows the different weights that were generated for each one of the users. For each of the weights that were generated for each industrial plant, which is the priority given to each company within the EIP, a percentage of dissatisfaction is obtained for the different participants, which will help to select the best solution for each participant in



which the different plants have the lowest total annual cost for each one. The percentage of dissatisfaction for each user is obtained through taking the TAC that was generated with the weighted solution or priority that was assigned to the company, minus the lowest total annual cost obtained for that plant ( TAC LOW ), divided by the highest total annual generated cost for the company ( TAC UP ) minus the lowest cost ( TAC LOW ) of the participant (see Equations 22, 23, and 24). The percentage of dissatisfaction is the sum of each individual dissatisfaction of the three industrial plants (see Table 4). As can be seen in Table 4, the total dissatisfaction percentages are between 2.25 and 2.46%, the obtained percentages of dissatisfaction are represented in the Pareto diagram shown in Figure 10. It should be noticed that 2.25% represents the lowest total dissatisfaction within the EIP, which is appreciated in the point that is closest to the utopia point, which represents the best scenario but infeasible, also this point is the farther from the nadir point, which represents the worst option obtained for the three industrial plants. For this reason, this point represents a high level of satisfaction for the different industrial plants within the EIP. Figure 11 presents the flowsheet that is obtained taking into account the result of the lowest total dissatisfaction within the EIP, which takes into account all the activities that are carried out within the EIP, as well as the use and reuse of water. The associated costs for the lowest point of dissatisfaction are $1,890,000 for the total annual cost for user 1 associated with the costs of WCPSP with $3,950,70, PCPSP with $60638.092 and RCPSP with $1,343,000, for user 2 a total annual cost of $1,653,200 generated by WCPSP with $1,813,50, PCPSP with $37537.913 and RCPSP with $1,434,300, for user 3 a total annual cost by $1, 577, 500 generated by the costs of WCPSP with $45035.573, PCPSP with $98175.893 and the RCPSP with $1,434,300 and with a consumption of fresh water within the eco-industrial park of 8631 t/h.

Conclusions This paper has presented an optimization approach for synthesizing eco-industrial parks for the proper use of water accounting for the acceptability of the involved industries. The acceptability has been measured through the use of an optimization approach that allows determining feasible solutions that are close to the utopian point for each of the involved parts. The proposed approach allows determining different pareto solutions that can be useful


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for the decision makers. Also, the pareto plot allows identifying the dissatisfaction level for the different industrial plants involved in the EIP. A case study has been presented to show the applicability of the proposed approach. The results have shown that it is very important to consider the induvial objectives for the involved plants for synthesizing an eco-industrial park, because this way is possible to decrease the dissatisfaction level for the involved parts and to obtain solutions close to the utopia point.

Nomenclature Parameters

CU P

Pipe unit cost, US$

CU r

Investment cost coefficient for interceptor, US$

CUM r

Unit cost for mass removed in each interceptor, US$/kg

CU w

Fresh water unit cost, US$/kg

Di1, j

Distance between source i and sink j, m

Di2,r

Distance between source i and interceptor r, m

Dr,3 j

Distance between interceptor r and process sink j, m

Di5

Distance between source i and environmental discharge, m

FSi

Flow rate of process source i, kg, m

FU j

Flow rate of process sink j, kg/h

HY

Plant operating hours per year, h/year

KF

Factor used to annualize the capital cost, 1/year



M max fier

Upper limit for the pipe segment from interceptor r to the waste discharge to the environmental, kg/h

M max fisr , j

Upper limit for the pipe segment from interceptor r to process source j, kg/h

M max fsei

Upper limit for the pipe segment from process source i to the waste discharge to the environmental, kg/h

M max fsii , r

Upper limit for the pipe segment from process source i to interceptor r, kg/h

M max fssi , j

Upper limit for the pipe segment from process source i process sink j, kg/h.

M min fier

Lower limit for the pipe segment from interceptor r to waste discharge to the environmental, kg/h

M min fisr , j

Lower limit for the pipe segment from interceptor r to process source j, kg/h

M min fsei

Lower limit for the pipe segment from process source i to the waste discharge to the environmental, kg/h

M min fsii , r

Lower limit for the pipe segment from process source i to interceptor r, kg/h

M min fssi , j

Lower limit for the pipe segment from process source i to process sink j,

kg/h

P

Parameter for cross-plant pipeline capital cost

RRr , p ( PP )

Efficiency factor of interceptor r for property operator p, dimensionless



Water density, kg/m3

 Pmin

Lower limit for property operator p

 Pmax

Upper limit for property operator p

v

Velocity, m/s


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

Cost function exponent, dimensionless

Variables CS

Compromise solution

FE

Flow rate in the waste discharge to the environmental, kg/h

fier

Flow rate from interceptor r to waste discharge to the environmental, kg/h

fiir1 ,r

Flow rate from interceptor r1 to interceptor r, kg/h

fisr , j

Flow rate from interceptor r to process sink j, kg/h

FRR

Flow rate in the interceptor r, kg/h

fsei

Flow rate form process source i to waste discharge to environmental, kg/h

fsii ,r

Flow rate from process source i to interceptor r, kg/h

fssi , j

Flow rate from process source i to process sink j, kg/h

fwsw, j

Flow rate of the fresh water w in the process sink j, kg/h

fspSP

Piping cost for each for plant, US$

fst

Total treated flow, kg/h

fwsw, j

Flow rate of the fresh water w in the process sink j, kg/h

PCSP

Cross-plant pipeline capital cost per each plant, US$/year

RCSP

Regeneration cost per each plant, US$/year

TACSP

Total annual cost per each plant, US$/year

TAC1

Total annual cost per plant 1, US$/year



TAC2


TAC3


TOTALFRESW

Total fresh water consumed, kg/h

WCSP

Fresh water cost per each plant, US$/year

p

Property operator p



1

Compromise solution for plant 1, dimensionless

2


3


 p (Piinp ,r )

Property operator for property p inlet to interceptor r

 p (Pi out p ,r )

Property operator for property p outlet from interceptor r

Binary variables

X i1, j

Existence of pipe segment sources-sink

X i2,r

Existence of pipe segment sources-interceptor

X r,3 j

Existence of pipe segment interceptor-sink

X r5

Existence of pipe segment interceptor-waste

Subscripts

i

Process sources

j

Process sinks

L

Number of properties considered


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N

Number of objective functions

p

Properties

r

Property interceptors

w

Types of fresh water

Sets

I

{J=1, 2…, NSinks j is a set of process sinks}

J

{I=1, 2…, NSources i is a set of process sources}

K

Number of scenarios

R

{r=1, 2, … NInterceptors R is a set of property interceptors}

SP

{SP=1, 2, … NNumber of plants SP is a set of number plants}

W

{w=1, 2, … NType of freshwates W is a set of fresh water types}

Superscripts LOW

Lower limit

UP

Upper limit

Author Information Corresponding Author *Ponce-Ortega José M. Tel. +52-443-3223500. Ext. 1277. Fax. +52-443-3273584. E-mail: [email protected]

Notes The authors declare no competing financial interest.

Acknowledgements The authors appreciate the financial support provided by the Mexican National Council for Science and Technology (CONACYT).



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[11] Liao, Z.W.; Wu, J.T.; Jiang, B.B.; Wang, J.D.; Yang, Y.R. Design methodology for ﬂexible multiple plant water networks. Ind. Eng. Chem. Res. 2007, 46: 4954– 4963. [12] Aviso, K.B.; Tan, R.R.; Culaba, A.B. Designing eco-industrial water exchange networks using fuzzy mathematical programming. Clean Technol. Environ. Policy. 2010, 12: 353–363. [13] Chew, I.M.L.; Foo D.C.Y.; Ng, D.K.S.; Tan, R.R. Flowrate targeting for interplant resource conservation network. Part 1: Unassisted integration scheme. Ind. Eng. Chem. Res. 2010a, 49: 6439–6455. [14] Chew, I.M.L.; Foo, D.C.Y.; Tan, R.R.; Flowrate targeting for interplant resource conservation network. Part 2: Assisted integration scheme. Ind. Eng. Chem. Res. 2010b, 49: 6456–6468. [15] Rubio-Castro, E.; Ponce-Ortega, J.M.; Nápoles-Rivera, E.; El-Halwagi, M.M.; SernaGonzalez, M.; Jiménez-Gutiérrez, A. Water integration of eco-industrial parks using a global optimization approach. Ind. Eng. Chem. Res. 2010, 49(20): 99459960. [16] Rubio-Castro, E.; Ponce-Ortega, J.M.; Serna-González, M.; El-Halwagi, M.M. Optimal reconfiguration of multi-plant water networks into an eco-industrial park. Comput. Chem. Eng. 2012, 44: 58-83. [17] Rubio-Castro, E.; Ponce-Ortega, J.M.; Serna-González, M.; El-Halwagi, M. M.; Pham, V. Global optimization in property-based interplant water integration. AIChE J. 2013, 59: 813–833. [18] Lee, J.Y.; Chen, C.L.; Lin, C.Y.; Foo, D.C.Y. A two-stage approach for the synthesis of inter-plant water networks involving continuous and batch units. Chem. Eng. Res. Des. 2014, 92: 941–953. [19] Boix, M.; Montastruc, L.; Pibouleau, L.; Azzaro-Pantel, C.; Domenech, S. Optimization methods applied to the design of eco-industrial parks: a literature review. J. Cleaner Prod. 2015, 87: 303-317.



[20] López-Díaz, D.C.; Lira-Barragan, L.F.; Rubio-Castro, E.; Ponce-Ortega, J.M.; ElHalwagi, M.M. Synthesis of eco-industrial parks interacting with a surrounding watershed. ACS Sustainable Chem. Eng. 2015, 3(7): 1564-1578. [21] Kolluri, S.S.; Esfahani, I.J.; Yoo, C. Robust fuzzy and multi-objective optimization approaches to generate alternate solutions for resource conservation of ecoindustrial park involving various future events. Proc. Saf. Environ. Protec. 2016, 103: 424-441. [22] Alnouri, S.Y.; Linke, P.; El-Halwagi, M.M. Synthesis of industrial park water reuse networks considering treatment systems and merged connectivity options. Comput. Chem. Eng. 2016a, 91: 289-306. [23] Alnouri, S.Y.; Linke, P.; Stijepovic, M.; El-Halwagi, M.M. On the identification of the optimal utility corridor locations in interplant water networks synthesis. Environ. Prog. Sustainable Energy. 2016b, 35: 1492-1511. [24] Leong, Y.T.; Lee, J.Y.; Tan, R.R.; Foo, J.J.; Chew, I.M.L. Multi-objective optimization for resource network synthesis in eco-industrial parks using an integrated analytic hierarchy process. J. Cleaner Prod. 2017, 143: 1268-1283. [25] Ibrić, N.; Ahmetović, E.; Kravanja, Z.; Maréchal, F.; Kermani, M. Synthesis of single and interplant non-isothermal water networks J. Environ. Manag. 2017, 203: 1095-1117. [26] Zhang, K.; Zhao, Y.; Cao, H.; Wen, H. Multi-scale water network optimization considering simultaneous intra- and inter-plant integration in steel industry. J. Cleaner Prod. 2018, 176: 663-675 [27] Zhuming, L.V.; Yun, Song.; Chen Chen, B.J.; Hui, Sun.; Zeyu, Lyu. A novel step-bystep optimization method for interplant water networks. J. Environ. Manag. 2018, 213: 255-270. [28] Tiu, B.T.; Cruz, D.E. An MILP model for optimizing water exchanges in eco-industrial parks considering water quality. Res. Cons. Recyc. 2017, 119: 89-96.


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[29] Liu, L.; Wang, J.; Song, H.; Du, J.; Yang, F. Multi-period water network management for industrial parks considering predictable variations. Comput. Chem. Eng. 2017, 104: 172-184. [30] Fadzil, A.F.; Alwi, S.R.W.; Manan, Z.; Klemes, J.J. Industrial site water minimisation via one-way centralized water reuse header. J. Clean. Prod. 2018, 200: 174-187. [31] Liu, L.; Song, H.; Zhang, L.; Du, J. Heat-integrated water allocation networks synthesis for industrial parks with sequential and simultaneous design. Comput. Chem. Eng. 2018, 108: 408-424. [32] Alnouri, S.Y.; Linke, P.; Bishnu, S., El-Halwagi, M.M. Synthesis of interplant water networks using principal pipes. Part 1: Network representation. Pocess Integr. Optim. Sustain. 2018, 1-11. [33] Dowling, A.W.; Ruiz-Mercado, G.; Zavala, V.M. A framework for multi-stakeholder decision-making and conflict resolution. Comput. Chem. Eng. 2016, 90: 136-150. [34] Tovar-Facio, J.; Lira-Barragán, L.F.; Nápoles-Rivera, F.; Bamufleh, H.S.; PonceOrtega, J.M.; El-Halwagi, M.M. Optimal synthesis of refinery property-based water networks with electrocoagulation treatment systems. ACS Sustainable Chem Eng. 2015, 4(1): 147-158. [35] Brooke, A.; Kendrick, D.; Meeraus, A.; Raman, R. GAMS, A user’s guide. GAMS Development Corporation, Washington D.C., UDA. 2018.



Page 26 of 42

Table 1. Stream data for the case study Composition(ppm) Plants

Sources 1

2

3

Flowrate (kg/h)

Min

Composition Toxicity

Min

Max

1

2900

0

0.013

0

1.5

2

2450

0

0.011

0

0.8

3

8083

0

0.013

0

1.3

4

3900

0

0.011

0

1.8

5

3279

0

0.1

0

6

3100

0

0.1

0

7

1800

0

0.01

0

1

8

1750

0

0.04

0

0.8

9

2000

0

0.02

0.02

1.3

0

0.075

0

0

Waste Property

Toxicity (%) Max

Interceptors

Plants

Sinks

Flowrate (kg/h)

Composition (ppm)

Toxicity (%)

1

2900

0.033

1.8

2

2450

0.022

0.5

3

8083

0.016

2.3

4

3900

0.024

1.5

1.2

5

3279

0.22

1.5

0.8

6

3100

0.01

0.75

7

1800

0.16

1.4

8

1750

0.1

1.75

9

2000

0.11

1.3

Cur (US$)

1

2

3

0 CUMr (US$/kg)

0 RR

1

7500

0.0065

0.02

2

5000

0.0033

0.15

3

9200

0.0098

0


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Table 2. Table of minimization of the objectives TAC TAC 1 MIN TAC 2 MIN TAC 3 MIN MIN TOTALFRESW MIN CS MIN TACTOTAL

TAC 1 TAC 2 TAC 3 TAC TOTAL $US/year $US/year $US/year $US/year TOTALFRESW(t/h) 1,688,300 1,781,400 1,790,800 8,871.48 5,260,500 1,973,900 1,605,100 1,714,700 9,399.76 5,293,700 1,967,500 1,776,000 1,507,100 8,886.02 5,250,600 7,734,100 7,623,700 7,420,300 22,778,100 16,003,000 15,748,000 15,361,000 74.01 47,112,000 1,745,100 1,765,700 1,600,400 8,602.159 5,111,200



Table 3. Considered weights for generating different feasible solutions. Scenarios Plant 1 Plant 2 Plant 3 1 0.0855 0.134 0.1024 2 0.0396 0.1453 0.113 3 0.0396 0.1521 0.0955 4 0.0828 0.1607 0.1058 5 0.2521 0.0265 0.1701 6 0.2184 0.0335 0.1509 7 0.0397 0.2397 0.0786 8 0.0749 0.3244 0.056 9 0.1665 0.0225 0.1981 10 0.1584 0.054 0.2013


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Table 4. Dissatisfaction for different feasible solutions identified through the multistakeholder approach. K 1 2 3 4 5 6 7 8 9 10

TCA1 $US %DIS 1 TAC2 $US %DIS2 TAC3 $US %DIS 3 FRESHWATER t/h TAC TOTAL $US % TOTAL 1,853,400 1.15 1,643,200 0.27 1,626,700 0.86 8628.875 5,123,300 2.29 1,890,000 1.41 1,653,200 0.34 1,577,500 0.51 8631.257 5,120,800 2.26 1,899,800 1.48 1,623,300 0.13 1,612,400 0.76 8628.875 5,135,500 2.37 1,870,900 1.28 1,622,600 0.12 1,642,700 0.98 8628.875 5,136,200 2.38 1,716,000 0.19 1,800,300 1.38 1,630,200 0.89 8629.797 5,146,500 2.46 1,716,200 0.19 1,789,900 1.31 1,632,500 0.91 8617.882 5,133,300 2.41 1,898,600 1.47 1,622,100 0.12 1,622,800 0.84 8629.236 5,143,500 2.42 1,811,800 0.86 1,622,600 0.12 1,690,300 1.32 8628.875 5,124,800 2.31 1,798,900 0.77 1,791,800 1.32 1,547,700 0.29 8629.049 5,138,300 2.39 1,805,500 0.82 1,788,400 1.30 1,547,800 0.29 8628.996 5,141,700 2.41



Caption For Figures Figure 1. Schematic representation for and EIP. Figure 2. Considering individual objectives. Figure 3. Implemented superstructure. Figure 4. Configuration of the eco-industrial park minimizing TAC1. Figure 5. Configuration of industrial eco-park minimizing TAC2. Figure 6. Configuration of industrial eco-park minimizing TAC3. Figure 7. Configuration for the eco-industrial park minimizing the total water consumption. Figure 8. Eco-industrial park configuration minimizing the compromise function. Figure 9. Eco-industrial park configuration minimizing the total TAC of the entire EIP. Figure 10. Pareto diagram for the results of the case study. Figure 11. Configuration of the eco-industrial park with the lowest percentage of total dissatisfaction.


Page 30 of 42

Page 31 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Pig Farmers Yiest

Plaster Boad Plant Gypsum Bioplant

Steam

Gas

Steam

Waste Heat

Oil Refinery Sulfur

Electric Power Station

Waste Heat(return)

Waste Heat

Gas

Volatile Ashes

Sulfuric Acid Producer

Fish Culture Sludge

Cement Factory

Local Farmers

Figure 1. Schematic representation for and EIP.



PLANT 1

Page 32 of 42

ENV I R ONM ENT AL I M P ACT

T A C1 ENV I R ONM ENT AL I M P ACT ENV I R ONM ENT AL I M P ACT

T A C2

PLANT 2

T A C3

PLANT 3

TAC GENERAL

Figure 2. Considering individual objectives.


Page 33 of 42

PLANT 1 I1 fsi

fss1,1

J1

fss1,2

1,1

fsi2,1

J2

I2

fsi

1,2

fsi3,1

fsi

fsi

2,2

I3

1,3

fsi

Fsi

fss3,3

J3

2,3

fsi3,3

fis R1

R2

R3

3

fss1,4

fie1,2,3

fisRn,Jn

5

5,3

fsi

6,3

fsi

fse fse fse

4

4,3

fsi 4,1

1

1,3

fsi fsi

fse fse fse

2

3,2

INTER-PLANT INTERCEPTORS

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


I4

fss4,4

J4

I5

fss

J5

I6

fss

6

5,2

fsi5,1 fsi6,1

fsi6,2

5,5

6,6

J6

PLANT 2

Figure 3. Implemented superstructure.


FE


PLANT 1 J1

I1 401.643Ton/h


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 42

970.383Ton/h

J2

I2 I3

3147.723Ton/h

J3

8871.476Ton/h

R2

R3

R4

FE J4

I4 321.916Ton/h

I5

1344.248Ton/h

J5

I7

J7

I8

J8

I9

J9

238.636Ton/h

1374.199Ton/h

J6

I6

10.326Ton/h

PLANT 2

PLANT 3

Figure 4. Configuration of the eco-industrial park minimizing TAC1.


Page 35 of 42

PLANT 1 655.781 Ton/h

J1

I1

2900 Ton/h


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


747.466 Ton/h

I2

1533.654 Ton/h

J2

676.176 Ton/h

J3

I3

9392.041 Ton/h

R2

R3

R4

43.317 Ton/h

FE

418.705 Ton/h

2000 Ton/h

J4

I4

450 Ton/h

J7

I7

368.051 Ton/h 549.401 Ton/h

I5

1380.749 Ton/h

I6

J5

I8

J6

I9

PLANT 2

693.069 Ton/h

J8 J9

PLANT 3

Figure 5. Configuration of industrial eco-park minimizing TAC2.



PLANT 1 679.955 Ton/h

381.641 Ton/h 891.131 Ton/h

J1

I1

986.998 Ton/h


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 42

198.574 Ton/h

J2

I2 255.087 Ton/h

J3

I3

8886.018 Ton/h

R2

R3

R4

FE

200.349 Ton/h 1942.653 Ton/h

789.288 Ton/h

I4

J4

I7

I5

J5

I8

J8

110.294 Ton/h

547.059 Ton/h

J6

I6

J7 279.412 Ton/h

666.667 Ton/h

J9

I9

PLANT 3

PLANT 2

Figure 6. Configuration of industrial eco-park minimizing TAC3.


Page 37 of 42

PLANT 1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


R1

R2

R3

I1

J1

I2

J2

I3

J3

R4

FE I4

J4

I7

J7

I5

J5

I8

J8

I6

J6

I9

J9

PLANT 2

PLANT 3

Figure 7. Configuration for the eco-industrial park minimizing the total water consumption.



PLANT 1 I1

311.076 Ton/h

J1

I2

68.683 Ton/h

J2

2681.25 Ton/h

J3

889.949 Ton/h


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 42

I3

8055.108 Ton/h

R2

R3

FE

1451.243 Ton/h

I4

J4

I7

J5

I8

J6

I9

J7

782.09 Ton/h

173.01 Ton/h

I5

1372.468 Ton/h

I6

PLANT 2

368.696 Ton/h

J8 J9

PLANT 3

Figure 8. Eco-industrial park configuration minimizing the compromise function.


Page 39 of 42

PLANT 1 J1

I1 1236.373 Ton/h

691.639 Ton/h

J2

I2

662.373 Ton/h


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


I3

2621.379 Ton/h

J3

8628.735 Ton/h

R2

R3

FE I4

J4

I7

40.625 Ton/h

J5

I8

312.821 Ton/h

J6

I9

J7

51.449 Ton/h

881.32 Ton/h

I5

837.191 Ton/h

J8

1379.184 Ton/h

I6

PLANT 2

J9

PLANT 3

Figure 9. Eco-industrial park configuration minimizing the total TAC of the entire EIP.



Figure 10. Pareto diagram for the results of the case study.


Page 40 of 42

Page 41 of 42

PLANT 1 1215.49Ton/hr

R2

R3

I1

363.27Ton/hr

J1

I2

692.959Ton/hr 1607.006Ton/hr

J2

I3

255.147Ton/hr 3212.323Ton/hr

J3

8631.257Ton/hr

R4 2681.25Ton/hr

1105.957Ton/hr


1843.772Ton/hr

1417.457Ton/hr

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


J4

I4 492.488Ton/hr 516.073Ton/hr

FE

806.893Ton/hr

I5

J7

I7 1666.667Ton/hr

J5

1378.26Ton/hr 1286.303Ton/hr

I6

1725.615Ton/hr

I8 1276.364Ton/hr

J8

87.15Ton/hr J9

I9

J6

537.333Ton/hr

332.543Ton/hr

PLANT 2

PLANT 3

Figure 11. Configuration of the eco-industrial park with the lowest percentage of total dissatisfaction.



For Table of Contents Use Only

PLANT 1 I1 fsi

fss1,1

J1

fss1,2

1,1

fsi2,1

J2

I2

fsi

1,2

fsi3,1

fsi

fsi

2,2

I3

1,3

fsi

Fsi

fss3,3

J3

2,3

fsi3,3

fis R1

R2

R3

1,3

fie1,2,3

fisRn,Jn

fsi

fsi

6,3

FE

5

5,3

fsi

fse fse fse

4

4,3

4,1

1

3

fss1,4

fsi fsi

fse fse fse

2

3,2


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 42 of 42

I4

fss4,4

J4

I5

fss

J5

I6

fss

6

5,2

fsi5,1 fsi6,1

fsi6,2

5,5

6,6

J6

PLANT 2

Synopsis: This paper presents an optimization approach for synthesizing eco-industrial parks for the proper use of water accounting for the acceptability of the involved industries.


Involving Acceptability in the Optimal Synthesis of Water Networks in

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