Abstract
A multi-period planning approach for water reuse and regeneration networks in Eco-Industrial Parks (EIPs) is presented. The objective of the optimization problem is to determine the lowest network cost design for such systems, by taking into account an entire planning horizon. A source-to-sink mapping approach has been proposed to formulate the multi-period planning problem. Water sources can either be allocated to water sinks, treatment units or discharged to environment. Freshwater streams and treated water are made available to mix with water sinks to enable reuse between plants. Waste water is allowed to be discharged into environment at threshold contaminant levels. The problem has been illustrated initially with two-stage centralized treatment unit, then by considering a hybrid treatment setup consisting of both centralized and decentralized options. The results obtained indicate considerable cost reductions, when compared to those developed separately for each individual period. Moreover, a decrease in the complexity of the water networks has also been observed, when simultaneously considering the entire planning horizon.
Funding statement: Qatar National Research Fund, (Grant/Award Number: ‘NPRP grant no. 4-1191-2-468’).
Nomenclature
Diameter of pipe connecting ith source in plant p1 to jth sink in plant p2
Flowrate between ith source in plant p1 to jth sink in plant p2 during time period t
Flowrate between ith source in plant p1 to r1 interceptor of stage 1 during time period t
Flowrate to be treated by interceptor r1 of stage 1 during time period t.
Flowrate between r1 interceptor of stage 1 to r2 interceptor of stage 2 during time period t
Flowrate to be treated by interceptor r2 of stage 2 during time period t.
Flowrate between r2 interceptor of stage 2 to environment during time period t.
Flowrate between r2 interceptor of stage 2 to jth sink in plant p2 during time period t
Flowrate between ith source in plant p1 for waste water discharge in time period t
Flowrate between fresh water source to jth sink of plant p2 in time period t
Concentration of contaminant b in fresh water
Concentration of contaminant b in ith source of plant p1 time period t
Concentration of contaminant b jth sink of plant p2 in time period t.
Inlet concentration of contaminant b in interceptor r1 of stage 1 in time period t.
Removal ratio of interceptor r1 in stage 1.
Outlet concentration of contaminant b in interceptor r1 of stage 1 during time period t.
Inlet concentration of contaminant b in interceptor r2 of stage 2 in time period t.
Removal ratio of interceptor r2 in stage 2.
Outlet concentration of contaminant b in interceptor r2 of stage 2 during time period t.
period t.
Binary variable representing connection between ith source and interceptor r1 in time period t.
Binary variable representing connection between interceptor r2 and jth sink in time period t.
Binary variable representing connection between interceptor r2 and environment in time period t.
Binary variable representing the existence of rth interceptor of stage s in time period t.
Maximum value of flowrate between sources and sinks.
Maximum value of flowrate between sources and interceptors.
Maximum value of flowrate between interceptors and sinks
Maximum value of flowrate between interceptor and environment
Maximum value of flowrate at any treatment unit.
References
1. El-Halwagi MM, Manuosiouthakis V. Synthesis of mass exchanger networks. AIChE J. 1989;35:1233–1244.10.1002/aic.690350802Search in Google Scholar
2. Wang YP, Smith R. Design of distributed effluent treatment systems. Chem Eng Sci. 1994;49:3127–3145.10.1016/0009-2509(94)E0126-BSearch in Google Scholar
3. Faria D, Bagajewicz M. Global optimization of water management problems using linear relaxations and bound contraction methods. Ind Eng Chem Res. 2011;50:3738–3753.10.1021/ie101206cSearch in Google Scholar
4. Kuo WC, Smith R. using water cascade analysis technique. AIChE Journal 2004;50:3169–3183. Designing for the interactions between water use and effluent treatment. Chem Eng Res Des. 1998;76:287–301.10.1205/026387698524938Search in Google Scholar
5. Foo DC, Manan ZA, Tan YL. Use cascade analysis to optimize water networks. Chem Eng Prog. 2006;102:45–52.Search in Google Scholar
6. Parand R, Yao HM, Tades MO, Pareek V. Targeting water utilities for threshold problem without waste discharge. Chem Eng Res Des. 2013;91:2569–2578.10.1016/j.cherd.2013.05.004Search in Google Scholar
7. Pombo FR, Magrini A, Szklo A. An analysis of water management in Brazilian petroleum refineries using rationalization techniques. Resour Conserv Recycl. 2013;73:172–179.10.1016/j.resconrec.2013.02.004Search in Google Scholar
8. Liu ZH, Shi J, Liu ZY. Design of wastewater treatment networks with single contaminant. Chem Eng J. 2012;192:315–325.10.1016/j.cej.2012.03.060Search in Google Scholar
9. Takama N, Kuriyama T, Shiroko K, Umeda T. Optimal water allocation in a petroleum refinery. Comput Chem Eng. 1980;4:251–258.10.1016/0098-1354(80)85005-8Search in Google Scholar
10. El-Halwagi MM, Gabriel F, Harell D. Rigorous graphical targeting for resource conservation via material recycle/reuse networks. Ind Eng Chem Res. 2003;42:4319–4328.10.1021/ie030318aSearch in Google Scholar
11. Gabriel F, El-Halwagi MM. Simultaneous synthesis of waste interception and material reuse networks: problem reformulation for global optimization. Environ Prog. 2005;24:171–180.10.1002/ep.10081Search in Google Scholar
12. Olesen SG, Polley GT. Dealing with plant geography and piping constraints in water network design. Trans Inst Chem Eng. 1996;74:273–276.10.1205/095758296528626Search in Google Scholar
13. Spriggs D, Lowe E, Watz J, El-Halwagi MM, Lovelady EM.. Design and development of eco-industrial parks. AIChE spring meeting, NewOrleans, LA; 2004 April25–29.Search in Google Scholar
14. Chew IML, Foo DC. Automated targeting for inter-plant water integration. Chem Eng J. 2009;153:23–36.10.1016/j.cej.2009.05.026Search in Google Scholar
15. Castro EC, Ortega JM, Gonzalez MS, El-Halwagi MM. Optimal reconfiguration of multi-plant water networks into an eco-industrial park. Comput Chem Eng. 2012;44:58–83.10.1016/j.compchemeng.2012.05.004Search in Google Scholar
16. Klemes JJ. Industrial water recycle/reuse. Curr Opin Chem Eng. 2012;1:238–245.10.1016/j.coche.2012.03.010Search in Google Scholar
17. Kastner CA, Lau R, Kraft M. Quantitative tools for cultivating symbiosis in industrial parks; a literature review. Appl Energy. 2015;155:599–612.10.1016/j.apenergy.2015.05.037Search in Google Scholar
18. Liu L, Wang J, Song H, Du J, Yang F. Synthesis of water networks for industrial parks considering inter-plant allocation. Comput Chem Eng. 2016;91:307–317.10.1016/j.compchemeng.2016.03.013Search in Google Scholar
19. Chen C-L, Hung S-W, Lee J-Y. Design of inter-plant water network with central and decentralized water mains. Comput Chem Eng. 2009;34:1522–1531.10.1016/j.compchemeng.2010.02.024Search in Google Scholar
20. Tiu BC, Cruz DE. An MILP model for optimizing water exchanges in eco-industrial parks considering water quality. Resour Conserv Recycl 2016. DOI:10.1016/j.Search in Google Scholar
21. Ramon MA, Boix M, Aussel D, Montastruc L, Domenech S. Water integration in eco-industrial parks using a multi-leader-follower approach. Comput Chem Eng. 2016;87:190–207.10.1016/j.compchemeng.2016.01.005Search in Google Scholar
22. Ramon MA, Boix M, Aussel D, Montastruc L, Domenech S. Optimal design of water exchanges in eco-industrial parks through a game theory approach. Comput Chem Eng. 2016;38:1177–1182.Search in Google Scholar
23. Montastruc L, Boix M, Pibouleau L, Pantel CA, Domenech S. On the flexibility of an eco-industrial park (EIP) for managing industrial water. J Clean Prod. 2013;43:1–11.10.1016/j.jclepro.2012.12.039Search in Google Scholar
24. Lee JY, Chen CL, Lin Y, Foo DC. A two stage approach for synthesis for synthesis of inter-plant water networks involving continuous and batch network. Chem Eng Res Des. 2014;92:941–953.10.1016/j.cherd.2013.08.008Search in Google Scholar
25. Alnouri SY, Linke P, El-Halwagi MM. Water integration in industrial zones: a spatial representation with direct recycle application. Clean Technol Environ Policy. 2014;16:1637–1659.10.1007/s10098-014-0739-2Search in Google Scholar
26. Alnouri SY, Linke P, El-Halwagi MM. A synthesis approach for industrial city water reuse networks considering central and distributed treatment systems. J Clean Prod. 2015;89:231–250.10.1016/j.jclepro.2014.11.005Search in Google Scholar
27. Alnouri SY, Linke P, El-Halwagi MM. Synthesis of industrial park water reuse networks considering treatment systems and merged connectivity options. Comput Chem Eng. 2016;91:289–306.10.1016/j.compchemeng.2016.02.003Search in Google Scholar
28. Lavric V, Iancu P, Plesu V. Genetic algorithm optimization of water consumption and wastewater network topology. J Clean Prod. 2005;13:1395–1405.10.1016/j.jclepro.2005.04.014Search in Google Scholar
29. Prakotpol D, Srinophakun T. GA pinch: genetic algorithm toolbox for water pinch technology. Chem Eng Process. 2004;43:203–217.10.1016/S0255-2701(03)00102-8Search in Google Scholar
30. Shafiei S, Domenech S, Koteles R, Paris J. System closure in pulp and paper mills: network analysis by genetic algorithm. J Clean Prod. 2004;12:131–135.10.1016/S0959-6526(02)00188-9Search in Google Scholar
31. Jezowski J, Poplewski G, Jezowska A. Optimization of water usage in chemical industry. Environ Prot Eng. 2003;29:97–117.Search in Google Scholar
32. Burgara MO, Ortega JM, González MS, El-Halwagi MM. Incorporation of the seasonal variations in the optimal treatment of industrial effluents discharged to watersheds. Ind Eng Chem Res. 2013;52:5145–5160.10.1021/ie303065hSearch in Google Scholar
33. Liao ZW, Wu JT, Jiang BB, Wang JD, Yang YR. Design methodology for flexible multiple plant water networks. Ind Eng Chem Res. 2007;46:4954–4963.10.1021/ie061299iSearch in Google Scholar
34. Bishnu SK, Linke P, Alnouri SY, El-Halwagi MM. Multi-period planning of optimal industrial city direct water reuse networks. Ind Eng Chem Res. 2014;53:8844–8865.10.1021/ie5008932Search in Google Scholar
35. Castro EC, Ortega JM, Gonzalez MS, Gutiérrez AJ, El-Halwagi MM. A global optimal formulation for the water integration in eco-industrial parks considering multiple pollutants. Comput Chem Eng. 2011;35:1558–1574.10.1016/j.compchemeng.2011.03.010Search in Google Scholar
36. Peters SM, Timmerhaus DK, West ER. Plant design and economics for chemical engineers. New York: McGraw-Hill, 2003.Search in Google Scholar
37. Lindo Systems. What’s Best! 9.0 – Excel Add-In for Linear, Nonlinear, and Integer Modeling and Optimization http://www.lindo.com.Search in Google Scholar
Supplemental Material
The online version of this article (DOI: https://doi.org/10.1515/cppm-2016-0049) offers supplementary material, available to authorized users.
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