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Optimal design of exchange water networks with control inputs in Eco-Industrial Parks

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  • Aussel, Didier
  • Cao Van, Kien
  • Salas, David

Abstract

Industrial water conservation is an important adaptation to preserve the environment. Eco-Industrial Parks (EIPs) have been designed to encourage the establishment of water exchange networks between enterprises in order to minimize freshwater consumption and wastewater discharge by maximizing wastewater reuse. In this paper, a mathematical programming model for designing and optimizing industrial water networks in EIPs is studied by formulating and solving Single-Leader–Multi-Follower (SLMF) game problems. Enterprises (followers) aim to minimize their operating costs by reusing wastewater from other enterprises, while the designer (leader) aims to minimize the consumption of natural resources within the ecopark. Moreover, when participating in the ecopark, enterprises can control all their input fluxes and the designer guarantees a minimal relative improvement in comparison with the stand-alone operation of each enterprise. The SLMF game is transformed into a single mixed-integer optimization problem. The obtained results are compared with the results of the blind-input model (Salas et al., 2020).

Suggested Citation

  • Aussel, Didier & Cao Van, Kien & Salas, David, 2023. "Optimal design of exchange water networks with control inputs in Eco-Industrial Parks," Energy Economics, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:eneeco:v:120:y:2023:i:c:s0140988322006090
    DOI: 10.1016/j.eneco.2022.106480
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    References listed on IDEAS

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    4. Didier Aussel & Anton Svensson, 2020. "A Short State of the Art on Multi-Leader-Follower Games," Springer Optimization and Its Applications, in: Stephan Dempe & Alain Zemkoho (ed.), Bilevel Optimization, chapter 0, pages 53-76, Springer.
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    Cited by:

    1. Elisabetta Allevi & Didier Aussel & Rossana Riccardi & Domenico Scopelliti, 2024. "Single-Leader-Radner-Equilibrium: A New Approach for a Class of Bilevel Problems Under Uncertainty," Journal of Optimization Theory and Applications, Springer, vol. 200(1), pages 344-370, January.

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