Design of robust water exchange networks for eco-industrial symbiosis |
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| Institution: | 1. Department of Chemical & Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore;2. Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2 L 3G1, Canada;1. Department of Management, Southwest Minnesota State University, 1501 State Street, Marshall, MN 56258, United States;2. Gordon & Jill Bourns College of Engineering, California Baptist University, 8432 Magnolia Ave, Riverside, CA 92504, United States;3. College of Business & Innovation, Minnesota State University Moorhead, Moorhead, MN 56563, United States;1. Department of Chemical Engineering, Texas A&M University at Qatar, PO Box 23874, Education City, Doha, Qatar;2. The Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA;3. Qatar Energy and Environment Research Institute (QEERI), PO Box 5825, Doha, Qatar;1. Department of Chemical Engineering, Texas A&M University at Qatar, P.O Box 23874, Education City, Doha, Qatar;2. The Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA |
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| Abstract: | The field of industrial ecology promotes the establishment of resource exchange networks in eco-industrial parks (EIPs) as an approach toward resource conservation. Previous studies have shown that full blown resource integration can be encouraged through the exchange of common utilities such as energy and water. Different approaches such as mathematical programming, pinch analysis and game theory have been used to identify the optimal network designs, which can simultaneously reduce the utilization of freshwater resources and the generation of wastewater streams. Since water exchange in an EIP involves multiple independently operating plants, information exchange between the participants is not completely transparent and multiple future scenarios are expected to happen as the fate and plans of other participants are not completely divulged. These future scenarios may bring about changes in the capacity or characteristic of industrial processes and may also involve the entry of additional companies and the closure of previously operating ones. Such aspects have not been fully addressed in previous studies. A robust optimization model is thus developed in this work to determine the optimal network design which can effectively operate in anticipation of multiple probable scenarios. Case studies are solved to demonstrate the capability of the model. |
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