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Sustainability Ranking of Desalination Plants Using Mamdani Fuzzy Logic Inference Systems

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School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, UK, Dubai Campus, Dubai International Academic City, P.O. Box 294345 Dubai, UAE
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Department of Geography, Carleton University, 1125 Colonel by Drive, Ottawa, ON K1S 5B6, Canada
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ILF Consulting EngineersߞAbu Dhabi, Al Reem Island, Sky Tower, Unit 36-01, 36th Floor, P.O. Box 73250 Abu Dhabi, UAE
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Water Engineering Department, Shahid Bahonar University of Kerman, 76169-14111 Kerman, Iran
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Division of Water Resources Engineering (TVRL), Department of Building and Environmental Technology, Faculty of Engineering, Lund University, P.O. Box 118, 22100 Lund, Sweden
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Civil Engineering Research Group, School of Computing, Science and Engineering, The University of Salford, Newton Building, Salford M5 4WT, UK
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Department of Civil Engineering Science, School of Civil Engineering and the Built Environment, University of Johannesburg, Kingsway Campus, P.O. Box 524, Aukland Park 2006, Johannesburg, South Africa
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Sustainability 2020, 12(2), 631; https://doi.org/10.3390/su12020631
Received: 4 December 2019 / Revised: 9 January 2020 / Accepted: 13 January 2020 / Published: 15 January 2020
(This article belongs to the Section Economic, Business and Management Aspects of Sustainability)
As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability. View Full-Text
Keywords: artificial intelligence; decision-making in water supply; energy efficiency; ranking modelling framework; reverse osmosis; sustainability indicator list; sustainability tool; sustainable water production; unsustainable production; water pollution artificial intelligence; decision-making in water supply; energy efficiency; ranking modelling framework; reverse osmosis; sustainability indicator list; sustainability tool; sustainable water production; unsustainable production; water pollution
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Rustum, R.; Kurichiyanil, A.M.J.; Forrest, S.; Sommariva, C.; Adeloye, A.J.; Zounemat-Kermani, M.; Scholz, M. Sustainability Ranking of Desalination Plants Using Mamdani Fuzzy Logic Inference Systems. Sustainability 2020, 12, 631.

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