Optimal Design of Spatially Constrained Interplant Water Networks with Direct Recycling Techniques using Genetic Algorithms
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2014
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In this work, an industrial city spatial representation that accounts for different plant layouts and arrangements is utilized for interplant water network synthesis. The problem has been previously tackled using deterministic optimization methods. This work employs a stochastic optimization approach, using genetic algorithms, for the design of spatially constrained interplant water networks using direct recycling techniques. The approach identifies well-performing solutions in an evolutionary manner, by generating populations of candidate solutions, then sampling regions that are associated with the highest performance probabilities. This ensures that only the fittest designs survive, when evaluating the network performance. A fitness objective that accounts for both freshwater and piping costs was utilized in the design evaluation stage. When compared to the results that have been obtained using deterministic optimization, trade-off trends between the optimum cost of the network and ...fresh/waste targets were manifested by means of stochastic optimization. Enhanced network performance was attained for a reduced total cost, at the expense of a certain deviation from fresh/waste targets.
Izvor:
Pres 2014, 17th Conference on Process Integration, Modelling and Optimisation for Energy Saving and, 2014, 39, 457-+Izdavač:
- Aidic Servizi Srl, Milano
Finansiranje / projekti:
- Qatar National Research Fund (a member of Qatar Foundation) [NPRP 4-1191-2-468]
DOI: 10.3303/CET1439077
ISSN: 1974-9791
WoS: 000346757600077
Scopus: 2-s2.0-84908112004
Institucija/grupa
Tehnološko-metalurški fakultetTY - JOUR AU - Alnouri, Sabla AU - Stijepović, Mirko AU - Linke, Patrick AU - El-Halwagi, Mahmoud PY - 2014 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/2725 AB - In this work, an industrial city spatial representation that accounts for different plant layouts and arrangements is utilized for interplant water network synthesis. The problem has been previously tackled using deterministic optimization methods. This work employs a stochastic optimization approach, using genetic algorithms, for the design of spatially constrained interplant water networks using direct recycling techniques. The approach identifies well-performing solutions in an evolutionary manner, by generating populations of candidate solutions, then sampling regions that are associated with the highest performance probabilities. This ensures that only the fittest designs survive, when evaluating the network performance. A fitness objective that accounts for both freshwater and piping costs was utilized in the design evaluation stage. When compared to the results that have been obtained using deterministic optimization, trade-off trends between the optimum cost of the network and fresh/waste targets were manifested by means of stochastic optimization. Enhanced network performance was attained for a reduced total cost, at the expense of a certain deviation from fresh/waste targets. PB - Aidic Servizi Srl, Milano T2 - Pres 2014, 17th Conference on Process Integration, Modelling and Optimisation for Energy Saving and T1 - Optimal Design of Spatially Constrained Interplant Water Networks with Direct Recycling Techniques using Genetic Algorithms EP - + SP - 457 VL - 39 DO - 10.3303/CET1439077 ER -
@article{ author = "Alnouri, Sabla and Stijepović, Mirko and Linke, Patrick and El-Halwagi, Mahmoud", year = "2014", abstract = "In this work, an industrial city spatial representation that accounts for different plant layouts and arrangements is utilized for interplant water network synthesis. The problem has been previously tackled using deterministic optimization methods. This work employs a stochastic optimization approach, using genetic algorithms, for the design of spatially constrained interplant water networks using direct recycling techniques. The approach identifies well-performing solutions in an evolutionary manner, by generating populations of candidate solutions, then sampling regions that are associated with the highest performance probabilities. This ensures that only the fittest designs survive, when evaluating the network performance. A fitness objective that accounts for both freshwater and piping costs was utilized in the design evaluation stage. When compared to the results that have been obtained using deterministic optimization, trade-off trends between the optimum cost of the network and fresh/waste targets were manifested by means of stochastic optimization. Enhanced network performance was attained for a reduced total cost, at the expense of a certain deviation from fresh/waste targets.", publisher = "Aidic Servizi Srl, Milano", journal = "Pres 2014, 17th Conference on Process Integration, Modelling and Optimisation for Energy Saving and", title = "Optimal Design of Spatially Constrained Interplant Water Networks with Direct Recycling Techniques using Genetic Algorithms", pages = "+-457", volume = "39", doi = "10.3303/CET1439077" }
Alnouri, S., Stijepović, M., Linke, P.,& El-Halwagi, M.. (2014). Optimal Design of Spatially Constrained Interplant Water Networks with Direct Recycling Techniques using Genetic Algorithms. in Pres 2014, 17th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Aidic Servizi Srl, Milano., 39, 457-+. https://doi.org/10.3303/CET1439077
Alnouri S, Stijepović M, Linke P, El-Halwagi M. Optimal Design of Spatially Constrained Interplant Water Networks with Direct Recycling Techniques using Genetic Algorithms. in Pres 2014, 17th Conference on Process Integration, Modelling and Optimisation for Energy Saving and. 2014;39:457-+. doi:10.3303/CET1439077 .
Alnouri, Sabla, Stijepović, Mirko, Linke, Patrick, El-Halwagi, Mahmoud, "Optimal Design of Spatially Constrained Interplant Water Networks with Direct Recycling Techniques using Genetic Algorithms" in Pres 2014, 17th Conference on Process Integration, Modelling and Optimisation for Energy Saving and, 39 (2014):457-+, https://doi.org/10.3303/CET1439077 . .