A multi-objective optimization model to plan city-scale water systems with economic and environmental objectives: a case study in Santiago, Chile
Daniela Gormaz-Cuevas,Javiera Riffo-Rivas,Ludovic Montastruc,Mariana Brüning-González,Felipe A. Díaz-Alvarado +4 more
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TLDR
In this paper, a multi-objective mixed-integer programming (MOP) problem is formulated to minimize the environmental and economic impact of the network, minimizing water extracted from natural sources and total cost.About:
This article is published in Journal of Cleaner Production.The article was published on 2021-01-10 and is currently open access. It has received 6 citations till now. The article focuses on the topics: Water scarcity & Reuse.read more
Figures
Fig. 14. Representation of the mass balance of a large newwaste water treatment plant with fresh water output quality. C: collection; DL: large existing DWTPs; DS: small existing DWTPs; ILfs: large industrial consumers with fresh water consumption and sewage discharge; ILfn: large industrial consumers with fresh water consumption and natural water course discharge; S: sink.X 
Fig. 21. Pareto curve obtained for the multi-objective problem. 
Fig. 3. Representation of the mass balance of commercial and residential consumption. DS: existing small DWTPs with surface water consumption; NS: new small DWTPs; MAs: modified small WWTPs with drinking water output quality; NAs: new small WWTPs with drinking water output quality; D: distribution; C: collection; WS: small existing WWTPs; NAs: new small WWTPs with drinking water output quality; NBs: new small WWTPs with fresh water output quality; NCs: new small WWTPs with irrigation water output quality; NDs: new small WWTPs with discharge water output quality; MAs: modified small WWTPs with drinking water output quality; MBs: modified small WWTPs with fresh water output quality; MCs: modified small WWTPs with irrigation water output quality; S: sink. 
Fig. 18. Representation of the mass balance of collection node. RC: residential consumption; CC: commercial consumption; EB: industrial effluent treatment plant connected with large industrial consumers; ELds: related to ILds; ELdn: related to ILdn; WL: large existing DWTPs; NAl: new large WWTPs with drinking water output quality; NBl: new large WWTPs with fresh water output quality; NCl: new large WWTPs with irrigation water output quality; NDl: new large WWTPs with discharge water output quality; MAl: modified large WWTPs with drinking water output quality; MBl: modified large WWTPs with fresh water output quality; MCl: modified large WWTPs with irrigation water output quality; S: sink. 
Fig. 4. Representation of the mass balance of local industrial consumption. DS: existing small DWTPs with surface water consumption; NS: new small DWTPs; MAs: modified small WWTPs with drinking water output quality; NAs: new small WWTPs with drinking water output quality; D: distribution; EB: industrial effluent treatment plant connected with by-district industrial consumers; S: sink. 
Fig. 5. Representation of the mass balance of large industrial consumption supplied with drinking water and discharging into surface water courses. DS: small existing DWTPs; D: distribution; NS: new small DWTPs; NAs: new small WWTPs with drinking water output quality;MAs: modified small WWTPs with drinking water output quality; ELdn: related to ILdn; S: sink.X
Citations
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An optimal resource recovery of biogas, water regeneration, and reuse network integrating domestic and industrial sources
TL;DR: In this article, a non-linear programming (NLP) model was developed to optimize water regeneration and reuse network, as well as biogas generation from selected wastewater streams.
Journal ArticleDOI
Hindcasting and Forecasting Total Suspended Sediment Concentrations Using a NARX Neural Network
TL;DR: In this article, a non-linear autoregressive exogenous neural network (NARX) was used for forecasting sediment concentrations at the exit of Francia Creek watershed (Valparaiso, Chile).
Journal ArticleDOI
Applications of Multi-Objective Optimization to Industrial Processes: A Literature Review
TL;DR: Multi-objective optimization has become a powerful tool to aid the decision-making mechanism in the synthesis, design, operation and control of such processes as mentioned in this paper , and the solution to the mathematical models provides the necessary tools to asses the system performance in terms of different metrics and evaluate the trade-offs between the objectives in conflict.
Journal ArticleDOI
Modeling Frictional Head Loss in the Redesign of City-Scale Water Networks through Multiobjective Optimization: Strategies to Deal with Nonlinear Terms
TL;DR: In this paper , the authors compared four strategies to simplify the nonlinear constraints generated by the extended Bernoulli equation with the Darcy-Weisbach friction term in a city-scale water network, and the best results were obtained with a two-stage strategy to address large MINLP multiobjective problems.
Journal Article
The Evaluation of Parameter M in the Big M Method of Linear Programming
TL;DR: This paper determines a constant M0 and proves that the big M method is convergent to an optimal solution of the primal problem when MM0.
References
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Industrial water management by multiobjective optimization: from individual to collective solution through eco-industrial parks
TL;DR: In this article, the problem is formulated as a Mixed-Integer Linear Programming problem (MILP) and solved by the e-constraint method, based on the necessary conditions of optimality defined by Savelski and Bagajewicz.
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A mixed integer optimisation approach for integrated water resources management
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Characteristics and costs of MF and UF plants
TL;DR: In this article, a questionnaire was sent to membrane filtration plants worldwide regarding their capacities, operation, water quality, and costs, and the total plant capacity of all utilities responding was 49 mgd (185,500 m 3 /d).