Reduction of the search space for the optimization problem of the design of the pumping station through the automatic identification of infeasible flow distributions

[EN] The pumping station design is a critical process in water distribution networks. This set of decisions will have an immediate impact on construction costs and determine energy consumption over the entire lifetime of the system. However, the minimization of investment and operational costs at th...

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Autores: Gutierrez-Bahamondes, Jimmy H., Valdivia-Muñoz, Bastian, Mora-Melia, Daniel, Iglesias Rey, Pedro Luís|||0000-0001-8300-3255
Tipo de recurso: capítulo de libro
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/206108
Acceso en línea:https://riunet.upv.es/handle/10251/206108
Access Level:acceso abierto
Palabra clave:Pumping station design
Setpoint curve
Metaheuristic
Search space
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spelling Reduction of the search space for the optimization problem of the design of the pumping station through the automatic identification of infeasible flow distributionsGutierrez-Bahamondes, Jimmy H.Valdivia-Muñoz, BastianMora-Melia, DanielIglesias Rey, Pedro Luís|||0000-0001-8300-3255Pumping station designSetpoint curveMetaheuristicSearch space[EN] The pumping station design is a critical process in water distribution networks. This set of decisions will have an immediate impact on construction costs and determine energy consumption over the entire lifetime of the system. However, the minimization of investment and operational costs at the same time is a complex problem that has been approached from different perspectives. To achieve this goal, in recent years, it has been shown that it is possible to optimize the selection of pumps, accessories, and control systems while optimizing the flow distribution provided by the pumping stations by using the setpoint curve. However, the huge number of possible combinations and the non-linearity of the equations rule out the use of exact methods to solve the proposed mathematical model. Despite this, some metaheuristic techniques, specifically population-based evolutionary algorithms, have shown good performance against case studies in networks with a high level of simplification. Each objective function evaluation involves at least one hydraulic simulation during the analysis periods. Therefore, the computational effort grows considerably as the size of the network increases, affecting the efficiency of these algorithms and limiting their use to smaller networks. Thus, optimization of the design of pumping stations in real-size networks is a problem that has not yet been fully resolved. To reduce the number of evaluations of the objective function during the optimization process, this work presents a new method for the reduction of the search space based on the automatic identification of infeasible flow ranges as part of the network preprocessing. The method considers the maximum capacity of the available pumps, the minimum pressure required, and the demand patterns of the network. In this way, each pumping station has different restrictions for the decision variables of the mathematical model related to the flow contributions. From this point on, the algorithm does not waste any computational effort evaluating solutions that represent flow distributions previously classified as infeasible. Therefore, it is possible to accelerate the convergence of the algorithms while preserving the quality of the solutions obtained. This new method can be applied to any direct injection network. The amount of solution space reduction will depend on the characteristics of each network. To clarify, this work includes the analysis of one case study and a genetic algorithm was implemented to resolve the model. Finally, the results show a reduction of the solutions space of 80% for the largest network presented.Editorial Universitat Politècnica de ValènciaDepartamento de Ingeniería Hidráulica y Medio AmbienteEscuela Técnica Superior de Ingeniería IndustrialRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-03-06book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/206108reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2061082026-06-13T07:49:27Z
dc.title.none.fl_str_mv Reduction of the search space for the optimization problem of the design of the pumping station through the automatic identification of infeasible flow distributions
title Reduction of the search space for the optimization problem of the design of the pumping station through the automatic identification of infeasible flow distributions
spellingShingle Reduction of the search space for the optimization problem of the design of the pumping station through the automatic identification of infeasible flow distributions
Gutierrez-Bahamondes, Jimmy H.
Pumping station design
Setpoint curve
Metaheuristic
Search space
title_short Reduction of the search space for the optimization problem of the design of the pumping station through the automatic identification of infeasible flow distributions
title_full Reduction of the search space for the optimization problem of the design of the pumping station through the automatic identification of infeasible flow distributions
title_fullStr Reduction of the search space for the optimization problem of the design of the pumping station through the automatic identification of infeasible flow distributions
title_full_unstemmed Reduction of the search space for the optimization problem of the design of the pumping station through the automatic identification of infeasible flow distributions
title_sort Reduction of the search space for the optimization problem of the design of the pumping station through the automatic identification of infeasible flow distributions
dc.creator.none.fl_str_mv Gutierrez-Bahamondes, Jimmy H.
Valdivia-Muñoz, Bastian
Mora-Melia, Daniel
Iglesias Rey, Pedro Luís|||0000-0001-8300-3255
author Gutierrez-Bahamondes, Jimmy H.
author_facet Gutierrez-Bahamondes, Jimmy H.
Valdivia-Muñoz, Bastian
Mora-Melia, Daniel
Iglesias Rey, Pedro Luís|||0000-0001-8300-3255
author_role author
author2 Valdivia-Muñoz, Bastian
Mora-Melia, Daniel
Iglesias Rey, Pedro Luís|||0000-0001-8300-3255
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Hidráulica y Medio Ambiente
Escuela Técnica Superior de Ingeniería Industrial
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Pumping station design
Setpoint curve
Metaheuristic
Search space
topic Pumping station design
Setpoint curve
Metaheuristic
Search space
description [EN] The pumping station design is a critical process in water distribution networks. This set of decisions will have an immediate impact on construction costs and determine energy consumption over the entire lifetime of the system. However, the minimization of investment and operational costs at the same time is a complex problem that has been approached from different perspectives. To achieve this goal, in recent years, it has been shown that it is possible to optimize the selection of pumps, accessories, and control systems while optimizing the flow distribution provided by the pumping stations by using the setpoint curve. However, the huge number of possible combinations and the non-linearity of the equations rule out the use of exact methods to solve the proposed mathematical model. Despite this, some metaheuristic techniques, specifically population-based evolutionary algorithms, have shown good performance against case studies in networks with a high level of simplification. Each objective function evaluation involves at least one hydraulic simulation during the analysis periods. Therefore, the computational effort grows considerably as the size of the network increases, affecting the efficiency of these algorithms and limiting their use to smaller networks. Thus, optimization of the design of pumping stations in real-size networks is a problem that has not yet been fully resolved. To reduce the number of evaluations of the objective function during the optimization process, this work presents a new method for the reduction of the search space based on the automatic identification of infeasible flow ranges as part of the network preprocessing. The method considers the maximum capacity of the available pumps, the minimum pressure required, and the demand patterns of the network. In this way, each pumping station has different restrictions for the decision variables of the mathematical model related to the flow contributions. From this point on, the algorithm does not waste any computational effort evaluating solutions that represent flow distributions previously classified as infeasible. Therefore, it is possible to accelerate the convergence of the algorithms while preserving the quality of the solutions obtained. This new method can be applied to any direct injection network. The amount of solution space reduction will depend on the characteristics of each network. To clarify, this work includes the analysis of one case study and a genetic algorithm was implemented to resolve the model. Finally, the results show a reduction of the solutions space of 80% for the largest network presented.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-03-06
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/206108
url https://riunet.upv.es/handle/10251/206108
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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