Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)

A generic water system consists of a series of works that allow the collection, conveyance, storage and finally the distribution of water in quantities and qualities such as to satisfy the needs of end users. In places characterized by high altitude differences between the intake works and inhabited...

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Autores principales:	Mariacrocetta Sambito, Stefania Piazza, Gabriele Freni
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	pump as turbine energy recovery water distribution system Bayesian Monte Carlo method Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/1871ac19556c4b5888105221ce6abbc5
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spelling	oai:doaj.org-article:1871ac19556c4b5888105221ce6abbc52021-11-11T19:51:22ZStochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)10.3390/su1321123182071-1050https://doaj.org/article/1871ac19556c4b5888105221ce6abbc52021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/12318https://doaj.org/toc/2071-1050A generic water system consists of a series of works that allow the collection, conveyance, storage and finally the distribution of water in quantities and qualities such as to satisfy the needs of end users. In places characterized by high altitude differences between the intake works and inhabited centres, the potential energy of the water is very high. This energy is attributable to high pressures, which could compromise the functionality of the pipelines; it is therefore necessary to dissipate part of this energy. A common alternative to dissipation is the possibility of exploiting this energy by inserting a hydraulic turbine. The present study aims to evaluate the results obtained from a stochastic approach for the solution of the multi-objective optimization problem of PATs (Pumps As Turbines) in water systems. To this end, the Bayesian Monte Carlo optimisation method was chosen for the optimization of three objective functions relating to pressure, energy produced and plant costs. The case study chosen is the Net 3 literature network available in the EPANET software manual. The same problem was addressed using the NSGA-III (Nondominated Sorting Genetic Algorithm) to allow comparison of the results, since the latter is more commonly used. The two methods have different peculiarities and therefore perform better in different contexts.Mariacrocetta SambitoStefania PiazzaGabriele FreniMDPI AGarticlepump as turbineenergy recoverywater distribution systemBayesian Monte Carlo methodEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12318, p 12318 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pump as turbine energy recovery water distribution system Bayesian Monte Carlo method Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	pump as turbine energy recovery water distribution system Bayesian Monte Carlo method Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Mariacrocetta Sambito Stefania Piazza Gabriele Freni Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)
description	A generic water system consists of a series of works that allow the collection, conveyance, storage and finally the distribution of water in quantities and qualities such as to satisfy the needs of end users. In places characterized by high altitude differences between the intake works and inhabited centres, the potential energy of the water is very high. This energy is attributable to high pressures, which could compromise the functionality of the pipelines; it is therefore necessary to dissipate part of this energy. A common alternative to dissipation is the possibility of exploiting this energy by inserting a hydraulic turbine. The present study aims to evaluate the results obtained from a stochastic approach for the solution of the multi-objective optimization problem of PATs (Pumps As Turbines) in water systems. To this end, the Bayesian Monte Carlo optimisation method was chosen for the optimization of three objective functions relating to pressure, energy produced and plant costs. The case study chosen is the Net 3 literature network available in the EPANET software manual. The same problem was addressed using the NSGA-III (Nondominated Sorting Genetic Algorithm) to allow comparison of the results, since the latter is more commonly used. The two methods have different peculiarities and therefore perform better in different contexts.
format	article
author	Mariacrocetta Sambito Stefania Piazza Gabriele Freni
author_facet	Mariacrocetta Sambito Stefania Piazza Gabriele Freni
author_sort	Mariacrocetta Sambito
title	Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)
title_short	Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)
title_full	Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)
title_fullStr	Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)
title_full_unstemmed	Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)
title_sort	stochastic approach for optimal positioning of pumps as turbines (pats)
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1871ac19556c4b5888105221ce6abbc5
work_keys_str_mv	AT mariacrocettasambito stochasticapproachforoptimalpositioningofpumpsasturbinespats AT stefaniapiazza stochasticapproachforoptimalpositioningofpumpsasturbinespats AT gabrielefreni stochasticapproachforoptimalpositioningofpumpsasturbinespats
_version_	1718431394677391360

Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)

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