Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/70038
Campo DC Valoridioma
dc.contributor.authorCabrera Santana, Pedro Jesúsen_US
dc.contributor.authorCarta González, José Antonioen_US
dc.date.accessioned2020-02-05T12:52:03Z-
dc.date.available2020-02-05T12:52:03Z-
dc.date.issued2019en_US
dc.identifier.isbn978-3-030-25445-2en_US
dc.identifier.issn1931-6828en_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/70038-
dc.description.abstractNumerous studies have been undertaken since the start of the 1990s—when various authors began to propose the use of artificial intelligence in the field of water desalination—on the employment of computational intelligence (CI) systems in this technological field. The main goal of the proposals put forward has been to tackle the high degree of complexity involved in the different processes that can be found in the desalination industry. The wide variety of topics suggested as potential candidates for the application of CI in desalination processes include, among others, alarm processing and fault detection, control systems, operational optimization applications, load forecasting and security assessment. Although desalination plants have traditionally been powered by energy supplied by the burning of fossil fuels, there is a growing trend today, for various reasons, to use renewable energy sources to directly power these plants. This has added new challenges to the management of desalination processes as the temporal variability of renewable energy sources makes the decision-making processes more complicated. In turn, this means that a multivariable approach is required to ensure optimal desalination plant operation by maximizing the exploitability of the variable renewable resource. This chapter presents a review of how CI systems have been used to date in the desalination industry. A special mention is given to new developments which use CI systems to help overcome newly emerging challenges related to the increasing usage of renewable energy sources in the powering of desalination processes.en_US
dc.languageengen_US
dc.publisherSpringeren_US
dc.relation.ispartofSpringer Optimization and Its Applicationsen_US
dc.sourceComputational Intelligence and Optimization Methods for Control Engineering / Blondin, M., Pardalos, P., Sanchis Sáez, J. (eds),v. 150, p. 105-131, (2019)en_US
dc.subject3313 Tecnología e ingeniería mecánicasen_US
dc.titleComputational Intelligence in the Desalination Industryen_US
dc.typeinfo:eu-repo/semantics/bookParten_US
dc.typeBook parten_US
dc.identifier.doi10.1007/978-3-030-25446-9_5en_US
dc.identifier.scopus85073228872-
dc.contributor.authorscopusid56331565000-
dc.contributor.authorscopusid7003652043-
dc.description.lastpage131en_US
dc.description.firstpage105en_US
dc.relation.volume150en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Capítulo de libroen_US
dc.identifier.eisbn978-3-030-25446-9-
dc.utils.revisionen_US
dc.identifier.supplement1931-6828-
dc.identifier.supplement1931-6828-
dc.identifier.ulpgcen_US
dc.identifier.ulpgcen_US
dc.identifier.ulpgcen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INGen_US
dc.contributor.buulpgcBU-INGen_US
dc.contributor.buulpgcBU-INGen_US
dc.contributor.buulpgcBU-INGen_US
dc.description.sjr0,19
dc.description.sjrqQ4
dc.description.spiqQ1
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptGIR Group for the Research on Renewable Energy Systems-
crisitem.author.deptDepartamento de Ingeniería Mecánica-
crisitem.author.deptGIR Group for the Research on Renewable Energy Systems-
crisitem.author.deptDepartamento de Ingeniería Mecánica-
crisitem.author.orcid0000-0001-9707-6375-
crisitem.author.orcid0000-0003-1379-0075-
crisitem.author.parentorgDepartamento de Ingeniería Mecánica-
crisitem.author.parentorgDepartamento de Ingeniería Mecánica-
crisitem.author.fullNameCabrera Santana, Pedro Jesús-
crisitem.author.fullNameCarta González, José Antonio-
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