FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W927760932> ?p ?o ?g. }

• W927760932 abstract "Water supply is energy intensive, resulting in large amounts of greenhouse gas emissions and increased electricity bills for water service providers (WSPs). In recent years, the incorporation of microhydropower (MHP) turbines within water supply networks (WSN) has been shown to be a viable option for pressure reduction and improved water supply sustainability. An option for optimal pressure management of a WSN is presented through the installation of MHP turbines. The optimization objective was to find optimal locations in a WSN to install turbines for maximized power generation. For comparison, a nonlinear programming approach, a mixed integer nonlinear programming (MINLP) approach, and an evolutionary optimization approach, using a genetic algorithm, were employed. The performance and suitability of each method was initially demonstrated on a theoretical five-node WSN. MINLP was found to be the most suitable technique. Further analyses were undertaken of a benchmark 25-node network. It was recommended that WSPs adopt this hydropower optimization approach in the decision-making process to reduce carbon footprint, increase revenue, and reduce the operational costs of water supply." @default.
• W927760932 created "2016-06-24" @default.
• W927760932 creator A5051178469 @default.
• W927760932 creator A5067400541 @default.
• W927760932 creator A5069797650 @default.
• W927760932 date "2016-02-01" @default.
• W927760932 modified "2023-10-17" @default.
• W927760932 title "Optimization of Water Distribution Networks for Combined Hydropower Energy Recovery and Leakage Reduction" @default.
• W927760932 cites W123149691 @default.
• W927760932 cites W1540930727 @default.
• W927760932 cites W1607114048 @default.
• W927760932 cites W1964916805 @default.
• W927760932 cites W1968456405 @default.
• W927760932 cites W1985312356 @default.
• W927760932 cites W1991914668 @default.
• W927760932 cites W1996526174 @default.
• W927760932 cites W1997621454 @default.
• W927760932 cites W2002523493 @default.
• W927760932 cites W2013082086 @default.
• W927760932 cites W2019806045 @default.
• W927760932 cites W2022439994 @default.
• W927760932 cites W2022809424 @default.
• W927760932 cites W2034793658 @default.
• W927760932 cites W2037801859 @default.
• W927760932 cites W2042596066 @default.
• W927760932 cites W2044373206 @default.
• W927760932 cites W2059268369 @default.
• W927760932 cites W2062147728 @default.
• W927760932 cites W2062612550 @default.
• W927760932 cites W2068052225 @default.
• W927760932 cites W2068129723 @default.
• W927760932 cites W2073725088 @default.
• W927760932 cites W2087092701 @default.
• W927760932 cites W2093629330 @default.
• W927760932 cites W2105134480 @default.
• W927760932 cites W2108225021 @default.
• W927760932 cites W2131550158 @default.
• W927760932 cites W2133271501 @default.
• W927760932 cites W2149683146 @default.
• W927760932 cites W2170213261 @default.
• W927760932 cites W2325896416 @default.
• W927760932 cites W2736062071 @default.
• W927760932 doi "https://doi.org/10.1061/(asce)wr.1943-5452.0000566" @default.
• W927760932 hasPublicationYear "2016" @default.
• W927760932 type Work @default.
• W927760932 sameAs 927760932 @default.
• W927760932 citedByCount "49" @default.
• W927760932 countsByYear W9277609322015 @default.
• W927760932 countsByYear W9277609322016 @default.
• W927760932 countsByYear W9277609322017 @default.
• W927760932 countsByYear W9277609322018 @default.
• W927760932 countsByYear W9277609322019 @default.
• W927760932 countsByYear W9277609322020 @default.
• W927760932 countsByYear W9277609322021 @default.
• W927760932 countsByYear W9277609322022 @default.
• W927760932 countsByYear W9277609322023 @default.
• W927760932 crossrefType "journal-article" @default.
• W927760932 hasAuthorship W927760932A5051178469 @default.
• W927760932 hasAuthorship W927760932A5067400541 @default.
• W927760932 hasAuthorship W927760932A5069797650 @default.
• W927760932 hasConcept C115527620 @default.
• W927760932 hasConcept C119599485 @default.
• W927760932 hasConcept C119857082 @default.
• W927760932 hasConcept C121332964 @default.
• W927760932 hasConcept C126255220 @default.
• W927760932 hasConcept C127413603 @default.
• W927760932 hasConcept C13280743 @default.
• W927760932 hasConcept C158622935 @default.
• W927760932 hasConcept C185798385 @default.
• W927760932 hasConcept C18903297 @default.
• W927760932 hasConcept C205649164 @default.
• W927760932 hasConcept C21880701 @default.
• W927760932 hasConcept C33923547 @default.
• W927760932 hasConcept C39432304 @default.
• W927760932 hasConcept C40675005 @default.
• W927760932 hasConcept C41008148 @default.
• W927760932 hasConcept C47737302 @default.
• W927760932 hasConcept C62520636 @default.
• W927760932 hasConcept C86803240 @default.
• W927760932 hasConcept C87717796 @default.
• W927760932 hasConcept C8880873 @default.
• W927760932 hasConcept C97053079 @default.
• W927760932 hasConceptScore W927760932C115527620 @default.
• W927760932 hasConceptScore W927760932C119599485 @default.
• W927760932 hasConceptScore W927760932C119857082 @default.
• W927760932 hasConceptScore W927760932C121332964 @default.
• W927760932 hasConceptScore W927760932C126255220 @default.
• W927760932 hasConceptScore W927760932C127413603 @default.
• W927760932 hasConceptScore W927760932C13280743 @default.
• W927760932 hasConceptScore W927760932C158622935 @default.
• W927760932 hasConceptScore W927760932C185798385 @default.
• W927760932 hasConceptScore W927760932C18903297 @default.
• W927760932 hasConceptScore W927760932C205649164 @default.
• W927760932 hasConceptScore W927760932C21880701 @default.
• W927760932 hasConceptScore W927760932C33923547 @default.
• W927760932 hasConceptScore W927760932C39432304 @default.
• W927760932 hasConceptScore W927760932C40675005 @default.
• W927760932 hasConceptScore W927760932C41008148 @default.
• W927760932 hasConceptScore W927760932C47737302 @default.
• W927760932 hasConceptScore W927760932C62520636 @default.

• next