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A solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions

There is interest for desalination technologies powered by solar energy as arid areas are typically bestowed with good solar potential. In response to a US DOE call for solar desalination analysis tools, we developed an open-source solar energy desalination analysis tool, sedat, for techno-economica...

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Autores principales: Fthenakis, Vasilis, Yetman, Gregory, Zhang, Zhuoran, Squires, John, Atia, Adam A., Alarcón-Padilla, Diego-César, Palenzuela, Patricia, Vicraman, Vikas, Zaragoza, Guillermo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123168/
https://www.ncbi.nlm.nih.gov/pubmed/35595803
http://dx.doi.org/10.1038/s41597-022-01331-4
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author Fthenakis, Vasilis
Yetman, Gregory
Zhang, Zhuoran
Squires, John
Atia, Adam A.
Alarcón-Padilla, Diego-César
Palenzuela, Patricia
Vicraman, Vikas
Zaragoza, Guillermo
author_facet Fthenakis, Vasilis
Yetman, Gregory
Zhang, Zhuoran
Squires, John
Atia, Adam A.
Alarcón-Padilla, Diego-César
Palenzuela, Patricia
Vicraman, Vikas
Zaragoza, Guillermo
author_sort Fthenakis, Vasilis
collection PubMed
description There is interest for desalination technologies powered by solar energy as arid areas are typically bestowed with good solar potential. In response to a US DOE call for solar desalination analysis tools, we developed an open-source solar energy desalination analysis tool, sedat, for techno-economical evaluation of desalination technologies and selection of regions with the highest potential for using solar energy to power desalination plants. It is expected that this software will simplify the planning, design, and valuation of solar desalination systems in the U.S. and worldwide. Sedat uses Dash for integrating various layers of large volumes of GIS data with Python-based models of solar energy generation and desalination technologies. It derives time-series of energy generation and water production, with details of plant performance and suggestions for improving the solar-desalination coupling. This paper summarizes the various phases of the tool’s development, presents example results showing the potential, under multiple objectives, of solar desalination in parts of the U.S. southwest, and discusses method details that would be useful for future model development.
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spelling pubmed-91231682022-05-22 A solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions Fthenakis, Vasilis Yetman, Gregory Zhang, Zhuoran Squires, John Atia, Adam A. Alarcón-Padilla, Diego-César Palenzuela, Patricia Vicraman, Vikas Zaragoza, Guillermo Sci Data Article There is interest for desalination technologies powered by solar energy as arid areas are typically bestowed with good solar potential. In response to a US DOE call for solar desalination analysis tools, we developed an open-source solar energy desalination analysis tool, sedat, for techno-economical evaluation of desalination technologies and selection of regions with the highest potential for using solar energy to power desalination plants. It is expected that this software will simplify the planning, design, and valuation of solar desalination systems in the U.S. and worldwide. Sedat uses Dash for integrating various layers of large volumes of GIS data with Python-based models of solar energy generation and desalination technologies. It derives time-series of energy generation and water production, with details of plant performance and suggestions for improving the solar-desalination coupling. This paper summarizes the various phases of the tool’s development, presents example results showing the potential, under multiple objectives, of solar desalination in parts of the U.S. southwest, and discusses method details that would be useful for future model development. Nature Publishing Group UK 2022-05-20 /pmc/articles/PMC9123168/ /pubmed/35595803 http://dx.doi.org/10.1038/s41597-022-01331-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Fthenakis, Vasilis
Yetman, Gregory
Zhang, Zhuoran
Squires, John
Atia, Adam A.
Alarcón-Padilla, Diego-César
Palenzuela, Patricia
Vicraman, Vikas
Zaragoza, Guillermo
A solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions
title A solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions
title_full A solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions
title_fullStr A solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions
title_full_unstemmed A solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions
title_short A solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions
title_sort solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123168/
https://www.ncbi.nlm.nih.gov/pubmed/35595803
http://dx.doi.org/10.1038/s41597-022-01331-4
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