Fuzzy logic-based prediction and parametric optimizing using particle swarm optimization for performance improvement in pyramid solar still
© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
| Veröffentlicht in: | Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 90(2024), 4 vom: 31. Aug., Seite 1321-1337 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2024
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| Zugriff auf das übergeordnete Werk: | Water science and technology : a journal of the International Association on Water Pollution Research |
| Schlagworte: | Journal Article TOPSIS distillate productivity fuzzy rules paraffin wax silver nanoparticles Silver 3M4G523W1G |
| Zusammenfassung: | © 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). The primary objective of this study is to develop a robust model that employs a fuzzy logic interface (FL) and particle swarm optimization (PSO) to forecast the optimal parameters of a pyramid solar still (PSS). The model considers a range of environmental variables and varying levels of silver nanoparticles (Ag) mixed with paraffin wax, serving as a phase change material (PCM). The study focuses on three key factors: solar intensity ranging from 350 to 950 W/m2, water depth varying between 4 and 8 cm, and silver (Ag) nanoparticle concentration ranging from 0.5 to 1.5% and corresponding output responses are productivity (P), glass temperature (Tg), and basin water temperature (Tw). The experimental design is based on Taguchi's L9 orthogonal array. A technique for ordering preference by similarity to the ideal solution (TOPSIS) is utilized to optimize the process parameters of PSS. Incorporating a fuzzy inference system (FIS) aims to minimize the uncertainty within the system, and the particle swarm optimization algorithm is employed to fine-tune the optimal settings. These methodologies are employed to forecast the optimal conditions required to enhance the productivity of the PSS |
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| Beschreibung: | Date Completed 31.08.2024 Date Revised 31.08.2024 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 0273-1223 |
| DOI: | 10.2166/wst.2024.277 |