|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM377028630 |
| 003 |
DE-627 |
| 005 |
20240902235142.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
240902s2024 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.2166/wst.2024.277
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1520.xml
|
| 035 |
|
|
|a (DE-627)NLM377028630
|
| 035 |
|
|
|a (NLM)39215741
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Senthilkumar, N
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Fuzzy logic-based prediction and parametric optimizing using particle swarm optimization for performance improvement in pyramid solar still
|
| 264 |
|
1 |
|c 2024
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 31.08.2024
|
| 500 |
|
|
|a Date Revised 31.08.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a © 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/).
|
| 520 |
|
|
|a 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
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a TOPSIS
|
| 650 |
|
4 |
|a distillate productivity
|
| 650 |
|
4 |
|a fuzzy rules
|
| 650 |
|
4 |
|a paraffin wax
|
| 650 |
|
4 |
|a silver nanoparticles
|
| 650 |
|
7 |
|a Silver
|2 NLM
|
| 650 |
|
7 |
|a 3M4G523W1G
|2 NLM
|
| 700 |
1 |
|
|a Yuvaperiyasamy, M
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Deepanraj, B
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Sabari, K
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t Water science and technology : a journal of the International Association on Water Pollution Research
|d 1986
|g 90(2024), 4 vom: 31. Aug., Seite 1321-1337
|w (DE-627)NLM098149431
|x 0273-1223
|7 nnns
|
| 773 |
1 |
8 |
|g volume:90
|g year:2024
|g number:4
|g day:31
|g month:08
|g pages:1321-1337
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.2166/wst.2024.277
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 90
|j 2024
|e 4
|b 31
|c 08
|h 1321-1337
|