Fuzzy logic-based prediction and parametric optimizing using particle swarm optimization for performance improvement in pyramid solar still

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 nano...

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Published inWater science and technology Vol. 90; no. 4; pp. 1321 - 1337
Main Authors Senthilkumar, N., Yuvaperiyasamy, M., Deepanraj, B., Sabari, K.
Format Journal Article
LanguageEnglish
Published England IWA Publishing 15.08.2024
Subjects
Algorithms
Alternative energy sources
Desalination
Design of experiments
Efficiency
Experimental design
Fossil fuels
Fuzzy Logic
Heat
Impact strength
Metal Nanoparticles - chemistry
Models, Theoretical
Morphology
Nanocomposites
Nanoparticles
Orthogonal arrays
Paraffin wax
Parameters
Particle swarm optimization
Phase change materials
Process parameters
Productivity
Radiation
Renewable resources
Silver
Silver - chemistry
Solar Energy
Temperature preferences
Thermal energy
Water depth
Water temperature
paraffin wax
silver nanoparticles
TOPSIS
distillate productivity
fuzzy rules
Online AccessGet full text
ISSN0273-1223
1996-9732
1996-9732
DOI10.2166/wst.2024.277

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Abstract 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.
AbstractList 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.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.
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.
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/m , 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 ( ), glass temperature ( ), and basin water temperature ( ). 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
Author Yuvaperiyasamy, M.
Senthilkumar, N.
Sabari, K.
Deepanraj, B.
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Copyright IWA Publishing 2024
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Keywords paraffin wax
silver nanoparticles
TOPSIS
distillate productivity
fuzzy rules
Language English
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Snippet 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...
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StartPage 1321
SubjectTerms Algorithms
Alternative energy sources
Desalination
Design of experiments
Efficiency
Experimental design
Fossil fuels
Fuzzy Logic
Heat
Impact strength
Metal Nanoparticles - chemistry
Models, Theoretical
Morphology
Nanocomposites
Nanoparticles
Orthogonal arrays
Paraffin wax
Parameters
Particle swarm optimization
Phase change materials
Process parameters
Productivity
Radiation
Renewable resources
Silver
Silver - chemistry
Solar Energy
Temperature preferences
Thermal energy
Water depth
Water temperature
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Title Fuzzy logic-based prediction and parametric optimizing using particle swarm optimization for performance improvement in pyramid solar still
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