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

• Published in: Water science and technology Vol. 90; no. 4; pp. 1321 - 1337
• Main Authors: Senthilkumar, N., Yuvaperiyasamy, M., Deepanraj, B., Sabari, K.
• Format: Journal Article
• Language: English
• 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
• ISSN: 0273-1223; 1996-9732; 1996-9732
• DOI: 10.2166/wst.2024.277

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.