Modelling and optimization of an inverted pyramid solar still using ANFIS-PSO: predictive analysis of water production, energy, and exergy efficiency.

Elsheikh, Ammar H.; Egiza, Mohamed; Diab, Mohamed Ragab; Nassar, Mahmoud; Alhosary, Mohamed; Nassar, Salman; Rozza, Mohamed; Faisal, Nadimul; Essa, Fadl A.

Abstract

This study addresses the pressing challenge of enhancing the predictive modelling of solar still performance, focusing on critical parameters: water yield, energy efficiency, and exergy efficiency. The research utilizes an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized with Particle Swarm Optimization (PSO) to refine the accuracy and reliability of predictions in solar desalination systems. By incorporating a modified solar still with an inverted pyramid aluminium basin and experimenting with different water volumes (5, 10, 20, and 30 litters) and natural materials such as stones, wick, and luffa, this study assesses their impact on heat retention and freshwater yield. The input variables for the ANFIS-PSO model include time, wind speed, ambient temperature, solar radiation, water quantity, and the type of natural materials used, which are crucial for understanding environmental and operational influences on solar still performance. The results show that the ANFIS-PSO model significantly outperforms the standard ANFIS model. During testing, the ANFIS-PSO model achieved R2 values of 0.9899 for water yield, 0.9706 for energy efficiency, and 0.9642 for exergy efficiency, compared to ANFIS R2 values of 0.8108, 0.6894, and 0.7250, respectively. Additionally, the mean error for water yield was reduced by 43 %, energy efficiency by 66 %, and exergy efficiency by 68 % in the ANFIS-PSO model, demonstrating its superior accuracy. These results highlight the potential of integrating PSO with ANFIS to enhance the predictive capability and reliability of solar desalination systems, offering valuable insights for their optimization and design.

Citation

ELSHEIKH, A.H., EGIZA, M., DIAB, M.R., NASSAR, M., ALHOSARY, M., NASSAR, S., ROZZA, M., FAISAL, N. and ESSA, F.A. 2025. Modelling and optimization of an inverted pyramid solar still using ANFIS-PSO: predictive analysis of water production, energy, and exergy efficiency. Separation and purification technology [online], 377(3), article number 134492. Available from: https://doi.org/10.1016/j.seppur.2025.134492

• Journal Article Type: Article
• Acceptance Date: Jul 23, 2025
• Online Publication Date: Jul 25, 2025
• Publication Date: Dec 19, 2025
• Deposit Date: Jul 25, 2025
• Publicly Available Date: Jul 25, 2025
• Journal: Separation and purification technology
• Print ISSN: 1383-5866
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 377
• Issue: 3
• Article Number: 134492
• DOI: https://doi.org/10.1016/j.seppur.2025.134492
• Keywords: Water productivity prediction; Adaptive Neuro-Fuzzy Inference System optimized with Particle Swarm Optimization (ANFIS-PSO) ; Natural materials; Solar desalination; Energy efficiency; Exergy efficiency
• Public URL: https://rgu-repository.worktribe.com/output/2935255
• Additional Information: This article has been published with separate supporting information. This supporting information has been incorporated into a single file on this repository and can be found at the end of the file associated with this output.