IoT-enabled stepped basin solar stills: Advanced optimization with PSO and ABC algorithms
This study focuses on optimizing IoT-enabled stepped basin solar stills by integrating the Taguchi method, Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms. The objective was to enhance distillate yield, thermal efficiency, and system performance by optimizing key parame...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
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| Series: | Desalination and Water Treatment |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1944398625000451 |
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| Summary: | This study focuses on optimizing IoT-enabled stepped basin solar stills by integrating the Taguchi method, Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms. The objective was to enhance distillate yield, thermal efficiency, and system performance by optimizing key parameters—water depth, basin material, phase change material (PCM) type, and reflector angle. The Taguchi orthogonal array minimized experimental runs, while PSO and ABC algorithms refined parameter selection. Experimental results showed that a combination of 5 mm water depth, black copper basin, salt hydrate PCM, and a 45° internal reflector angle achieved a distillate yield of 3200 ml/day with 78.05 % efficiency, nearing the theoretical maximum of 4100 ml/day. Real-time IoT monitoring enabled dynamic adjustments, further improving efficiency. The findings highlight the effectiveness of combining smart monitoring and advanced optimization techniques to create scalable and sustainable solar desalination solutions for water-scarce regions. |
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| ISSN: | 1944-3986 |