Optimizing Pyramid Solar Still Performance using Response Surface Methodology and Artificial Neural Networks | ||
| Journal of Environmental Studies | ||
| Volume 36, Issue 1, December 2024, Pages 40-50 PDF (1011.44 K) | ||
| Document Type: High quality original papers | ||
| DOI: 10.21608/jesj.2024.296455.1079 | ||
| Authors | ||
| Godwin Immanuel* 1; Samson Isaac2; Amanpreet Kaur3; Pearlin E4; Shailendra Kumar Bohidar5; Raja Manikandan6 | ||
| 1Sathyabama Institute of Science and Technology | ||
| 2Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu 641114, India. | ||
| 3Department of Computer Science & Engineering, Chitkara Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab 140401, India. | ||
| 4Department of English, Panimalar Engineering College, Chennai 600123, Tamil Nadu, India. | ||
| 5Department of Mechanical Engineering, School of Engineering & I.T.,MATS University , Raipur, | ||
| 6Department of Electronics and Communication Engineering, K.Ramakrishnan College of Technology, Trichy, Tamil Nadu 621112, India. | ||
| Abstract | ||
| The global demand for potable water continues to rise, there is an urge to call for innovative approaches to ensure sustainable water supply. This study investigates the optimization of process parameters in Pyramid Solar Still (PSS) using Response Surface Methodology (RSM) and a Feedforward Artificial Neural Network (ANN). Experimental trials were conducted in Vellore, India, under a 30-day duration to evaluate the performance of PSS. By leveraging RSM and ANN, the research aimed to enhance the thermal efficiency and water yield of PSS. Key parameters solar intensity, inclination angle, and water depth were optimized, resulting in a significant improvement in both thermal efficiency and water yield. Specifically, the thermal efficiency increased by 42%, while the water yield improved by 1.8 litres per square meter. Economic analysis demonstrated a reduction in water production costs, with the cost per litre decreasing by 0.20 INR. This study proves the effectiveness of integrating RSM and ANN in optimizing solar stills, contributing to advancements in water purification technologies. | ||
| Keywords | ||
| Pyramid Solar Still; Response Surface Methodology; Feed forward Artificial Neural Network; thermal efficiency; water yield | ||
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