Predictive modeling and optimization of tubular distiller operation using response surface methodology under silver nanomaterial infused PCM thickness variations
This study introduces a novel mathematical model to predict freshwater production and temperature profiles within a tubular solar still (TSS) under varying conditions. Employing RSM (response surface methodology) with a four-factor, five-level central composite design, we evaluated the performance o...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X24017209 |
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| Summary: | This study introduces a novel mathematical model to predict freshwater production and temperature profiles within a tubular solar still (TSS) under varying conditions. Employing RSM (response surface methodology) with a four-factor, five-level central composite design, we evaluated the performance of an Ag-nanomaterial's-improved phase changing material (PCM)-enhanced TSS. RSM effectively modeled the system, enabling optimization of yield (P) and water (Tw) and glass (Tg) temperatures across different PCM thicknesses. Regression models were developed using RSM to predict performance parameters, leading to the identification of optimal process conditions. PCM thickness was varied from 0 to 4 cm. Optimal conditions included a 1.34 cm PCM thickness, 40 °C ambient temperature, 0.73 m/s air speed, and 720 W/m2 radiation. In this case the expected optimum responses of productivity, 5931.15 mL/m2.d. The RSM models demonstrated high accuracy and consistency with experimental data, validating the approach. These findings highlight the potential of RSM for enhancing solar distillation system performance. |
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| ISSN: | 2214-157X |