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Spiral Heat Exchanger’s Mathematical Modelling and Optimization using Multipurpose Genetic Algorithm | ||
| Chemical Process Design | ||
| مقاله 3، دوره 3، شماره 2، اسفند 2024، صفحه 34-51 اصل مقاله (1.47 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22111/cpd.2025.49578.1038 | ||
| نویسندگان | ||
| Mehdi Jafari1؛ Jamshid Khorshidi* 2؛ Ali Sheibani1؛ Taleb Zarei2 | ||
| 1Department of Chemical Engineering,Yazd Branch, Islamic Azad University, Yazd, Iran | ||
| 2Department of Mechanical Engineering, University of Hormozgan, Bandar Abbas, Iran | ||
| چکیده | ||
| This study presents a novel approach to optimize the thermal design of spiral plate heat exchangers (SPHEs) through a multi-objective genetic algorithm (GA) using MATLAB programming. SPHEs, known for their low fouling and ease of descaling, often outperform conventional heat exchangers for fouling-causing processes. The scientific innovation of this work includes using GA to simultaneously optimize the construction cost and pressure drop while increasing the thermal efficiency by fine-tuning key geometric parameters, including channel spacing and width, hydraulic diameter, and outer diameter. For this purpose, a robust numerical model relying on energy balance equations was introduced to accurately predict the temperature distribution and calculate the total convective heat transfer coefficient (HTC) and logarithmic mean temperature difference (LMTD). The GA-based optimization resulted in a 60% increase in HTC and a 50% reduction in total costs compared to the baseline design. The results confirm the effectiveness of the multi-objective GA method as a powerful optimization tool, indicating strong agreement between the optimized calculations and the SPHE design formulas. This dual improvement in thermal efficiency and cost reduction highlights the practical and economic importance of the proposed method for industrial-scale heat exchanger applications. | ||
| کلیدواژهها | ||
| Spiral heat exchanger؛ Fouling؛ Temperature distribution؛ Optimization؛ Genetic algorithm | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 243 تعداد دریافت فایل اصل مقاله: 78 |
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