spiral heat exchanger’s mathematical modelling and optimization using multipurpose genetic algorithm

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 ofdescaling, 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 methodas 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 economicimportance of the proposed method for industrial-scale heat exchanger applications.