Efficient cooling of high temperature synthesis gas produced in underground coal gasification (UCG) systems is essential for improving the efficiency and reliability of downstream energy conversion processes. Previous studies have demonstrated that membrane helical coil heat exchangers provide superior heat transfer performance compared with conventional heat exchanger geometries due to curvature-induced secondary flow structures. However, identifying the optimal geometric and operating parameters for such complex systems remains a challenging task.

The present study introduces an artificial intelligence assisted optimization framework for the design of membrane helical coil heat exchangers used in high pressure syngas cooling applications. A large dataset of thermo hydraulic performance parameters was generated using computational fluid dynamics (CFD) simulations performed in ANSYS Fluent under varying geometric and operating conditions.

Machine learning models including Artificial Neural Networks (ANN) and Random Forest Regression were employed to predict key performance indicators such as Nusselt number, friction factor, and thermal performance factor. The trained models were integrated with a Genetic Algorithm (GA) to perform multi-objective optimization aimed at maximizing heat transfer while minimizing pressure drop and entropy generation.

The results show that the AI-based optimization framework significantly reduces computational time compared with conventional trial and error optimization methods. The optimized heat exchanger configuration achieved approximately 32% improvement in heat transfer performance with only a moderate increase in pressure drop. The proposed methodology demonstrates strong potential for the intelligent design of advanced heat exchanger systems used in underground coal gasification and other high-temperature energy conversion technologies.