AI-Based Optimization of Heat Exchangers

Abstract

Abstract - Heat exchangers are critical components in industries such as power generation, chemical processing, refrigeration, automotive engineering, and renewable energy systems. Their performance directly influences energy efficiency, operational costs, and environmental sustainability. Conventional design and optimization techniques often rely on empirical correlations, trial-and-error methods, or computationally intensive simulations, which may be time-consuming and unable to efficiently explore complex design spaces. Recent advancements in Artificial Intelligence (AI) have introduced innovative approaches for optimizing heat exchanger design, operation, and maintenance. AI techniques, including Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Support Vector Machines (SVMs), and Deep Learning (DL), enable rapid prediction of thermal performance, optimization of geometric parameters, and intelligent fault detection while reducing computational effort. This paper presents a comprehensive review of AI-based optimization methods for heat exchangers, highlighting their applications in thermal performance enhancement, energy efficiency improvement, pressure drop minimization, and predictive maintenance. The study also discusses the advantages, limitations, and future prospects of integrating AI with Computational Fluid Dynamics (CFD), Internet of Things (IoT), and digital twin technologies for real-time monitoring and adaptive optimization. The findings indicate that AI-driven optimization can significantly improve heat transfer efficiency, reduce energy consumption, lower operational costs, and support sustainable industrial processes. The paper concludes that AI has emerged as a transformative technology for next-generation intelligent heat exchanger systems, offering substantial potential for research and industrial implementation. Key Words: AI, GA, ANN,DL, SVM.