Machine Learning in Heat Exchangers: State-of-the-Art Review

Heat exchangers play a key role in energy-efficient and sustainable industrial operations. However, process uncertainties are making realization of efficient and stable heat exchanger operation a challenge. Computational methods have been helpful in designing, operation, and control of heat exchanger. Recently, machine learning (ML) has emerged as a very effective tool in heat exchanger offline design as well as online operation and control. This review focuses on the ML methods reported in the literature on parameter estimation, such as fouling factor and heat transfer coefficient, thermal performance, control strategies, and offline and online optimization of heat exchangers. The more frequently used and effective ML methods are identified, and the gaps in research and the demands of practical implementation are elaborated. This study will provide the state of the art in ML applications in heat exchangers and will help in further studies in realizing the scaled-up digital twin realization for the Industry 4.0 mode of heat exchanger design and operation.