Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing

Efficient heat exchanger design is paramount in optimizing chemical processing operations, where energy consumption and cost considerations are crucial. Traditional design approaches rely on empirical correlations and iterative simulations, often resulting in suboptimal solutions due to the complex and nonlinear nature of heat transfer phenomena. In this study, the research proposes a novel approach to enhance heat exchanger design using an autoencoder model for predicting both efficiency and cost. The autoencoder model, a type of artificial neural network, is trained on a comprehensive dataset encompassing various operating conditions, geometric configurations, and material properties of heat exchangers. By learning the underlying patterns and relationships within the data, the autoencoder can effectively capture the nonlinear mappings between design parameters and performance metrics. Through extensive validation and testing, the proposed autoencoder model demonstrates superior accuracy in predicting heat exchanger efficiency and cost compared to conventional methods. Furthermore, the model enables rapid exploration of design alternatives and sensitivity analysis, facilitating informed decision-making in the design phase. By leveraging machine learning techniques, this approach offers a promising avenue for advancing heat exchanger design towards higher efficiency and lower cost in chemical processing applications. The framework demonstrates considerable promise in bolstering efficiency and enhancing economic viability, boasting a correlation coefficient of 0.98171 and a normalized root mean square error (NRMSE) of 0.001523.

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