MODELLING AND OPTIMIZING SHELL-AND-TUBE HEAT EXCHANGERS WITH AIR INJECTION

Abstract

This research paper is devoted to optimization of the shell-and-tube heat exchangers (STHEs) with air injection using machine learning tools to estimate thermohydraulic performance. Comparative study of four machine learning models, Random Vector Functional Link (RVFL), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest (RF), to assess their usefulness in predicting key performance indicators of temperature of outlets and pressure drops were analysed. As put forth in the study, data preprocess, feature selection, and hyperparameter tuning are evident in enhancing the precision of such models. The obtained results reveal that RVFL is preferable with respect to predictive capabilities as it produced high correlation coefficients in predicting outlet temperature and pressure drops. The work proves that machine learning can have significant capabilities to optimize the design of heat exchangers, with the advantage of using data to guide the pursuit of energy efficiency and thermal performance of an industrial process.