Leveraging artificial intelligence for simplified adiabatic compression heating prediction: Comparing the use of artificial neural networks with conventional numerical approach

Abstract

This study presents a comprehensive evaluation of artificial neural networks (ANNs) for predicting adiabatic compression heating in high-pressure processing (HPP) and high-pressure thermal processing (HPTP). The ANNs were thoroughly compared with experimental data, as well as data predicted by a previously developed approach. This previous approach used numerical methods to extract relevant material properties from experimental data, which was then used for compression heating predictions by solving an ordinary differential equation. The new work showcases the efficacy of ANNs in accurately predicting compression heating effects. The results indicate that ANNs offer a robust, flexible, and efficient approach for modelling adiabatic heating, with significant implications for the design and optimisation of HPTP treatments. The study underscores the transformative potential of AI in advancing food preservation technologies.