Construction of Cooking Heat transfer Model and Prediction of Temperature based on Machine Learning

Abstract

In order to simulate the cooking process heated by gas, considering the characteristics of water and edible oil and combining with the existing heat transfer research, a cooking heat transfer model with mass dissipation of heating medium was constructed in this paper. The numerical simulation of the heat-transfer model under different heating conditions was carried out with MATLAB. The container and the medium temperature simulation results fit well with the experimental data under the same conditions. Based on the physical model, two deep learning models, MDTN (Multiple Time Difference Network) and LSTM(Long Short-Term Memory), were used to learn the variation law of the container temperature during heating. When using the MDTN model to predict the temperature value over a long time horizon, the actual temperature of the container was required to be added to the DataSet intermittently to prevent temperature error from diverging gradually. However, when using the LSTM model for prediction, since the temperature change was relatively stable, this model could predict the temperature of a longer time series using only the initial temperature sequence, and the prediction result was close to the actual temperature. The prediction results of both models conformed to the laws of physics.

Keywords: Cooking Heat Transfer, Numerical Simulation, Machine Learning, LSTM, MDTN, Temperature Prediction

DOI: 10.54941/ahfe1002847