Mass flow characteristics prediction of refrigerants through electronic expansion valve based on XGBoost

In an effort to enhance the prediction accuracy of the mass flow characteristics of refrigerants through electronic expansion valves (EEVs), this study develops a mass flow model using the XGBoost machine learning algorithm. Utilizing experimental data from open literature for refrigerants R1233zd(E) and R245fa, the model aims to accurately predict the mass flow coefficient in EEVs, a crucial aspect for improving the performance and efficiency of refrigeration systems. The model's performance is evaluated using the coefficient of determination (R2) on the training and test datasets, revealing a minimal performance gap and no overfitting issue. Remarkably, our model's predictions for R1233zd(E) and R245fa datasets align consistently with experimental data, with 96.4 % and 90.6 % of the predicted data deviating from the experimental ones within +/- 5 %, respectively. Further, the root-mean-square error (RMSE) and coefficient of variation of the root mean square error (CV-RMSE) values are quite low, with 2.2 % and 2.33 % for R1233zd(E), and 4.23 % and 4.47 % for R245fa, indicating high prediction accuracy. Compared to the original models, the paper approach with the XGBoost algorithm significantly improves prediction accuracy. This advancement provides a promising direction for developing more efficient and reliable control strategies for refrigeration systems in various applications.