Hydrogen energy holds promise for controlling emissions but is limited by the production cost and method. Chemical looping hydrogen production (CLHP) provides a more efficient and environmentally sustainable route to produce highpurity hydrogen compared with conventional methods. Yet, CLHP involves a series of operational variables, and the optimization of operating conditions is the critical issue for large-scale hydrogen production. In this study, support vector machine (SVM), decision tree (DT), random forest (RF), artificial neural network (ANN), and physics-informed neural network (PINN) models are developed to predict hydrogen production rates by analyzing multiple process variables. Through the analysis of the database and experiments, we integrated physical consistency as prior physical knowledge into the PINN for eliminating the data dependence. All models are optimized for optimal performance through hyperparameters. The comparison of five machine learning models reveals that DT and RF models exhibit a characteristic step-like pattern in their predictions, while SVM and ANN models produce outputs that often diverge from the expected trend. The prediction of the PINN model exhibits good performance with R 2 , mean squared error, and mean absolute percentage error scores of 0.882, 1.228, and 18.1%, respectively. The results are with high interpretability due to the physical-informed inherent feature. Then, the CLHP process is studied, and the relationships between hydrogen yield and operating temperature, gas flow rate, and mass fraction of iron oxide are established. This work shows the difference in the prediction curves between the different models. By training various general models and comparing their predictive performance on chemical looping data, we can gain valuable insights to guide subsequent predictions for CLHP. It will be beneficial for the design of oxygen carriers and the optimization of the CLHP process.
