Prediction of Chemical Looping Hydrogen Production Using Physics-Informed Machine Learning

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 high-purity 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 R2, 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.