The screening of high-quality oxygen carriers is a key focus in the field of chemical looping combustion. However, the existing screening methods have the problems of high cost and long material design cycles. Here, a machine learning model has been established and successfully predicted the effect of composition, porosity, specific surface area and other physicochemical properties on the redox performance. A database consisting of 190 samples was used to train the BP-ANN algorithm and the SVM algorithm. The SVM algorithm triumphs over the BP-ANN algorithm in that the best model by the SVM algorithm makes predictions with a high coefficient of determination (R² = 0.961) and a low root means square error (RMSE = 0.014). According to the obtained model, the copper ore was estimated to exhibit high reaction performance in terms of 68% CH₄ conversion and 96% CO conversion at 950 ^oC. We anticipate the machine learning method can be extended to predict the performance of oxygen carriers for other chemical looping applications.
 

Machine learning, BP-ANN and SVM Algorithm, Oxygen carrier screening, Chemical looping combustion