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 being high cost and having long material design cycles. Here, a machine learning model has been established which 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 (R 2 = 0.961) and a low root mean square error (RMSE = 0.014).According to the obtained model, the copper ore was estimated to exhibit high reaction performance in terms of 68% CH 4 conversion and 96% CO conversion at 950 °C. We anticipate the machine learning method can be extended to predict the performance of oxygen carriers for other chemical looping applications.

A key focus of chemical looping oxidative coupling of methane is the screening of high-quality oxygen carriers. However, existing screening methods suffer from long material design cycles and high costs....

Thermal sintering of oxygen carriers is a major challenge in the chemical looping hydrogen generation (CLHG) process. Operating the CLHG process at moderate temperatures is a feasible approach to address...

Plasma-assisted chemical looping oxidative coupling of methane (CLOCM) is a process that holds promise for the direct conversion of methane to hydrocarbons at low temperatures. However, the tendency of the...