Machine Learning for Chemical Looping: Recent Advances and Prospects

Chemical looping is a revolutionary energy conversion method aimed at the low-carbon transformation of fossil fuels. The development of this technology primarily involves the screening of oxygen carriers, the design of reactors, and the optimization of process flows, typically requiring extensive experimental trials and time consumption. Machine learning, with its high-precision predictive capabilities, can optimize the development of chemical looping technology. This review comprehensively summarizes the methods and recent advances in the application of machine learning in chemical looping technology. This review outlined the typical machine learning process involving database construction, model analysis, and application of interpretable algorithms. Then, recent advances in oxygen carrier screening, reactor design, and process flow optimization through machine learning are explored. To address the challenges found in these research developments, potential solutions and future application perspectives are proposed. We hope that this review can offer inspiration for researchers in this field and promote the advancement of chemical looping technology.