Integration of Materials and Process Informatics: Metal Oxide and Process Design for CO(2) Reduction

[Image: see text] In materials informatics, a mathematical model constructed between the synthesis conditions of materials and their properties and activities is used to design synthesis conditions in which the properties and activities have the desired values. In process informatics, a mathematical model constructed between the process conditions for devices and industrial plants and product quality and cost is used to design process conditions that can produce the desired products. In this study, we propose a method to simultaneously design the synthesis conditions of materials and the process conditions of products by integrating materials and process informatics in the reverse water-gas shift chemical looping (RWGS-CL) reaction, which produces CO from CO(2) using metal oxides via the RWGS-CL process. Four methods: Gaussian process regression-Bayesian optimization (GPR-BO), Gaussian mixture regression–Bayesian optimization (GMR-BO), GMR-BO-multiple, and GPR-GMR-BO were investigated for the optimization. All four proposed methods outperformed the results of a random search. GPR-BO achieved the highest performance and proposed 27 promising candidates for the synthesis conditions and metal oxides. The selected metals did not include Cu and Ga, which tended to have high predicted CO(2) and H(2) conversion rates, but Fe and La, which had slightly lower predicted CO(2) and H(2) conversion rates. These results indicate that a combination of metal oxides with lower predicted CO(2) and H(2) conversion rates and optimized process conditions was important for the optimization of both materials and processes, which was achieved by integrating materials and process informatics via the proposed method. Thus, we confirmed that it is possible to simultaneously optimize the combination of metals, composition ratios, synthesis conditions of the material or the metal oxide, and the process conditions using experimental datasets, process simulations, and machine learning, such as GPR, GMR, BO, and multiobjective optimization with a genetic algorithm.