We aim to achieve resource recycling by capturing and using CO 2 generated in a chemical production and disposal process. We focused on CO 2 conversion to CO by the reverse water gas shift–chemical looping (RWGS-CL) reaction. This reaction proceeds in two steps (H 2 + MO x ⇆ H 2 O + MO x –1 ; CO 2 + MO x –1 ⇆ CO + MO x ) via a metal oxide that acts as an oxygen carrier. High CO 2 conversion can be achieved owing to a low H 2 O concentration in the second step, which causes an unwanted back reaction (H 2 + CO 2 ⇆ CO + H 2 O). However, the RWGS-CL process is difficult to control because of repeated thermochemical redox cycling, and the CO 2 and H 2 conversion extents vary depending on the metal oxide composition and experimental conditions. In this study, we developed metal oxides and simultaneously optimized experimental conditions to satisfy target CO 2 and H 2 conversion extents by using machine learning and Bayesian optimization. We used transfer learning to improve the prediction accuracy of the mathematical models by incorporating a data set and knowledge of oxygen vacancy formation energy. Furthermore, we analyzed the RWGS-CL reaction based on the prediction accuracy of each variable and the feature importance of the random forest regression model.

In process design, the values of design variables X for equipment and operating conditions should be appropriately selected for entire processes, including all unit operations, such as reactors and distillation columns, to consider effects between unit operations. However, as the number of X increases, many more simulations are required to search for the optimal X values. Furthermore, multiple objective variables Y, such as yields, make the optimization problem difficult. We propose a process design method based on adaptive design of experiments and Bayesian optimization. Selection of X values that satisfy the target values of multiple Y variables are searched, and simulations for the selected X values are then repeated. Therefore, the X will be selected by a small number of simulations. We verify the effectiveness of this method by simulating an ethylene oxide production plant.

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 CO2 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 CO2 and H2 conversion rates, but Fe and La, which had slightly lower predicted CO2 and H2 conversion rates. These results indicate that a combination of metal oxides with lower predicted CO2 and H2 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.

In process design, the values of design variables X for equipment and operating conditions should be optimized for entire processes, including all unit operations, such as reactors and distillation columns, to consider effects between unit operations. However, as the number of X increases, many more simulations are required to search for the optimal X values. Furthermore, multiple objective variables Y, such as yields, make the optimization problem difficult. We propose a process design method based on adaptive design of experiments and Bayesian optimization. Optimization of X values that satisfy target values of multiple Y variables are searched, and simulations for the optimized X values are then repeated. Therefore, X will be optimized by a small number of simulations. We verify the effectiveness of this method by simulating an ethylene oxide production plant.