Machine learning based predictive model for methanol steam reforming with technical, environmental, and economic perspectives

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To overcome limitations of conventional H2 production approaches such as steam methane reforming (SMR) in a membrane reactor (MR) such as large CO2 emission and deactivation of catalyst and membrane, a promising alternative H2 production system of methanol steam reforming (MSR) in serial reactors and membrane filters is reported here, affording its high product yield and a compact design. In this study, technical, environmental, and economic feasibility according to 12 techno-economic parameters and detailed effects of each parameter for this H2 production system are comprehensively investigated with a machine learning (ML) based predictive model in the following steps: (1) process simulation using Aspen Plus® with detailed thermodynamic phenomena and environmental performance; (2) numerical model using MATLAB® based on technical and environmental performance from the process simulation results; (3) ML-based predictive model having outputs of H2 production rate, CO2 emission, and unit H2 production cost feasibility trained by 12,000 data sets from a numerical model. It is well noted from this study that # of reactors and operating temperature for technical performance, # of reactors and S/C ratio for environmental performance, and operating temperature, # of reactors, reactant, and labor for economic performance are reported as most influential factors.