Development of automated software for analysis and simulation of the reverse water-gas-shift reaction

The ongoing climate crisis and the increasing concentration of CO2 in the atmosphere have become a main challenge of our time. As a consequence, the development of sustainable energy technologies, such as the Power-to-Liquid (PtL) processes, has attracted significant scientific and industrial interest. The reverse water gas shift reaction (rWGS) forms a critical role in transforming CO2 into valuable intermediates or products. This master’s thesis deals with the development and implementation of an integrated and automated workflow for the evaluation of gas composition and reaction performance in rWGS experiments. It establishes a reproducible data connection between experimental data acquisition, calibration procedures and a simulation tool. A Python based data calibration method, utilizing the generalized least squares regression is applied on the raw data of the experimental results with a corresponding error propagation. In addition, this study presents a simulation tool for the rWGS and other similar reactions. The tool serves as a preliminary investigation of product composition for the rWGS reaction and helps in identifying potential byproducts. The findings of the study highlight the effectiveness of process automation in improving data management, which not only supports the research on CO2 utilization and sustainable fuel production in the PtL technology but also facilitates the efficient calculation and comparison of catalyst efficiencies, including conversion, yield and selectivity.