Methanol is of rising interest as a potential hydrogen storage molecule and chemical building block producible from green hydrogen and captured carbon dioxide. Although the reaction kinetics have been studied for decades and numerous models are available, new recent insights reveal that a so far not quantitatively considered autocatalytic reaction pathway is of large relevance in heterogeneously catalyzed methanol synthesis over Cu/ZnO/Al2O3 catalysts. Inspired by these recent reports, an extended kinetic model was derived and parameterized exploiting the same data base used to parameterize earlier derived models. Thus, we provide the first model for quantifying the kinetics of the heterogeneously catalyzed methanol synthesis from CO/CO2/H2 which includes a methanol-assisted autocatalytic reaction pathway. Various reduced model variants were derived from the suggested model. A comparison with these reduced models and also with recalibrated further literature models reveals that the incorporation of the autocatalytic reaction pathway is beneficial. This finding encourages further assessment and validation considering a broader data base.

Focus of this paper is the experimental demonstration of forced periodic operation applied to the synthesis of methanol. A theoretical analysis using the nonlinear frequency response (NFR) method and rigorous mathematical optimization provide a general guideline for analyzing simultaneously carried modulations of inlet composition and total volumetric flow rate. Influences of the forcing parameters (amplitudes, frequencies, phase differences, and shapes of forcing functions) were studied for well‐mixed reactors (CSTR) and theoretically for tubular reactors. Predictions of promising operating conditions were made exploiting a kinetic model developed and validated for an industrial Cu/ZnO/Al ₂ O ₃ catalyst. Confirming the trends of the theoretical results, in an experimental study using a laboratory CSTR, higher time‐averaged product amounts and improved yields were achieved compared to conventional steady state operation applying the optimized phase‐shift. Hereby, square wave modulations of the inputs outperformed sinusoidal modulations. A severe and unsolved problem concerns the deactivation of the catalyst observed in the course of the three months experimental campaign, which caused performance reductions for both steady state and periodic operation.

The transition of the chemical industry towards the utilization of feedstocks based on renewable energies results in a more dynamic process behavior. Advanced mathematical methods are a key factor to handle this complexity. In this contribution, methanol synthesis from hydrogen, carbon dioxide and carbon monoxide is investigated as promising power-2-X technology. Optimal experimental design is used to recalibrate an existing mechanistic kinetic model. Subsequently, the most uncertain sub-model, namely the reversible catalyst dynamics, is partially replaced by neural networks. Several architectures were evaluated, and optimal experimental design was applied to enhance the performance of a chosen architecture. All experiments were realized in an experimental setup able to acquire time-resolved data. A commercial CuO/ZnO/Al₂O₃ catalyst was used in a well-mixed Berty type reactor. The combination of optimal experimental design with hybrid modeling led to an improved quality of the kinetic model needed for process control and optimization.