Planar laser-induced fluorescence with laser synchronized flow control is employed as a non-invasive in situ technique to investigate a NOx storage catalyst, especially to grant a deeper insight into the...

Using natural gas and sustainable biogas as feed, high-temperature pyrolysis represents a potential technology for large-scale hydrogen production and simultaneous carbon capture. Further utilization of solid carbon accruing during the process (i. e., in battery industry or for metallurgy) increases the process's economic chances. This study demonstrated the feasibility of gas-phase methane pyrolysis for hydrogen production and carbon capture in an electrically heated high-temperature reactor operated between 1200 and 1600 °C under industrially relevant conditions. While hydrogen addition controlled methane conversion and suppressed the formation of undesired byproducts, an increasing residence time decreased the amount of byproducts and benefited high hydrogen yields. A temperature of 1400 °C ensured almost full methane conversion, moderate byproduct formation, and high hydrogen yield. A reaction flow analysis of the gas-phase kinetics revealed acetylene, ethylene, and benzene as the main intermediate products and precursors of carbon formation.

The Front Cover shows an electrically heated high‐temperature reactor that produces gaseous hydrogen and solid carbon by pyrolysis of methane that originates from natural gas or biogas. Pyrolytic methane decomposition is an industrially feasible process that allows large‐scale hydrogen production and simultaneous carbon capture without any direct carbon dioxide emissions, hereby contributing to a transformation of the chemical industry towards more sustainability. More information can be found in the Research Article by P. Lott et al.

Parameter estimation is a crucial step for successful microkinetic modeling in catalysis. However, the large number of parameters to be optimized in order to match the experimental data is a bottleneck. In this regard, the global optimization algorithm Basin-Hopping is utilized to automate the typically time-extensive and error-prone task of manual fitting of kinetic parameters for a heterogeneous catalytic system. The stochastic approach of the Basin-Hopping algorithm to explore the kinetic parameter solution space coupled with local search methods makes it possible to screen the high-dimensional space for an optimal set of kinetic parameters giving the least residual between the simulated and the experimentally measured catalytic performance data. Our approach also ensures that only thermodynamically consistent solution candidates are explored at each optimization step. We utilize two example case studies in heterogeneous catalysis, namely, methane oxidation over a palladium catalyst and carbon monoxide methanation over a nickel catalyst, with corresponding detailed kinetic models to illustrate the applicability of the algorithm to efficiently fine-tune detailed kinetic models.

Methane pyrolysis is a very attractive and climate-friendly process for hydrogen production and the sequestration of carbon as solid material. The formation of soot particles in methane pyrolysis reactors needs to be understood for technology scale-up calling for appropriate soot growth models. A monodisperse model is coupled with a plug flow reactor model and elementary-step reaction mechanisms to numerically simulate processes in methane pyrolysis reactors, namely, the chemical conversion of methane to hydrogen, formation of C–C coupling products and polycyclic aromatic hydrocarbons, and growth of soot particles. The soot growth model accounts for the effective structure of the aggregates by calculating the coagulation frequency from the free-molecular regime to the continuum regime. It predicts the soot mass, particle number, area, and volume concentration, along with the particle size distribution. For comparison, experiments on methane pyrolysis are carried out at different temperatures and collected soot samples are characterized using Raman spectroscopy, transmission electron microscopy (TEM), and dynamic light scattering (DLS).