Robert Güttel

Catalyst deactivation critically affects the long-term reactor performance. Thus, the development and parametrization of accurate models to predict the effect of operation conditions is crucial for reactor design. Current models are limited by power-law frameworks or lack the flexibility to be used in reactor models. In this work, the application of artificial neural networks (aNNs) to learn deactivation kinetics is demonstrated and critically evaluated against state-of-the-art power law models. Hence, a rate-based deactivation model is proposed in which an unconstrained aNN learns the intrinsic activity gradient as a function of the catalyst state and operation conditions. The model is trained on activity-time data using an integral strategy based on the neural ordinary differential equation method, enabling direct use of experimentally accessible quantities. Using simulated deactivation experiments, a systematic hyperparameter study is conducted to identify suitable network architectures, and the NODE training strategy is demonstrated. The model accurately predicts temperature-dependent deactivation data and shows robust interpolation to unseen conditions, even with small data sets. Application to long-term CO methanation experiments over a Ni-based catalyst demonstrates excellent agreement with experimental activity profiles, capturing regime changes and complex temperature effects. A comparison to a power-law deactivation model yields similarly good best fits with the aNN showing greater robustness to random initialization. A cross-validation study underlines the strong interpolation capability of the aNN, whereas both power-law and aNN models show limitations for extrapolation, as expected for empirical models. Overall, the aNN-based model provides an accurate, flexible, and robust alternative to the classical power-law deactivation models.

NiSe ₂ nanooctahedral cocatalysts significantly enhance TiO ₂ photocatalytic hydrogen production by promoting charge separation and surpassing photogenerated charge recombination.

Designing an integrated, flexible and cost effective Fischer-Tropsch process for the conversion of CO/CO2 mixtures to hydrocarbons as chemical feedstocks or sustainable aviation fuels is of great interest to the chemical industry. We demonstrate that using Ru as catalyst CO/CO2 mixtures can be flexibly transformed into hydrocarbons suitable for further processing within a single process step. The selectivity of this process to the various hydrocarbons, expressed by the chain growth probability, depends on the used CO/CO2 mixture and the operating conditions. Explicitly modeling this dependence is a challenging task. We propose a Bayesian model based on mechanistic considerations and parameterized using Bayesian inference. Our Bayesian design of experiments reveals a strong temperature dependence as well as an inhibiting effect of CO2 on chain growth. The resulting model possesses high predictive power and allows for consistent uncertainty quantification of the chain growth probability even outside of the parameterized range. This makes it useful for the design and operation of innovative, flexible and cost effective CO/CO2 Fischer-Tropsch processes.

Sorption‑enhanced ammonia synthesis (SEAS) requires solid sorbents with high capacity, fast kinetics, and reliable models at elevated temperature and pressure. Here, zeolites and supported metal halides are compared systematically for high‑temperature NH3 sorption for SEAS process design. Commercial zeolites (3A, 4A, 13X, LiLSX) were screened by quasi‑continuous pulse chemisorption (ambient pressure, 50–350 °C), combining capacity determination with TPD‑based site analysis. The most promising candidate LiLSX, still adsorbing NH3 at more than 300 °C, was then benchmarked against supported MnCl2‑based sorbents in a high‑pressure magnetic suspension balance (200–400 °C, up to 12 bar ammonia partial pressure). Morphology and salt distribution were characterized by SEM/EDX. Equilibria were modeled with Langmuir, Toth, Sips and dual‑site Langmuir (DSL) isotherms. NH3 adsorption kinetics on LiLSX were fitted using driving force, stretched exponential (SE) and double SE (DSE) rate expressions. Only LiLSX showed appreciable NH3 uptake above 200 °C in pulse chemisorption and remains active at 300 °C and 0.1 bar ammonia partial pressure. TPD signal and isotherm modeling confirm adsorption on two site types. At SEAS‑relevant conditions, LiLSX attained on average ~40 % higher NH3 capacity than MnCl2(30 wt.%)/AC. LiLSX sustained a quasi‑stable working capacity over 90 adsorption–desorption cycles at 300–350 °C without detectable structural damage. Among all kinetic models tested, the DSE approach best captures LiLSX NH3 uptake behavior and reflects two characteristic time scales aligned with the dual‑site equilibrium description. The presented methodology and model parameters directly support the design and simulation of SEAS reactors.

Transient experimentation is broadly applied in heterogeneous catalysis as insights into surface dynamics can be retrieved. However, the nature and activity of the involved surface intermediates remain uncertain, impeding clear mechanistic conclusions. Thus, in this work, we extend transient experimentation with microkinetic modeling and spectroscopic techniques. In this study, CO ₂ methanation experiments were performed on a supported catalyst (Ru/TiO ₂ ) using the Periodic Transient Kinetics (PTK) method. Elemental balances based on the transient results showed decoupled hydrogenation pathways and storage behavior for surface carbon and oxygen species. The nature of these species was elucidated by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) studies providing evidence for support‐bound species. Potential lumped mechanisms were formulated discriminating between species adsorbed on the support or active material. On the basis of these mechanisms, microkinetic models were set up, fitted to the experimental data and compared by their deviation to the transient experimental data. By comparing the steady‐state coverage of the simulated surface to the spectroscopic results, plausibility was checked. We show that DRIFTS, PTK, and microkinetic modeling provide complementary data in a way that quantitative mechanistic information can be obtained. This synergy is only possible using dynamic experimentation.

Catalyst deactivation critically affects long-term reactor performance. Thus, the development and parameterization of accurate models to predict the effect of operation conditions is crucial for reactor design. Current models are limited by power-law frameworks or lack in flexibility to be used in reactor models. In this work, a rate-based deactivation model is proposed, in which an artificial neural network learns the intrinsic activity gradient as a function of the catalyst state and operating conditions. The model is trained on activity–time data using an integral strategy based on the neural ordinary differential equation method, enabling direct use of experimentally accessible quantities. Using simulated deactivation experiments, systematic hyper-parameter study is conducted to identify suitable network architectures and the NODE training strategy is demonstrated. The model accurately predicts temperature-dependent deactivation data and shows robust interpolation to unseen conditions, even with small data sets. Application to long-term CO methanation experiments over a Ni-based catalyst demonstrates excellent agreement with experimental activity profiles, capturing regime changes and complex temperature effects. The proposed framework provides an accurate and flexible alternative to classical deactivation models which is ready to use in reactor modeling.

Designing experimental campaigns to make efficient use of available time and resources is a critical task in any researchers day. For complex chemical engineering problems, model-based design of experiments (MBDOE) is a reliable tool to sequentially conduct optimal experiments. However, since this paradigm is built upon frequentist methods, uncertainty of data is oftentimes not considered in it despite being critical. On the other hand, Bayesian statistics methods allow for uncertainty modeling throughout the experimental design workflow. Here, a Bayesian design of experiments (bDOE) workflow is introduced leveraging Bayesian optimization (BO) in conjunction with three information-theory based acquisition functions to design optimal sequential experimental plans. It is demonstrated that the experimental budget can be reduced by approximately 60 % when parameterizing kinetic models using this methodology. Model discrimination can be achieved within very few (approximately four) specifically designed experiments. Overall, the proposed bDOE workflow lends itself to problems where the model structure is known but experiments are time-consuming, expensive or limited in number.

For return missions from Mars, the supply of rocket fuel is crucial. As transportation of propellant from Earth and storage on Mars are costly and energy intensive, in situ resource utilization (ISRU) for methane production on Mars presents a promising solution. The major challenge is to identify a robust production process, which provides the required propellant purity. In this study, we compare two process concepts, with and without recycle, under realistic operating conditions using thermodynamic modeling and simulation. The H ₂ /CO ₂ feed ratio is crucial to achieve sufficiently high methane selectivity and thereby reduce the effort in product gas cleaning and the overall process complexity. At the same time, recycling unreacted reactants reduces resource consumption, which is preferable for ISRU scenarios. Hence, our study provides a basis for more detailed process design using kinetic information on the involved process steps.