CaRMeN: a tool for analysing and deriving kinetics in the real world

Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis

Biocatalysis is becoming a data science. High-throughput experimentation generates a rapidly increasing stream of biocatalytic data, which is the raw material for mechanistic and novel data-driven modeling approaches for the predictive design of improved biocatalysts and novel bioprocesses. The holistic and molecular understanding of enzymatic reaction systems will enable us to identify and overcome kinetic bottlenecks and shift the thermodynamics of a reaction. The full characterization and modeling of reaction systems is a community effort; therefore, published methods and results should be findable, accessible, interoperable, and reusable (FAIR), which is achieved by developing standardized data exchange formats, by a complete and reproducible documentation of experimentation, by collaborative platforms for developing sustainable software and for analyzing data, and by repositories for publishing results together with raw data. The FAIRification of biocatalysis is a prerequisite to developing highly automated laboratory infrastructures that improve the reproducibility of scientific results and reduce the time and costs required to develop novel synthesis routes.

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

Non-Idealities in Lab-Scale Kinetic Testing: A Theoretical Study of a Modular Temkin Reactor

The Temkin reactor can be applied for industrial relevant catalyst testing with unmodified catalyst particles. It was assumed in the literature that this reactor behaves as a cascade of continuously stirred tank reactors (CSTR). However, this assumption was based only on outlet gas composition or inert residence time distribution measurements. The present work theoretically investigates the catalytic CO2 methanation as a test case on different catalyst geometries, a sphere, and a ring, inside a single Temkin reaction chamber under isothermal conditions. Axial gas-phase species profiles from detailed computational fluid dynamics (CFD) are compared with a CSTR and 1D plug-flow reactor (PFR) model using a sophisticated microkinetic model. In addition, a 1D chemical reactor network (CRN) model was developed, and model parameters were adjusted based on the CFD simulations. Whereas the ideal reactor models overpredict the axial product concentrations, the CRN model results agree well with the CFD simulations, especially under low to medium flow rates. This study shows that complex flow patterns greatly influence species fields inside the Temkin reactor. Although residence time measurements suggest CSTR-like behavior, the reactive flow cannot be described by either a CSTR or PFR model but with the developed CRN model.

Optimizing Solid Oxide Fuel Cell Performance to Re-evaluate Its Role in the Mobility Sector

A sustainable, interconnected, and smart energy network in which hydrogen plays a major role cannot be dismissed as a utopia anymore. There are vast international and industrial ambitions to reach the envisioned system transformation, and the decarbonization of the mobility sector is a central pillar comprising a huge economic share. Solid oxide fuel cells (SOFCs) are one of the most promising technologies in the brigade of clean energy devices and have potentially wide applicability for transportation, due to their high efficiencies and impurity tolerance. To uncover future pathways to boost the cell's performance, we propose a detailed multiscale modeling methodology to evaluate the direct impact of cell materials and morphologies on commercial-scale system performance. After acquiring intrinsic electrokinetics decoupled from mass and charge transport of different anode and cathode materials via a half-cell model, a full cell model is employed to identify the most promising electrode combination. Subsequently, a scale-up to the system level is performed by coupling a 3-D kW-stack model to the balance of plant components while focusing on morphological optimization of the membrane electrode assembly (MEA). On optimally tailoring the MEA, model results demonstrate that an advanced cell design comprising a Ni fiber-CGO matrix structured anode and a LSCF-infiltrated CGO cathode could reach a stack power density of 1.85 kW L^-1 and a net system efficiency of 52.2% for operation at <700 °C, with manageable stack temperature gradients of <14 K cm^-1. The model-optimized power density is substantially higher than those of commercial stacks and surpasses industrial targets for SOFC-based range extenders. Thus, with further cell and stack development targeting the performance limiting processes elucidated in the paper, commercial SOFCs could, alongside range extenders, also act as prime movers in larger scale transport applications such as trucks, trains, and ships.

Reduction of CO2 Emission from Off-Gases of Steel Industry by Dry Reforming of Methane

In a novel process, CO₂ and CH₄ from the off‐gases of the coke oven and blast furnace are used in homogeneous reforming of those greenhouse gases to valuable syngas, a mixture of H₂ and CO. Synthetic mixtures of the off‐gases from those large apparatuses of steel industry are fed to a high‐temperature, high‐pressure flow reactor at varying temperature, pressure, residence time, and mixing ratio of coke oven gas (COG) to blast furnace gas (BFG). In this study, a maximal reduction of 78.5 % CO₂ and a CH₄ conversion of 95 % could be achieved at 1350 °C, 5.5 bar, and a COG/BFG ratio of 0.6. Significant carbonaceous deposits were formed but did not block the reactor tube in the operational time window allowing cyclic operation of the process. These measurements were based on prior thermodynamic analysis and kinetic predictions using an elementary‐step reaction mechanism.

Combustion in the future: The importance of chemistry

Combustion involves chemical reactions that are often highly exothermic. Combustion systems utilize the energy of chemical compounds released during this reactive process for transportation, to generate electric power, or to provide heat for various applications. Chemistry and combustion are interlinked in several ways. The outcome of a combustion process in terms of its energy and material balance, regarding the delivery of useful work as well as the generation of harmful emissions, depends sensitively on the molecular nature of the respective fuel. The design of efficient, low-emission combustion processes in compliance with air quality and climate goals suggests a closer inspection of the molecular properties and reactions of conventional, bio-derived, and synthetic fuels. Information about flammability, reaction intensity, and potentially hazardous combustion by-products is important also for safety considerations. Moreover, some of the compounds that serve as fuels can assume important roles in chemical energy storage and conversion. Combustion processes can furthermore be used to synthesize materials with attractive properties. A systematic understanding of the combustion behavior thus demands chemical knowledge. Desirable information includes properties of the thermodynamic states before and after the combustion reactions and relevant details about the dynamic processes that occur during the reactive transformations from the fuel and oxidizer to the products under the given boundary conditions. Combustion systems can be described, tailored, and improved by taking chemical knowledge into account. Combining theory, experiment, model development, simulation, and a systematic analysis of uncertainties enables qualitative or even quantitative predictions for many combustion situations of practical relevance. This article can highlight only a few of the numerous investigations on chemical processes for combustion and combustion-related science and applications, with a main focus on gas-phase reaction systems. It attempts to provide a snapshot of recent progress and a guide to exciting opportunities that drive such research beyond fossil combustion.

Microkinetic Modeling of the Oxidation of Methane Over PdO Catalysts—Towards a Better Understanding of the Water Inhibition Effect

Water, which is an intrinsic part of the exhaust gas of combustion engines, strongly inhibits the methane oxidation reaction over palladium oxide-based catalysts under lean conditions and leads to severe catalyst deactivation. In this combined experimental and modeling work, we approach this challenge with kinetic measurements in flow reactors and a microkinetic model, respectively. We propose a mechanism that takes the instantaneous impact of water on the noble metal particles into account. The dual site microkinetic model is based on the mean-field approximation and consists of 39 reversible surface reactions among 23 surface species, 15 related to Pd-sites, and eight associated with the oxide. A variable number of available catalytically active sites is used to describe light-off activity tests as well as spatially resolved concentration profiles. The total oxidation of methane is studied at atmospheric pressure, with space velocities of 160,000 h−1 in the temperature range of 500–800 K for mixtures of methane in the presence of excess oxygen and up to 15% water, which are typical conditions occurring in the exhaust of lean-operated natural gas engines. The new approach presented is also of interest for modeling catalytic reactors showing a dynamic behavior of the catalytically active particles in general.

CaRMeN: An Improved Computer-Aided Method for Developing Catalytic Reaction Mechanisms

The software tool CaRMeN (Catalytic Reaction Mechanism Network) was exemplarily used to analyze several surface reaction mechanisms for the combustion of H2, CO, and CH4 over Rh. This tool provides a way to archive and combine experimental and modeling information as well as computer simulations from a wide variety of sources. The tool facilitates rapid analysis of experiments, chemical models, and computer codes for reactor simulations, helping to support the development of chemical kinetic models and the analysis of experimental data. In a comparative study, experimental data from different reactor configurations (channel, annular, and stagnation flow reactors) were modeled and numerically simulated using four different catalytic reaction mechanisms from the literature. It is shown that the software greatly enhanced productivity.