Multiscale Eulerian CFD of Chemical Processes: A Review

This review covers the scope of multiscale computational fluid dynamics (CFD), laying the framework for studying hydrodynamics with and without chemical reactions in single and multiple phases regarded as continuum fluids. The molecular, coarse-grained particle, and meso-scale dynamics at the individual scale are excluded in this review. Scoping single-scale Eulerian CFD approaches, the necessity of multiscale CFD is highlighted. First, the Eulerian CFD theory, including the governing and turbulence equations, is described for single and multiple phases. The Reynolds-averaged Navier–Stokes (RANS)-based turbulence model such as the standard k-ε equation is briefly presented, which is commonly used for industrial flow conditions. Following the general CFD theories based on the first-principle laws, a multiscale CFD strategy interacting between micro- and macroscale domains is introduced. Next, the applications of single-scale CFD are presented for chemical and biological processes such as gas distributors, combustors, gas storage tanks, bioreactors, fuel cells, random- and structured-packing columns, gas-liquid bubble columns, and gas-solid and gas-liquid-solid fluidized beds. Several multiscale simulations coupled with Eulerian CFD are reported, focusing on the coupling strategy between two scales. Finally, challenges to multiscale CFD simulations are discussed. The need for experimental validation of CFD results is also presented to lay the groundwork for digital twins supported by CFD. This review culminates in conclusions and perspectives of multiscale CFD.

A Practical Guide to Surface Kinetic Monte Carlo Simulations

This review article is intended as a practical guide for newcomers to the field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC simulations as prevalently used for surface and interface applications. We will provide worked out examples using the kmos code, where we highlight the central approximations made in implementing a KMC model as well as possible pitfalls. This includes the mapping of the problem onto a lattice and the derivation of rate constant expressions for various elementary processes. Example KMC models will be presented within the application areas surface diffusion, crystal growth and heterogeneous catalysis, covering both transient and steady-state kinetics as well as the preparation of various initial states of the system. We highlight the sensitivity of KMC models to the elementary processes included, as well as to possible errors in the rate constants. For catalysis models in particular, a recurrent challenge is the occurrence of processes at very different timescales, e.g., fast diffusion processes and slow chemical reactions. We demonstrate how to overcome this timescale disparity problem using recently developed acceleration algorithms. Finally, we will discuss how to account for lateral interactions between the species adsorbed to the lattice, which can play an important role in all application areas covered here.

Molecular Modelling for Reactor Design

Chemical reactor modelling based on insights and data on a molecular level has become reality over the last few years. Multiscale models describing elementary reaction steps and full microkinetic schemes, pore structures, multicomponent adsorption and diffusion inside pores, and entire reactors have been presented. Quantum mechanical (QM) approaches, molecular simulations (Monte Carlo and molecular dynamics), and continuum equations have been employed for this purpose. Some recent developments in these approaches are presented, in particular time-dependent QM methods, calculation of van der Waals forces, new approaches for force field generation, automatic setup of reaction schemes, and pore modelling. Multiscale simulations are discussed. Applications of these approaches to heterogeneous catalysis are demonstrated for examples that have found growing interest over the last few years, such as metal-support interactions, influence of pore geometry on reactions, noncovalent bonding, reaction dynamics, dynamic changes in catalyst nanoparticle structure, electrocatalysis, solvent effects in catalysis, and multiscale modelling.

Contrasting the Role of Ni/Al2O3 Interfaces in Water-Gas Shift and Dry Reforming of Methane

Transition metal nanoparticles (NPs) are typically supported on oxides to ensure their stability, which may result in modification of the original NP catalyst reactivity. In a number of cases, this is related to the formation of NP/support interface sites that play a role in catalysis. The metal/support interface effect verified experimentally is commonly ascribed to stronger reactants adsorption or their facile activation on such sites compared to bare NPs, as indicated by DFT-derived potential energy surfaces (PESs). However, the relevance of specific reaction elementary steps to the overall reaction rate depends on the preferred reaction pathways at reaction conditions, which usually cannot be inferred based solely on PES. Hereby, we use a multiscale (DFT/microkinetic) modeling approach and experiments to investigate the reactivity of the Ni/Al₂O₃ interface toward water-gas shift (WGS) and dry reforming of methane (DRM), two key industrial reactions with common elementary steps and intermediates, but held at significantly different temperatures: 300 vs 650 °C, respectively. Our model shows that despite the more energetically favorable reaction pathways provided by the Ni/Al₂O₃ interface, such sites may or may not impact the overall reaction rate depending on reaction conditions: the metal/support interface provides the active site for WGS reaction, acting as a reservoir for oxygenated species, while all Ni surface atoms are active for DRM. This is in contrast to what PESs alone indicate. The different active site requirement for WGS and DRM is confirmed by the experimental evaluation of the activity of a series of Al₂O₃-supported Ni NP catalysts with different NP sizes (2-16 nm) toward both reactions.

Conjugation-Driven "Reverse Mars-van Krevelen"-Type Radical Mechanism for Low-Temperature C-O Bond Activation

C-O bond activation on monofunctional catalysts (metals, carbides, and oxides) is challenging due to activity constraints imposed by energy scaling relationships. Yet, contrary to predictions, recently discovered multifunctional metal/metal oxide catalysts (e.g., Rh/ReOx, Rh/MoOx, Ir/VOx) demonstrate unusually high C-O scission activity at moderate temperatures. Herein, we use extensive density functional theory calculations, first-principles microkinetic modeling, and electronic structure analysis to elucidate the metal/metal oxide synergy in the Ru/RuO2 catalyst, which enables up to 76% yield of the C-O scission product (2-methyl furan) in catalytic transfer hydrogenolysis of furfural at low temperatures. Our key mechanistic finding is a facile radical-mediated C-O bond activation on RuO2 oxygen vacancies, which directly leads to a weakly bound final product. This is the first time the radical reduction mechanism is reported in heterogeneous catalysis at temperatures <200 °C. We attribute the unique catalytic properties to the formation of a conjugation-stabilized furfuryl radical upon C-O bond scission, the strong hydroxyl affinity of oxygen vacancies due to the metallic character of RuO2, and the acid-base heterogeneity of the oxide surface. The conjugation-driven radical-assisted C-O bond scission applies to any catalytic surface that preserves the π-electron system of the reactant and leads to C-O selectivity enhancement, with notable examples including Cu, H-covered Pd, self-assembled monolayers on Pd, and oxygen-covered Mo2C. Furthermore, we reveal the cooperativity of active sites in multifunctional catalysts. The mechanism is fully consistent with kinetic studies and isotopic labeling experiments, and the insights gained might prove useful more broadly in overcoming activity constraints induced by energy scaling relationships.

Constructing kinetic models of metabolism at genome-scales: A review

Constraint-based modeling of biological networks (metabolism, transcription and signal transduction), although used successfully in many applications, suffer from specific limitations such as the lack of representation of metabolite concentrations and enzymatic regulation, which are necessary for a complete physiologically relevant model. Kinetic models conversely overcome these shortcomings and enable dynamic analysis of biological systems for enhanced in silico hypothesis generation. Nonetheless, kinetic models also have limitations for modeling at genome-scales chiefly due to: (i) model non-linearity; (ii) computational tractability; (iii) parameter identifiability; (iv) estimability; and (v) uncertainty. In order to support further development of kinetic models as viable alternatives to constraint-based models, this review presents a brief description of the existing obstacles towards building genome-scale kinetic models. Specific kinetic modeling frameworks capable of overcoming these obstacles are covered in this review. The tractability and physiological feasibility of these models are discussed with the objective of using available in vivo experimental observations to define the model parameter space. Among the different methods discussed, Monte Carlo kinetic models of metabolism stand out as potentially tractable methods to model genome scale networks while also addressing in vivo parameter uncertainty.

Theoretical Heterogeneous Catalysis: Scaling Relationships and Computational Catalyst Design

Scaling relationships are theoretical constructs that relate the binding energies of a wide variety of catalytic intermediates across a range of catalyst surfaces. Such relationships are ultimately derived from bond order conservation principles that were first introduced several decades ago. Through the growing power of computational surface science and catalysis, these concepts and their applications have recently begun to have a major impact in studies of catalytic reactivity and heterogeneous catalyst design. In this review, the detailed theory behind scaling relationships is discussed, and the existence of these relationships for catalytic materials ranging from pure metal to oxide surfaces, for numerous classes of molecules, and for a variety of catalytic surface structures is described. The use of the relationships to understand and elucidate reactivity trends across wide classes of catalytic surfaces and, in some cases, to predict optimal catalysts for certain chemical reactions, is explored. Finally, the observation that, in spite of the tremendous power of scaling relationships, their very existence places limits on the maximum rates that may be obtained for the catalyst classes in question is discussed, and promising strategies are explored to overcome these limitations to usher in a new era of theory-driven catalyst design.

Density functional theory simulations of complex catalytic materials in reactive environments: beyond the ideal surface at low coverage

Advanced DFT models of complex catalysts, such as amorphous silica–alumina and supported subnanometric platinum particles, bridge the gap between the ideal surface model and the industrial catalyst.