Multiscale kinetic modeling in catalysis ⇒ from microkinetics to computational fluid dynamics and process simulations

Problem statement

We envision multiscale modeling as critical enablers of reaction understanding, catalyst and reactor design, scale-up, and process optimization. The framework includes predicting the molecular reaction mechanism at the molecular level to the process optimization stage. As catalytic processes occur at the multiscale, we address these issues individually and collectively.

At the microkinetic level, our models resolve the rates of the individual elementary steps, rate-determining step (RDS), adsorption, and desorption mechanisms. We use quantum chemical calculations (density functional theory, DFT) to support our assumed kinetic pathways, original parameter estimations, and adsorption-desorption energies.

We incorporate thermodynamic constraints into our models. Once developed, the microkinetic model could guide the catalyst and reactor design. We also have experience developing Langmuir-Hinshelwood and Eley-Rideal types of kinetic models.

At the macrokineitc level, we develop lump-based and empirical models which, in some cases, are very robust and, together with other models, can be used to extract information such as mechanism change, optimize conditions, or for reactor pre-design.

We couple hydrodynamics, heat transfer, and reaction kinetics at the reactor level in computational fluid dynamic (CFD) simulations. Together with optimization algorithms, we aim to improve operating scenarios, develop innovative reactor prototypes, and predict process behaviors at the industrial scale.

Goals

  • Microkinetics I ⇒ key thermodynamic relationships
  • Microkinetics II ⇒ fitting, training, and optimization
  • Microkinetics III ⇒ ab initio kinetic modeling
  • Macrokinetics ⇒ complex reaction networks and population balances
  • CPFD ⇒ reactor modeling and scale-up
  • CFD ⇒ reactor modeling and optimization
  • CFD II ⇒ modeling operando reactors
  • Process system engineering ⇒ gPROMS

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Related Publications

Improving robustness of kinetic models for steam reforming based on artificial neural networks and ab initio calculations

by Morlanes, Lezcano, Yerrayya, Mazumder, Castaño
Chem. Eng. J. Year: 2022 DOI: https://doi.org/10.1016/j.cej.2021.133201

Abstract

Steam reforming of hydrocarbons is and will be, in the short-medium run, the leading technology for producing hydrogen. At the same time, steam reforming has a large carbon footprint that can be decreased by implementing better kinetic models for process intensification. In this work, we have developed a methodology based on artificial neural networks to fit and improve the robustness of the kinetic model for steam reforming of naphtha surrogates (hexane and heptane) using a NiMgAl catalyst derived from hydrotalcite precursors. We analyze several strategies to obtain the fittest kinetic model and discuss the robustness of each. These models include hydrocarbon steam reforming, water gas shift and methanation reactions, and differ mainly in the type of adsorption term in the Langmuir-Hinshelwood formalism. The adsorption energies calculated by ab initio (DFT) provide insights on the different adsorption mechanisms of hydrocarbons and water on the catalyst surface sites. At the same time, validation of the kinetic model was conducted using wider range of experimental conditions and different model and real feeds (methane, naphtha, diesel and vegetable oil). In this way, the versatility of the model proposed and the strengths and weaknesses of using a data-driven approach for the kinetic model selection were proven.

Keywords

C2H REF MKM