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

Robust data curation for improved kinetic modeling in oxidative coupling of methane using high-throughput reactors

by Lezcano, Gobouri, Realpe, Kulkarni, Velisoju, Castaño
Chem. Eng. Sci. Year: 2024 DOI: https://doi.org/10.1016/j.ces.2023.119412

Abstract

High-throughput catalytic reactors are useful for developing large datasets of kinetic results and discovering faster new catalysts and models. However, these large datasets are often challenging to curate, and here we present a methodology for reinforced outlier detection using a robustified principal component analysis with a minimum covariance determinant. We estimate the kinetic parameters of three Mn-Na-W catalysts for the oxidative coupling of methane using datasets curated in different ways. These catalysts differ in their support composition (SiO2 and SiC), preparation method (impregnation or spray-dying), and performance. The kinetic model subsequently trained consists of six global reactions, and the obtained fit demonstrates that the proposed data curation method is a useful tool for enhancing the predictions of subsequent models and gaining further insight into underlying correlations within the data, especially when the aesthetic curation of data is too resource-demanding and/or biased.

Keywords

MKM CHA