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

A Data-Driven Reaction Network for the Fluid Catalytic Cracking of Waste Feeds

by Alvira, Hita, Rodriguez, Arandes, Castaño
Processes Year: 2018

Extra Information

Open Access.

Abstract

Establishing a reaction network is of uttermost importance in complex catalytic processes such as fluid catalytic cracking (FCC). This step is the seed for a faithful reactor modeling and the subsequent catalyst re-design, process optimization or prediction. In this work, a dataset of 104 uncorrelated experiments, with 64 variables, was obtained in an FCC simulator using six types of feedstock (vacuum gasoil, polyethylene pyrolysis waxes, scrap tire pyrolysis oil, dissolved polyethylene and blends of the previous), 36 possible sets of conditions (varying contact time, temperature and catalyst/oil ratio) and three industrial catalysts. Principal component analysis (PCA) was applied over the dataset, showing that the main components are associated with feed composition (27.41% variance), operational conditions (19.09%) and catalyst properties (12.72%). The variables of each component were correlated with the indexes and yields of the products: conversion, octane number, aromatics, olefins (propylene) or coke, among others. Then, a data-driven reaction network was proposed for the cracking of waste feeds based on the previously obtained correlations.

Keywords

FCC W2C MKM