Cross-scale catalyst modeling framework: A case study of H2 storage and release via formic acid

03 June 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

We propose a novel Systems-to-Atoms (S2A) modeling framework that integrates the kinetics of reaction chemistry and structural configurations across various length scales with the aim to establish a versatile template for multiscale modeling of reactive flow problems and to predict the operando activity of catalyst materials. The approach encompasses a microkinetic model to forecast surface reactions on individual facets of catalyst nanoparticles, coupled with the computation of average surface reaction rates for catalyst nanoparticles of specific size distributions. Macro-homogeneous surface reaction kinetics are derived as a function of catalyst loading, and used as input parameter for the continuum-scale reactor model. The cross-scale framework enables the optimization of catalyst utilization through reactor design and operating strategy. To demonstrate the S2A framework, we study the storage and release of hydrogen from formic acid using Pd, Pt, and Cu catalysts. Formic acid is a promising liquid organic hydrogen carrier (LOHC), given its high volumetric capacity and low toxicity and flammability under ambient conditions. The framework predicts observed trends in formic acid dehydrogenation activity for catalysts with comparable weight loading and metal particle diameters, demonstrating satisfactory quantitative alignment. Finally, the seamless transmission of parameter uncertainties between scales is also discussed.

Keywords

Liquid organic hydrogen carrier
Catalyst
Reactor modeling
Formic acid
Cross-scale modeling

Supplementary materials

Title
Description
Actions
Title
Details on Micro-kinetic model, meso and reactor modeling
Description
Includes details on Micro-kinetic model, meso and reactor modeling, associated equations, sensitivity analysis, and the comparison with experimentally reported data
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