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Modeling and scaling processes to generate high-pressure hydrogen (H2) from ammonia

Problem statement

The increasing world energy demand accelerates the depletion of fossil fuels, which consequently boosts the research and development of alternative and viable energy sources. Ammonia (NH3) is a dense, carbon-free energy and hydrogen vector. It can provide on-site hydrogen via catalytic decomposition or cracking.

Our work covers the fundamentals of the microkinetics (using benchmark catalysts such as Ru-based and cheaper, novel alternatives such as Co-Ba-Ce-based) to the reactor modeling.

We use DFT-guided and microkinetic modeling to help understand the rates and catalyst performance. Whereas the reactor modeling from the laboratory scale to the industrial-scale mandates considering the heat-mass transfer effects for efficient implementation of the process.

Goals

  • Develop a microkinetic modeling framework to analyze the catalyst performance and the effect of the role of promoters
  • Dimensionless number analysis to transcend the scale of operation and scale up
  • Using reactive computational fluid dynamics and process modeling: model and simulate an ammonia cracker unit at different scales, including a repurposed steam reformer
  • Model and simulate different reactor configurations, such as packed bed reactors with and without membranes
AMD

Related People

Related Publications

Microkinetic Modeling to Decode Catalytic Reactions and Empower Catalytic Design

by Kulkarni, Lezcano, Velisoju, Realpe, Castaño
ChemCatChem Year: 2024 DOI: https://doi.org/10.1002/cctc.202301720

Abstract

Kinetic model development is integral for designing, redesigning, monitoring, and optimizing chemical processes. Of the various approaches used within this field, microkinetic modeling is a crucial tool that focuses on surface events to analyze overall and preferential reaction pathways. This work covers noticeable features of microkinetic modeling for three critical case studies: (i) ammonia to hydrogen, (ii) oxidative coupling of methane to chemicals, and (iii) carbon dioxide hydrogenation for methanol synthesis. We analyze how microkinetic modeling enables predicting and optimizing complex reaction networks, allowing the design of efficient and tailored catalysts with enhanced activity and selectivity.

Keywords

MKM CO2 CHA AMD