From data to models: a purpose-driven framework for catalyst modelling in automotive applications.
Doctoral thesis, 2026
Combustion engines will remain important in heavy-duty transport for years, especially in long-haul and marine shipping. During this transition, emission aftertreatment is one of the fastest ways to reduce harmful pollutants from vehicles already in use. Stricter regulations (such as Euro 7 and EPA 27) demand low emissions during rapid transients and across a wide operating range with detailed monitoring strategies.
To meet this challenge, mechanistic catalyst models are essential. By embedding physics and chemistry in the model structure, they provide insight, transparency, and better control than purely black-box approaches. They also enable hybrid strategies that combine physical modelling with data-driven methods, while keeping a clear link to real mechanisms and parameters.
This thesis advances modelling for Selective Catalytic Reduction (SCR) catalysts in lean-burn engines, focusing on vanadium-based NH3-SCR and Pd-based H2-SCR. The work targets both mechanistic understanding and model quality. A workflow is developed from experiments to validated models: reactor characterization to correct dispersion and delay effects, steady-state analysis to create model structures using a data-driven framework, and model discrimination to reduce overfitting and parameter correlation.
For vanadium-based NH3-SCR, experiments and modelling are combined to capture NH3 adsorption and internal mass transfer in a monolith reactor. Model structures are treated as variables and optimized with a custom algorithm, supported by new objective functions that improve robustness. The final model identifies five Langmuir-type NH3 adsorption sites with different behavior to temperature and water. Steady-state parameters are linked to thermodynamic properties, strengthening physical meaning and parameter validity. Internal mass transfer is assessed using two washcoat formulations and by considering non-square channels. The result is a compact, detailed model that captures key SCR dynamics and improves prediction of transient responses across wide conditions.
For Pd-based H2-SCR aimed at lean H2 engines, 1 wt% Pd catalysts on Al2O3, TiO2, BEA zeolite, and SSZ-13 zeolite are tested from 100–300°C under varying H2/NO ratios in dry and wet conditions. Pd/TiO2 gives the highest NO conversion and N2 yield, consistent with material characterization. A kinetic model coupled with mass transfer simulates NO reduction to N2, N2O, and NH3, together with H2 oxidation, and reproduces trends for all supports. NH3 forms only on Pd/TiO2, while N2O forms on all supports and is unaffected by water concentration. External mass transfer is shown to limit fast H2 oxidation, increasing H2 available for NO reduction.
To meet this challenge, mechanistic catalyst models are essential. By embedding physics and chemistry in the model structure, they provide insight, transparency, and better control than purely black-box approaches. They also enable hybrid strategies that combine physical modelling with data-driven methods, while keeping a clear link to real mechanisms and parameters.
This thesis advances modelling for Selective Catalytic Reduction (SCR) catalysts in lean-burn engines, focusing on vanadium-based NH3-SCR and Pd-based H2-SCR. The work targets both mechanistic understanding and model quality. A workflow is developed from experiments to validated models: reactor characterization to correct dispersion and delay effects, steady-state analysis to create model structures using a data-driven framework, and model discrimination to reduce overfitting and parameter correlation.
For vanadium-based NH3-SCR, experiments and modelling are combined to capture NH3 adsorption and internal mass transfer in a monolith reactor. Model structures are treated as variables and optimized with a custom algorithm, supported by new objective functions that improve robustness. The final model identifies five Langmuir-type NH3 adsorption sites with different behavior to temperature and water. Steady-state parameters are linked to thermodynamic properties, strengthening physical meaning and parameter validity. Internal mass transfer is assessed using two washcoat formulations and by considering non-square channels. The result is a compact, detailed model that captures key SCR dynamics and improves prediction of transient responses across wide conditions.
For Pd-based H2-SCR aimed at lean H2 engines, 1 wt% Pd catalysts on Al2O3, TiO2, BEA zeolite, and SSZ-13 zeolite are tested from 100–300°C under varying H2/NO ratios in dry and wet conditions. Pd/TiO2 gives the highest NO conversion and N2 yield, consistent with material characterization. A kinetic model coupled with mass transfer simulates NO reduction to N2, N2O, and NH3, together with H2 oxidation, and reproduces trends for all supports. NH3 forms only on Pd/TiO2, while N2O forms on all supports and is unaffected by water concentration. External mass transfer is shown to limit fast H2 oxidation, increasing H2 available for NO reduction.
Catalyst Modelling
NH3-SCR
Modelling Framework
H2-SCR
Scaniasalen, Chalmersplatsen 1, Chalmers
Opponent: Prof. Enrico Tronconi, Department of Energy, Politecnico di Milano, Italy
Author
Andres Felipe Suarez Corredor
Chalmers, Chemistry and Chemical Engineering, Chemical Technology
Characterization Method for Gas Flow Reactor Experiments - NH<inf>3</inf>Adsorption on Vanadium-Based SCR Catalysts
Industrial & Engineering Chemistry Research,;Vol. 60(2021)p. 11399-11411
Journal article
Understanding the NH<inf>3</inf> adsorption mechanism on a vanadium-based SCR catalyst: A data-driven modeling approach
Chemical Engineering Science,;Vol. 262(2022)
Journal article
A. Suarez-Corredor, J. Shao, M. Bäbler, B. Westerberg, and L. Olsson. Influence of Catalyst Supports on H2-SCR Catalysts: A Combined Experimental and Modelling Approach Including Mass Transfer.
A. Suarez-Corredor, M. Bäbler, L. Olsson, A. Widd, M. Skoglundh, and B. Westerberg. NH3 Adsorption Dynamics in a Vanadium-based SCR Catalyst: Implications of an Improved Mass Transfer Description.
Combustion engines will remain part of heavy-duty transport for years, especially in hard-to-change sectors such as long-haul trucking and marine shipping. Even in ambitious climate plans, combustion engines are expected to stay in the global fleet. The transition will also look different across the world: some regions can decarbonize faster, while low-income and developing countries may rely longer on cheaper, but unstable, fossil-based systems. With today’s geopolitical uncertainty, there is no single solution for “sustainable transportation”, so we cannot focus on only one technology.
This is why improving what we already use still matters. For combustion engines, exhaust aftertreatment systems are essential to cut harmful pollutants and reduce impacts on air quality and health. As regulations get stricter, these systems become more advanced, and better understanding of the catalysts becomes critical.
This thesis addresses that need by developing modelling frameworks for Selective Catalytic Reduction (SCR) catalysts for both diesel and hydrogen applications. It covers the full process of building useful mechanistic models: designing experiments, extracting information from data, testing model structures, validating results, and preparing models for practical use. Because mechanistic models are based on physical principles, they offer insight and transparency, and they also support hybrid approaches combined with Machine Learning.
For NH3-SCR, the thesis improves the description of NH3 adsorption and studies how internal mass transfer can affect adsorption in a vanadium-based catalyst. A data-driven model discrimination approach tests many model structures, aiming for strong prediction without overfitting, and links adsorption behavior to thermodynamics for physical meaning and external validity. For H2-SCR, the thesis combines experiments and modelling to show how catalyst supports affect NO conversion, formation of N2O and NH3, and how external mass transfer can improve hydrogen use for NO reduction.
This is why improving what we already use still matters. For combustion engines, exhaust aftertreatment systems are essential to cut harmful pollutants and reduce impacts on air quality and health. As regulations get stricter, these systems become more advanced, and better understanding of the catalysts becomes critical.
This thesis addresses that need by developing modelling frameworks for Selective Catalytic Reduction (SCR) catalysts for both diesel and hydrogen applications. It covers the full process of building useful mechanistic models: designing experiments, extracting information from data, testing model structures, validating results, and preparing models for practical use. Because mechanistic models are based on physical principles, they offer insight and transparency, and they also support hybrid approaches combined with Machine Learning.
For NH3-SCR, the thesis improves the description of NH3 adsorption and studies how internal mass transfer can affect adsorption in a vanadium-based catalyst. A data-driven model discrimination approach tests many model structures, aiming for strong prediction without overfitting, and links adsorption behavior to thermodynamics for physical meaning and external validity. For H2-SCR, the thesis combines experiments and modelling to show how catalyst supports affect NO conversion, formation of N2O and NH3, and how external mass transfer can improve hydrogen use for NO reduction.
KCK - Kompetenscentrum Katalys 2022-2026
Umicore (KCK2022-2026), 2022-01-01 -- 2026-12-31.
Preem (KCK2022-2026), 2022-01-01 -- 2026-12-31.
Scania AB (Dnr:2021-036543Pnr:52689-1), 2022-01-01 -- 2026-12-31.
Volvo Group (PO:2435702-000), 2022-01-01 -- 2026-12-31.
Johnson Matthey (2500123383), 2022-01-01 -- 2026-12-31.
Driving Forces
Sustainable development
Areas of Advance
Transport
Materials Science
Subject Categories (SSIF 2025)
Chemical Engineering
Physical Chemistry
DOI
10.63959/chalmers.dt/5831
ISBN
978-91-8103-374-8
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5831
Publisher
Chalmers
Scaniasalen, Chalmersplatsen 1, Chalmers
Opponent: Prof. Enrico Tronconi, Department of Energy, Politecnico di Milano, Italy