Grzegorz Brus

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Associate professor

AGH University of Science and Technology

Poland

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About

Grzegorz Brus is an Associate Professor at AGH University of Science and Technology in Poland. His research focuses on artificial intelligence applications in energy engineering, particularly modeling solid oxide fuel cells and steam reforming processes. His recent articles explore advanced computational techniques, including machine learning and numerical analysis, to enhance energy system performance and efficiency.

Recent Grants

Grant: Close

Easy-to-Assemble Stack Type (EAST): Development of solid oxide fuel cel stack for the innovation in polish energy sector

Open Date: 2017-01-01

Close Date: 2019-12-31

Grant: Close

Trójwymiarowa analiza lokalnych zmian mikrostruktury w stosie ogniw paliwowych typu SOFC

Open Date: 2016-07-05

Close Date: 2019-07-04

Grant: Close

Evaluation of the direct internal reforming reaction in consideration of the microstructure of the SOFC porous electrode

Open Date: 2012-04-01

Close Date: 2015-03-31

Articles (20)

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2023

Collaborators (2)

Marcin Moździerz

assistant professor

AGH University of Science and Technology

POLAND

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Janusz Szmyd

Professor

AGH University of Science and Technology

POLAND

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Grzegorz Brus

Research Interests

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About

Recent Grants

Articles (20)

Artificial intelligence-based modeling of solid oxide fuel cells for improved transient prediction and control optimization

Bridging equilibrium and kinetics prediction with a data-weighted neural network model of methane steam reforming

Topology-informed machine learning for efficient prediction of solid oxide fuel cell electrode polarization

Computational topology in energy engineering: a new lens on microstructural complexity

Unveil carbon dioxide recycling potential throughout distributor-type membrane reactor

A parametric analysis of the long term performance of a solid oxide fuel cell anode

Numerical analysis of transport phenomena in a steam reforming reactor with optimal multi-segments catalyst distribution

Combining physical modeling and machine learning for micro-scale modeling of a fuel cell electrode

Enhancing a deep learning model for the steam reforming process using data augmentation techniques

Integrating experimental and numerical data for improved steam reforming simulation with deep learning

Transport Phenomena in a Banded Solid Oxide Fuel Cell Stack—Part 1: Model and Validation

Transport Phenomena in a Banded Solid Oxide Fuel Cell Stack—Part 2: Numerical Analysis

Enhancing Hydrogen Production from Biogas through Catalyst Rearrangements

Coaxial multi-criteria optimization of a methane steam reforming reactor for effective hydrogen production and thermal management

Genetic algorithm-based strategy for the steam reformer optimization

A fast Gaussian process-based method to evaluate carbon deposition during hydrocarbons reforming

Microstructure evolution of Solid Oxide Fuel Cell anodes characterized by persistent homology

Overcoming a recent impasse in the application of artificial neural networks as solid oxide fuel cells simulator with computational topology

A Surrogate Model of the Butler-Volmer Equation for the Prediction of Thermodynamic Losses of Solid Oxide Fuel Cell Electrode

The prediction of the polarization curves of a solid oxide fuel cell anode with an artificial neural network supported numerical simulation

Collaborators (2)

Marcin Moździerz

Janusz Szmyd

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