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Hybrid pathways for methane production: Merging thermodynamic insights with machine learning
,
Peyman Karimi ,
Majid Majd,
Ian Mabbett ,
Mary Larimi ,
Ciaran Martin ,
Anna RL. Carter
Journal of Cleaner Production, Volume: 526, Start page: 146662
Swansea University Authors:
Azita Etminan, Peter Holliman , Ian Mabbett , Mary Larimi
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DOI (Published version): 10.1016/j.jclepro.2025.146662
Abstract
A comprehensive study was conducted to simultaneously simulate thermodynamic behavior and predict catalyst performance for CH4 production via CO and CO2 methanation, using blast furnace gas (BFG) and basic oxygen furnace gas (BOFG) as feedstocks. Thermodynamic equilibrium simulations based on Gibbs...
| Published in: | Journal of Cleaner Production |
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| ISSN: | 0959-6526 1879-1786 |
| Published: |
Elsevier BV
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70382 |
| Abstract: |
A comprehensive study was conducted to simultaneously simulate thermodynamic behavior and predict catalyst performance for CH4 production via CO and CO2 methanation, using blast furnace gas (BFG) and basic oxygen furnace gas (BOFG) as feedstocks. Thermodynamic equilibrium simulations based on Gibbs free energy minimization identified optimal reaction conditions at moderate temperatures (150–250 °C) and elevated pressures, achieving over 98 % CO2 conversion with less than 1 wt% carbon formation. In parallel, machine learning models were developed using an augmented dataset of 2777 experimental observations. Atomic-level structural and electronic descriptors were incorporated into the dataset, including unit cell density and formation energy for active metals, promoters, and supports. Feature selection through Pearson correlation and RFECV identified active phase weight, support density, and reduction conditions as the most influential variables. Among all tested algorithms, XGBoost and CatBoost demonstrated the highest accuracy, with R2 values exceeding 0.93 for predicting CH4 yield, selectivity, and CO2 conversion. SHAP and partial dependence analyses showed that catalyst stability and textural properties govern overall performance. This integrated thermodynamic and machine learning approach defines the operating limits for high-efficiency methanation and provides a data-driven framework for catalyst optimization in industrial applications. |
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| Keywords: |
Thermodynamic analysis; Catalyst selection; Machine learning; CO2 Methanation; Energy and Exergy Analysis |
| College: |
Faculty of Science and Engineering |
| Funders: |
We gratefully thank EPSRC and Tata Steel for co-sponsoring an iCASE PhD studentship (Voucher number 220106) for AE and EPSRC for funding the Sustain Hub (EP/S018107/1) for PJH and the Centre for Digital Citizens - Next Stage Digital Economy Centre (EP/T022582/1) for ARLC. |
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146662 |