Predicting Methane Dry Reforming Performance via Multi-Output Machine Learning: A Comparative Study of Regression Models

Sheila Devasahayam (), John Samuel Thella and Manoj K. Mohanty
Additional contact information
Sheila Devasahayam: WA School of Mines (WASM), Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie, WA 6430, Australia
John Samuel Thella: Department of Mining and Metallurgical Engineering, University of Nevada, Reno, NV 89557, USA
Manoj K. Mohanty: Department of Mining and Metallurgical Engineering, University of Nevada, Reno, NV 89557, USA

Energies, 2025, vol. 18, issue 18, 1-42

Abstract: Dry reforming of methane (DRM) offers a sustainable route to convert two major greenhouse gases—CH 4 and CO 2 —into synthesis gas (syngas), enabling low-carbon hydrogen production and carbon utilization. This study applies fifteen machine learning (ML) regression models to simultaneously predict CH 4 conversion, CO 2 conversion, H 2 yield, and CO yield using a published dataset of 27 experiments with Ni/CaFe 2 O 4 -catalyzed DRM. The comparative evaluation covers linear, tree-based, ensemble, and kernel-based algorithms under a unified multi-output learning framework. Feature importance analysis highlights reaction temperature, CH 4 /CO 2 feed ratio, and Ni metal loading as the most influential variables. Predictions from the top-performing models (CatBoost and Random Forest) identify optimal performance windows—feed ratio near 1.0 and temperature between 780–820 °C—consistent with thermodynamic and kinetic expectations. Although no new catalysts are introduced, the study demonstrates how ML can extract actionable parametric insights from small experimental datasets, guiding future DRM experimentation and process optimization for hydrogen-rich syngas production.

Keywords: dry reforming of methane (DRM); low-carbon hydrogen; multi-output machine learning regression models; feature transformation; carbon utilization; generalizability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/18/4807/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/18/4807/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:18:p:4807-:d:1745870

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().