Journal article
Hydrogen Enhancement in Syngas Through Biomass Steam Gasification: Assessment with Machine Learning Models
Energies (Basel), Vol.18(5), 1200
02/28/2025
DOI: 10.3390/en18051200
Abstract
Artificial intelligence (AI), particularly supervised machine learning, has revolutionized the biofuel industry by enhancing feedstock selection, predicting fluid compositions, optimizing operations, and streamlining decision-making. These algorithms outperform traditional models by accurately handling complex, high-dimensional data more efficiently and cost-effectively. This study assesses the effectiveness of various machine learning algorithms in engineering, focusing on a comparative analysis of artificial neural networks (ANNs), support vector machines (SVMs), tree-based models, and regularized regression models. The results show that random forest (RF) models excel in predicting syngas composition and its lower heating value (LHV), achieving high precision with training and testing RMSE values below 0.2 and R-squared values close to 1. A detailed SHAP analysis identified the steam-to-biomass ratio (SBR) as the most critical factor in these predictions while also noting the significant impact of temperature conditions. This underscores the importance of thermal parameters in gasification and supports the systematic integration of AI in biofuel production to enhance predictive accuracy.
Details
- Title: Subtitle
- Hydrogen Enhancement in Syngas Through Biomass Steam Gasification: Assessment with Machine Learning Models
- Creators
- Yunye Shi - University of Tennessee at ChattanoogaDiego Mauricio Yepes Maya - Universidade Federal de ItajubáElecto Silva Lora - Universidade Federal de ItajubáAlbert Ratner - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Energies (Basel), Vol.18(5), 1200
- Publisher
- MDPI
- DOI
- 10.3390/en18051200
- ISSN
- 1996-1073
- eISSN
- 1996-1073
- Grant note
- KBIH FoundationResearch Support Foundation of the state of Minas Gerais (FAPEMIG): RED-00090-21 National Council for Scientific and Technological Development (CNPq): 406948/2021-6, 302860/2022-3 Coordination for the Improvement of Higher-Level Personnel (CAPES)BRICS project CNPQ/Finep/MCTIC/BRICS-STI: 03/2019, 442318/2017-0 University of Itajuba (UNIFEI)Deutsche Gesellschaft fuer Internationale Zusammenarbeit (GIZ) Grant: 81281464
The authors wish to express their thanks to the KBIH Foundation, Research Support Foundation of the state of Minas Gerais (FAPEMIG)-project number RED-00090-21, the National Council for Scientific and Technological Development (CNPq)-project numbers 406948/2021-6 and 302860/2022-3, the Coordination for the Improvement of Higher-Level Personnel (CAPES) and the BRICS project CNPQ/Finep/MCTIC/BRICS-STI N degrees 03/2019, Process: 442318/2017-0, the project "Green Hydrogen Center (CH2V), between the Federal University of Itajuba (UNIFEI) and the Deutsche Gesellschaft fuer Internationale Zusammenarbeit (GIZ) Grant (contract) No. 81281464.
- Language
- English
- Date published
- 02/28/2025
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9984799681302771
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