Hydrogen Enhancement in Syngas Through Biomass Steam Gasification: Assessment with Machine Learning Models DOI Creative Commons

Yunye Shi,

Diego Mauricio Yepes Maya, Electo Eduardo Silva Lora

и другие.

Energies, Год журнала: 2025, Номер 18(5), С. 1200 - 1200

Опубликована: Фев. 28, 2025

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 accurately handling complex, high-dimensional data more efficiently cost-effectively. This study assesses effectiveness of various learning in engineering, focusing on a comparative analysis artificial neural networks (ANNs), support vector machines (SVMs), tree-based models, regularized regression models. The results show that random forest (RF) excel syngas composition its lower heating value (LHV), achieving high precision with training testing RMSE values below 0.2 R-squared close to 1. A detailed SHAP identified steam-to-biomass ratio (SBR) as most critical factor these predictions while also noting significant impact temperature conditions. underscores importance thermal parameters gasification supports systematic integration AI production enhance predictive accuracy.

Язык: Английский

Investigating the interaction between H2O-Char and CO2-Char in co-gasification through isotope tracer and ReaxFF method DOI
Deng Zhao, Yuejun Wang, Qingxin Li

и другие.

International Journal of Hydrogen Energy, Год журнала: 2025, Номер 101, С. 863 - 874

Опубликована: Янв. 5, 2025

Язык: Английский

Процитировано

0
Explainable machine learning for predicting thermogravimetric analysis of oxidatively torrefied spent coffee grounds combustion DOI
Suluh Pambudi, Jiraporn Sripinyowanich Jongyingcharoen, Wanphut Saechua

и другие.

Energy, Год журнала: 2025, Номер unknown, С. 135288 - 135288

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Hydrogen Enhancement in Syngas Through Biomass Steam Gasification: Assessment with Machine Learning Models DOI Creative Commons

Yunye Shi,

Diego Mauricio Yepes Maya, Electo Eduardo Silva Lora

и другие.

Energies, Год журнала: 2025, Номер 18(5), С. 1200 - 1200

Опубликована: Фев. 28, 2025

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 accurately handling complex, high-dimensional data more efficiently cost-effectively. This study assesses effectiveness of various learning in engineering, focusing on a comparative analysis artificial neural networks (ANNs), support vector machines (SVMs), tree-based models, regularized regression models. The results show that random forest (RF) excel syngas composition its lower heating value (LHV), achieving high precision with training testing RMSE values below 0.2 R-squared close to 1. A detailed SHAP identified steam-to-biomass ratio (SBR) as most critical factor these predictions while also noting significant impact temperature conditions. underscores importance thermal parameters gasification supports systematic integration AI production enhance predictive accuracy.

Язык: Английский

Процитировано

0