Colloids and Surfaces B Biointerfaces, Journal Year: 2025, Volume and Issue: 251, P. 114588 - 114588
Published: Feb. 22, 2025
Language: Английский
Colloids and Surfaces B Biointerfaces, Journal Year: 2025, Volume and Issue: 251, P. 114588 - 114588
Published: Feb. 22, 2025
Language: Английский
Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106818 - 106818
Published: Jan. 1, 2025
Language: Английский
Citations
0Entropy, Journal Year: 2025, Volume and Issue: 27(1), P. 83 - 83
Published: Jan. 17, 2025
The co-gasification of biomass and plastic waste offers a promising solution for producing hydrogen-rich syngas, addressing the rising demand cleaner energy. However, optimizing this complex process to maximize hydrogen yield remains challenging, particularly when balancing diverse feedstocks improving efficiency. While machine learning (ML) has shown significant potential in simulating such processes, there is no clear consensus on most effective regression models co-gasification, especially with limited experimental data. Additionally, interpretability these key concern. This study aims bridge gaps through two primary objectives: (1) modeling using seven different ML algorithms, (2) developing framework evaluating model interpretability, ultimately identifying suitable optimization. A comprehensive set experiments was conducted across three dimensions, generalization ability, predictive accuracy, thoroughly assess models. Support Vector Regression (SVR) exhibited superior performance, achieving highest coefficient determination (R2) 0.86. SVR outperformed other capturing non-linear dependencies demonstrated overfitting mitigation. further highlights limitations models, emphasizing importance regularization hyperparameter tuning stability. By integrating Shapley Additive Explanations (SHAP) into evaluation, work first provide detailed insights feature demonstrate operational feasibility industrial-scale production process. findings contribute development robust supporting advancement sustainable energy technologies reduction greenhouse gas (GHG) emissions.
Language: Английский
Citations
0Published: Jan. 1, 2025
Language: Английский
Citations
0Bioresource Technology, Journal Year: 2025, Volume and Issue: unknown, P. 132242 - 132242
Published: Feb. 1, 2025
Language: Английский
Citations
0Colloids and Surfaces B Biointerfaces, Journal Year: 2025, Volume and Issue: 251, P. 114588 - 114588
Published: Feb. 22, 2025
Language: Английский
Citations
0