Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches

Nguyen, Van Giao; Sharma, Prabhakar; Ağbulut, Ümit; Le, Huu Son; Cao, Dao Nam; Dzida, Marek; Osman, Sameh M.; Le, Huu Cuong; Tran, Viet Dung

doi:10.1080/15435075.2024.2326076

International Journal of Green Energy

2024

DOI: 10.1080/15435075.2024.2326076

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Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches

Van Giao Nguyen,

Prabhakar Sharma,

Ümit Ağbulut

et al.

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2024

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Cited by 3 publications

References 285 publications

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Influence of metakaolin on pervious concrete strength: a machine learning approach with shapley additive explanations

Sathiparan,

Jeyananthan,

Subramaniam

2024

Multiscale and Multidiscip. Model. Exp. and Des.

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Influence of metakaolin on pervious concrete strength: a machine learning approach with shapley additive explanations

Sathiparan,

Jeyananthan,

Subramaniam

2024

Multiscale and Multidiscip. Model. Exp. and Des.

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Harnessing Biomass Energy: Advancements through Machine Learning and AI Applications for Sustainability and Efficiency

Balakrishnan,

Sharma,

Bora

et al. 2024

Process Safety and Environmental Protection

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Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

Le,

Paramasivam,

Adril

et al. 2024

Int. J. Renew. Energy Dev.

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This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

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Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches

Cited by 3 publications

References 285 publications

Influence of metakaolin on pervious concrete strength: a machine learning approach with shapley additive explanations

Influence of metakaolin on pervious concrete strength: a machine learning approach with shapley additive explanations

Harnessing Biomass Energy: Advancements through Machine Learning and AI Applications for Sustainability and Efficiency

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

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