Machine learning-driven optimization of pretreatment and enzymatic hydrolysis of sugarcane bagasse: Analytical insights for industrial scale-up

Fuel, Год журнала: 2025, Номер 390, С. 134682 - 134682

Опубликована: Фев. 19, 2025

Язык: Английский

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Machine learning-driven optimization of biphasic pretreatment conditions for enhanced lignocellulosic biomass fractionation

Energy, Год журнала: 2025, Номер unknown, С. 136241 - 136241

Опубликована: Апрель 1, 2025

Язык: Английский

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Lignocellulose‐Derived Energy Materials and Chemicals: A Review on Synthesis Pathways and Machine Learning Applications

Small Methods, Год журнала: 2025, Номер unknown

Опубликована: Апрель 23, 2025

Abstract Lignocellulose biomass, Earth's most abundant renewable resource, is crucial for sustainable production of high–value chemicals and bioengineered materials, especially energy storage. Efficient pretreatment vital to boost lignocellulose conversion bioenergy biomaterials, cut costs, broaden its energy–sector applications. Machine learning (ML) has become a key tool in this field, optimizing processes, improving decision‐making, driving innovation valorization This review explores main strategies – physical, chemical, physicochemical, biological, integrated methods evaluating their pros cons It also stresses ML's role refining these supported by case studies showing effectiveness. The examines challenges opportunities integrating ML into storage, underlining pretreatment's importance unlocking lignocellulose's full potential. By blending process knowledge with advanced computational techniques, work aims spur progress toward sustainable, circular bioeconomy, particularly storage solutions.

Язык: Английский

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Comparative Evaluation of Ensemble Machine Learning Models for Methane Production from Anaerobic Digestion

Fermentation, Год журнала: 2025, Номер 11(3), С. 130 - 130

Опубликована: Март 7, 2025

This study provides a comparative evaluation of several ensemble model constructions for the prediction specific methane yield (SMY) from anaerobic digestion. From authors’ knowledge based on existing research, present their accuracy and utilization in digestion modeling relative to individual machine learning methods is incomplete. Three input datasets compiled samples using agricultural forestry lignocellulosic residues previous studies were used this study. A total six five evaluated per dataset, whose was assessed robust 10-fold cross-validation 100 repetitions. Ensemble models outperformed one out three terms accuracy. They also produced notably lower coefficients variation root-mean-square error (RMSE) than most accurate (0.031 0.393 dataset A, 0.026 0.272 B, 0.021 0.217 AB), being much less prone randomness training test data split. The optimal generally benefited higher number included, as well diversity principles. Since reporting final fitting single split-sample approach highly randomness, adoption multiple repetitions proposed standard future studies.

Язык: Английский

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Artificial Intelligence in Biofuel Applications

Опубликована: Апрель 30, 2025

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Green synthesis pathways for efficient rice straw utilization in agriculture

Rendiconti lincei. Scienze fisiche e naturali, Год журнала: 2025, Номер unknown

Опубликована: Май 23, 2025

Язык: Английский

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