Applications of machine learning in supercritical fluids research

The Journal of Supercritical Fluids, Год журнала: 2023, Номер 202, С. 106051 - 106051

Опубликована: Июль 25, 2023

Язык: Английский

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Predicting co-liquefaction bio-oil of sewage sludge and algal biomass via machine learning with experimental optimization: Focus on yield, nitrogen content, and energy recovery rate

The Science of The Total Environment, Год журнала: 2024, Номер 920, С. 170779 - 170779

Опубликована: Фев. 9, 2024

Язык: Английский

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Artificial intelligence methods for modeling gasification of waste biomass: a review

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(3)

Опубликована: Фев. 26, 2024

Язык: Английский

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Optimization of computational intelligence approach for the prediction of glutinous rice dehydration

Journal of the Science of Food and Agriculture, Год журнала: 2024, Номер 104(10), С. 6208 - 6220

Опубликована: Март 7, 2024

Five computational intelligence approaches, namely Gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT) and support vector machine (SVM), were used to describe the evolution moisture during dehydration glutinous rice. The hyperparameters models optimized with three strategies: Bayesian optimization, grid search random search. To understand parameters that facilitate model adaptation process, global sensitivity analysis (GSA) was compute impact input variables on output.

Язык: Английский

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Biomass-derived carbon-based catalysts for lignocellulosic biomass and waste valorisation: a circular approach

Green Chemistry, Год журнала: 2024, Номер 26(15), С. 8642 - 8668

Опубликована: Янв. 1, 2024

Within a circular approach, cost-effective, tailored and robust biomass-derived catalysts to convert biomass play key role in biorefinery developments.

Язык: Английский

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