Applications of machine learning in supercritical fluids research

The Journal of Supercritical Fluids, Journal Year: 2023, Volume and Issue: 202, P. 106051 - 106051

Published: July 25, 2023

Language: 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, Journal Year: 2024, Volume and Issue: 920, P. 170779 - 170779

Published: Feb. 9, 2024

Language: Английский

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

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(3)

Published: Feb. 26, 2024

Language: 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, Journal Year: 2024, Volume and Issue: 104(10), P. 6208 - 6220

Published: March 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.

Language: Английский

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

Green Chemistry, Journal Year: 2024, Volume and Issue: 26(15), P. 8642 - 8668

Published: Jan. 1, 2024

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

Language: Английский

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