Co-pyrolysis of biomass and plastic wastes and application of machine learning for modelling of the process: A comprehensive review

Journal of the Energy Institute, Год журнала: 2025, Номер 119, С. 101973 - 101973

Опубликована: Янв. 5, 2025

Язык: Английский

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Machine learning prediction of bio-oil production from the pyrolysis of lignocellulosic biomass: Recent advances and future perspectives

Journal of Analytical and Applied Pyrolysis, Год журнала: 2024, Номер 179, С. 106486 - 106486

Опубликована: Март 30, 2024

Bio-oil produced through pyrolysis of lignocellulosic biomass has recently received significant attention due to its possible uses as a second-generation biofuel. The yield and characteristics bio-oil are affected by reaction conditions the type feedstock that is used. Recently, machine learning (ML) techniques have been widely employed forecast performance bi-oil. In this study, comprehensive review ML research on carried out. Regression methods were most frequently build prediction models top five for random forest, artificial neural network, gradient boosting, support vector regression, linear regression. results developed quite consistent with experiment results. However, studies data had limitations such used restricted data, extraction features using their own knowledge, limited algorithms. We highlighted challenges potential cutting-edge in production.

Язык: Английский

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Machine learning to predict the production of bio-oil, biogas, and biochar by pyrolysis of biomass: a review

Environmental Chemistry Letters, Год журнала: 2024, Номер unknown

Опубликована: Сен. 5, 2024

Язык: Английский

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Sustainable hydrogen production via microalgae: Technological advancements, economic indicators, environmental aspects, challenges, and policy implications

Environmental Research, Год журнала: 2023, Номер 244, С. 117815 - 117815

Опубликована: Дек. 3, 2023

Язык: Английский

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From Microalgae to Bioenergy: Recent Advances in Biochemical Conversion Processes

Fermentation, Год журнала: 2023, Номер 9(6), С. 529 - 529

Опубликована: Май 29, 2023

Concerns about rising energy demand, fossil fuel depletion, and global warming have increased interest in developing utilizing alternate renewable sources. Among the available resources, microalgae biomass, a third-generation feedstock, is promising for production due to its rich biochemical composition, metabolic elasticity, ability produce numerous bioenergy products, including biomethane, biohydrogen, bioethanol. However, true potential of biomass future economy yet be realized. This review provides comprehensive overview various conversion processes (anaerobic digestion, direct biophotolysis, indirect photo fermentation, dark microalgae-catalyzed traditional alcoholic fermentation by ethanologenic microorganisms) that could adapted transform into different products. Recent advances are compiled critically analyzed, their limitations terms process viability, efficacy, scalability, economic environmental sustainability highlighted. Based on current research stage technological development, biomethane from anaerobic digestion bioethanol identified as methods commercialization microalgae-based bioenergy. significant challenges these technologies’ remain, high costs low recovery efficiency. Future should focus reducing costs, an integrated biorefinery approach, effectively artificial intelligence tools optimization scale-up solve accelerate development

Язык: Английский

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Microwave-assisted In-situ catalytic co-pyrolysis of polypropylene and polystyrene mixtures: Response surface methodology analysis using machine learning

Journal of Analytical and Applied Pyrolysis, Год журнала: 2023, Номер 172, С. 105984 - 105984

Опубликована: Апрель 28, 2023

Язык: Английский

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Study on waste tire pyrolysis product characteristics based on machine learning

Journal of environmental chemical engineering, Год журнала: 2023, Номер 11(6), С. 111314 - 111314

Опубликована: Окт. 27, 2023

Язык: Английский

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Torrefied biomass quality prediction and optimization using machine learning algorithms

Chemical Engineering Journal Advances, Год журнала: 2024, Номер 19, С. 100620 - 100620

Опубликована: Июнь 26, 2024

Torrefied biomass is a vital green energy source with applications in circular economies, addressing agricultural residue and rising demands. In this study, ML models were used to predict durability (%) mass loss (%). Firstly, data was collected preprocessed, its distribution correlation analyzed. Gaussian Process Regression (GPR) Ensemble Learning Trees (ELT) then trained tested on 80 % 20 of the data, respectively. Both machine learning underwent optimization through Genetic Algorithm (GA) Particle Swarm Optimization (PSO) for feature selection hyperparameter tuning. GPR-PSO demonstrates excellent accuracy predicting (%), achieving training R2 score 0.9469 an RMSE value 0.0785. GPR-GA exhibits exceptional performance 1 9.7373e-05. The temperature duration during torrefaction are crucial variables that line conclusions drawn from previous studies. GPR ELT effectively optimize torrefied quality, leading enhanced density, mechanical properties, grindability, storage stability. Additionally, they contribute sustainable agriculture by reducing carbon emissions, improving cost-effectiveness, aiding design development pelletizers. This not only increases density grindability but also enhances nutrient delivery efficiency, water retention, reduces footprint. Consequently, these outcomes support biodiversity promote agricultural, ecosystem, environmental practices.

Язык: Английский

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Artificial intelligence-driven prediction models for the cultivation of Chlorella vulgaris FSP-E in food waste culture medium: A comparative analysis and validation of models

Algal Research, Год журнала: 2025, Номер unknown, С. 103935 - 103935

Опубликована: Янв. 1, 2025

Язык: Английский

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Prediction and optimization of exergetic efficiency of reactive units of a petroleum refinery under uncertainty through artificial neural network-based surrogate modeling

Process Safety and Environmental Protection, Год журнала: 2023, Номер 177, С. 1403 - 1414

Опубликована: Авг. 1, 2023

Язык: Английский

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