Machine learning applications for biochar studies: A mini-review

Bioresource Technology, Journal Year: 2024, Volume and Issue: 394, P. 130291 - 130291

Published: Jan. 4, 2024

Language: Английский

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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, Journal Year: 2025, Volume and Issue: 119, P. 101973 - 101973

Published: Jan. 5, 2025

Language: Английский

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Energy-efficient design of cyclone separators: Machine learning prediction of particle self-rotation velocities

Energy, Journal Year: 2025, Volume and Issue: 316, P. 134452 - 134452

Published: Jan. 7, 2025

Language: Английский

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Biocrude production via hydrothermal liquefaction of cycas circinalis seed shell: A machine learning approach

International Journal of Green Energy, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: Jan. 15, 2025

Hydrothermal liquefaction (HTL) is a promising thermochemical method for converting biomass into bio-crude fuel. This study explores the HTL of Cycas circinalis seed shell (CSS), focusing on impacts reaction time, feed slurry concentration, and temperature yield. Experiments were conducted at temperatures ranging from 250 to 375°C, times 10 40 minutes, concentrations between 10% 30%. A decision tree regression (DTR) model predicted optimal yield 35% 30% with high accuracy (R² = 0.9853, RMSE 0.992). Results highlight time as key factors influencing production.The was characterized using Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS). Degradation kinetics CSS analyzed Coats-Redfern heating rates 5, 10, 20°C/min. Parameters such activation energy (E), rate constant, pre-exponential factor (A), enthalpy, entropy, Gibbs free determined. research advances hydrothermal technology promotes development sustainable efficient conversion processes.

Language: Английский

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A novel intelligent system based on machine learning for hydrochar multi-target prediction from the hydrothermal carbonization of biomass

Biochar, Journal Year: 2024, Volume and Issue: 6(1)

Published: March 1, 2024

Abstract Hydrothermal carbonization (HTC) is a thermochemical conversion technology to produce hydrochar from wet biomass without drying, but it time-consuming and expensive experimentally determine the optimal HTC operational conditions of specific desired hydrochar. Therefore, machine learning (ML) approach was used predict optimize properties. Specifically, biochemical components (proteins, lipids, carbohydrates) were predicted analyzed first via elementary composition. Then, accurate single-biomass (no mixture) based ML multi-target models (average R 2 = 0.93 RMSE 2.36) built properties (yield, elemental composition, atomic ratio, higher heating value). Biomass composition (elemental biochemical), proximate analyses, inputs herein. Interpretation model results showed that ash, temperature, N C content most critical factors affecting properties, relative importance (25%) for than operating (19%). Finally, an intelligent system constructed on model, verified by applying ratios (N/C, O/C, H/C). It could also be extended production samples with experimental validation co-HTC mixed reported in literature. This study advances field integrating predictive modeling, systems, mechanistic insights, offering holistic precise control optimization through HTC. Graphical

Language: Английский

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Machine learning for sustainable organic waste treatment: a critical review

npj Materials Sustainability, Journal Year: 2024, Volume and Issue: 2(1)

Published: April 8, 2024

Abstract Data-driven modeling is being increasingly applied in designing and optimizing organic waste management toward greater resource circularity. This study investigates a spectrum of data-driven techniques for treatment, encompassing neural networks, support vector machines, decision trees, random forests, Gaussian process regression, k -nearest neighbors. The application these explored terms their capacity complex processes. Additionally, the delves into physics-informed highlighting significance integrating domain knowledge improved model consistency. Comparative analyses are carried out to provide insights strengths weaknesses each technique, aiding practitioners selecting appropriate models diverse applications. Transfer learning specialized network variants also discussed, offering avenues enhancing predictive capabilities. work contributes valuable field modeling, emphasizing importance understanding nuances technique informed decision-making various treatment scenarios.

Language: 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, Journal Year: 2024, Volume and Issue: 179, P. 106486 - 106486

Published: March 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.

Language: Английский

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Co-hydrothermal carbonization of sludge and food waste for hydrochar valorization: Effect of mutual interaction on sulfur transformation

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 905, P. 167318 - 167318

Published: Sept. 24, 2023

Language: Английский

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Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes

Energy, Journal Year: 2023, Volume and Issue: 288, P. 129854 - 129854

Published: Dec. 3, 2023

Language: Английский

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Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 945, P. 173939 - 173939

Published: June 20, 2024

Language: Английский

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