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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Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass

Energy, Journal Year: 2023, Volume and Issue: 278, P. 127967 - 127967

Published: May 29, 2023

Language: Английский

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Integrated pretreatment of poplar biomass employing p-toluenesulfonic acid catalyzed liquid hot water and short-time ball milling for complete conversion to xylooligosaccharides, glucose, and native-like lignin

Bioresource Technology, Journal Year: 2023, Volume and Issue: 384, P. 129370 - 129370

Published: June 20, 2023

Language: Английский

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Machine learning-aided hydrothermal carbonization of biomass for coal-like hydrochar production: Parameters optimization and experimental verification

Bioresource Technology, Journal Year: 2023, Volume and Issue: 393, P. 130073 - 130073

Published: Nov. 19, 2023

Language: Английский

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The role of biochar nanomaterials in the application for environmental remediation and pollution control

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 492, P. 152310 - 152310

Published: May 16, 2024

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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Advances in Research and Technology of Hydrothermal Carbonization: Achievements and Future Directions

Agronomy, Journal Year: 2024, Volume and Issue: 14(5), P. 955 - 955

Published: May 2, 2024

Hydrothermal carbonization (HTC) has emerged as a pivotal technology in the battle against climate change and fosters circular economies. Operating within unique reaction environment characterized by water solvent moderate temperatures at self-generated pressures, HTC efficiently converts biomass residues into valuable bio-based products. Despite HTC’s potential—from management of challenging wastes to synthesis advanced carbons implementation biorefineries—it encounters hurdles transitioning from academic exploration industrial implementation. Gaps persist, general comprehension intricacies difficulty large-scale integration with wastewater treatments, process water, absence standardized assessment techniques for Addressing these challenges demands collaboration bridge many scientific sectors touched HTC. Thus, this article reviews current state some hot topics considered crucial development: It emphasizes role cornerstone waste biorefineries, highlighting potentialities its development. In particular, it surveys fundamental research aspects, delving pathways, predictive models, analytical techniques, modifications while exploring technological applications challenges, peculiar focus on combined HTC, integration, plant energy efficiency.

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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Interpreting XGBoost predictions for shear-wave velocity using SHAP: Insights into gas hydrate morphology and saturation

Fuel, Journal Year: 2024, Volume and Issue: 364, P. 131145 - 131145

Published: Feb. 6, 2024

Language: Английский

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Roles of iron and manganese in bimetallic biochar composites for efficient persulfate activation and atrazine removal

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

Published: April 22, 2024

Abstract As for Atrazine (C 8 H 14 ClN 5 ) degradation in soil, iron (Fe)-manganese (Mn) bimetallic biochar composites were proved to be more efficient persulfate (PS) activation than monometallic ones. The atrazine removal rates of Fe/Mn loaded + PS systems 2.17–2.89 times higher alone. Compared with biochar, the by (77.2–96.7%) mainly attributed synergy and adsorption due larger amounts metal oxides on surface. Fe-rich was free radicals (i.e., $${\text{SO}}_{4}^{ \cdot - }$$ SO 4 · - ·OH) through oxidative routes, whereas surface-bound radicals, 1 O 2 , responsible Mn-rich systems. Furthermore, a ratio Fe(II) Mn(III) formed valence state exchange between Fe Mn contributed significantly effective generation radicals. pathways involved alkyl hydroxylation, oxidation, dealkylation, dechlorohydroxylation. results indicated that less are PS-based atrazine, which guides ration design easily available carbon materials targeted remediation various organic-polluted soil. Graphical

Language: Английский

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