Renewable Energy, Journal Year: 2024, Volume and Issue: 231, P. 120969 - 120969
Published: July 14, 2024
Language: Английский
Renewable Energy, Journal Year: 2024, Volume and Issue: 231, P. 120969 - 120969
Published: July 14, 2024
Language: Английский
Applied Catalysis B Environment and Energy, Journal Year: 2023, Volume and Issue: 340, P. 123223 - 123223
Published: Sept. 1, 2023
Language: Английский
Citations
49Bioresource Technology, Journal Year: 2024, Volume and Issue: 394, P. 130291 - 130291
Published: Jan. 4, 2024
Language: Английский
Citations
20Carbon Neutrality, Journal Year: 2024, Volume and Issue: 3(1)
Published: Jan. 8, 2024
Abstract The utilization of biochar derived from biomass residue to enhance anaerobic digestion (AD) for bioenergy recovery offers a sustainable approach advance energy and mitigate climate change. However, conducting comprehensive research on the optimal conditions AD experiments with addition poses challenge due diverse experimental objectives. Machine learning (ML) has demonstrated its effectiveness in addressing this issue. Therefore, it is essential provide an overview current ML-optimized processes biochar-enhanced order facilitate more systematic ML tools. This review comprehensively examines material flow preparation impact comprehension reviewed optimize production process perspective. Specifically, summarizes application techniques, based artificial intelligence, predicting yield properties residues, as well their AD. Overall, analysis address challenges recovery. In future research, crucial tackle that hinder implementation pilot-scale reactors. It recommended further investigate correlation between physicochemical process. Additionally, enhancing role throughout entire holds promise achieving economically environmentally optimized efficiency. Graphical
Language: Английский
Citations
19Bioresource Technology, Journal Year: 2025, Volume and Issue: unknown, P. 132115 - 132115
Published: Jan. 1, 2025
Language: Английский
Citations
3Bioresource Technology, Journal Year: 2023, Volume and Issue: 387, P. 129634 - 129634
Published: Aug. 21, 2023
Language: Английский
Citations
31Chemical Engineering Journal, Journal Year: 2023, Volume and Issue: 475, P. 146069 - 146069
Published: Sept. 15, 2023
Language: Английский
Citations
27Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 485, P. 149975 - 149975
Published: Feb. 24, 2024
Language: Английский
Citations
18ACS Sustainable Chemistry & Engineering, Journal Year: 2024, Volume and Issue: 12(19), P. 7578 - 7590
Published: April 29, 2024
Incorporating density functional theory (DFT) and machine learning (ML) methodologies, an intrinsic relationship model was developed utilizing the Kamlet–Taft parameters polarity values of 104 deep eutectic solvents (DES). DES with high lignocellulosic pretreatment efficiency were expected to be screened through synergistic combination hydrogen bond acidity (α), basicity (β), polarization (Π*) molecular index (MPI). Partial least-squares (PLS) models a variety ML used predict cellulose retention delignification. The XGBoost has highest predictive performance R2 0.97 0.91, respectively. Feature importance analysis partial dependence explain variables based on model. showed that α, β, Π* MPI donor determined efficiency. among 4 is nonlinear, there are multiple extreme in different intervals. gave parameter range corresponding Based given this study, composition ratio can selected ensure at least 80% retained 50% lignin removed. Molecular simulation results these highly efficient often contain large number bonds polar groups.
Language: Английский
Citations
13Bioresource Technology, Journal Year: 2024, Volume and Issue: 399, P. 130624 - 130624
Published: March 21, 2024
Language: Английский
Citations
10Circular Economy, Journal Year: 2024, Volume and Issue: 3(2), P. 100088 - 100088
Published: May 31, 2024
Biological treatment technologies (such as anaerobic digestion, composting, and insect farming) have been extensively employed to handle various degradable organic wastes. However, the inherent complexity instability of biological processes adversely affect production renewable energy nutrient-rich products. To ensure stable consistent product quality, researchers invested heavily in control strategies for treatment, with machine learning (ML) recently proving effective optimizing predicting parameters, detecting disturbances, enabling real-time monitoring. This review critically assesses application ML providing an in-depth evaluation key algorithms. study reveals that artificial neural networks, tree-based models, support vector machines, genetic algorithms are leading treatment. A thorough investigation applications farming underscores its remarkable capacity predict products, optimize processes, perform monitoring, mitigate pollution emissions. Furthermore, this outlines challenges prospects encountered applying highlighting crucial directions future research area.
Language: Английский
Citations
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