Chemical Engineering Journal, Journal Year: 2023, Volume and Issue: 476, P. 146508 - 146508
Published: Oct. 7, 2023
Language: Английский
Chemical Engineering Journal, Journal Year: 2023, Volume and Issue: 476, P. 146508 - 146508
Published: Oct. 7, 2023
Language: Английский
The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 906, P. 167681 - 167681
Published: Oct. 14, 2023
Language: Английский
Citations
53Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 489, P. 151381 - 151381
Published: April 16, 2024
Language: Английский
Citations
19Bioresource Technology, Journal Year: 2025, Volume and Issue: unknown, P. 132115 - 132115
Published: Jan. 1, 2025
Language: Английский
Citations
2Chemical Engineering Journal, Journal Year: 2022, Volume and Issue: 449, P. 137854 - 137854
Published: June 30, 2022
Language: Английский
Citations
68The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 839, P. 156073 - 156073
Published: May 23, 2022
Language: Английский
Citations
50Journal of Electroanalytical Chemistry, Journal Year: 2023, Volume and Issue: 946, P. 117703 - 117703
Published: Aug. 20, 2023
Language: Английский
Citations
31Chemical Engineering Journal, Journal Year: 2023, Volume and Issue: 480, P. 148219 - 148219
Published: Dec. 20, 2023
Language: Английский
Citations
28Waste Management, Journal Year: 2023, Volume and Issue: 159, P. 163 - 173
Published: Feb. 9, 2023
Language: Английский
Citations
24Carbon 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
14Energy Materials, Journal Year: 2024, Volume and Issue: 4(1)
Published: Jan. 16, 2024
Accelerants can enhance methane production in biomass energy systems. Single-component accelerants cannot satisfy the demands of anaerobic co-digestion (AcoD) to maximize overall performance. In this work, nitrogen-doped bio-based carbon derived from coconut shells, containing bimetallic Ni/Fe nanoparticles, FeNi3 alloys, and compounds (Fe2O3, FeN, Fe3O4), was constructed as hybrid (Ni-N-C, Fe-N-C, Fe/Ni-N-C) boost CH4 CO2 reduction. The cumulative biogas yield (553.65, 509.65, 587.76 mL/g volatile solids), content (63.58%, 57.90%, 67.39%), total chemical oxygen demand degradation rate (60.15%, 54.92%, 65.38%) AcoD with Ni-N-C (2.625 g/L), Fe-N-C (3.500 Fe/Ni-N-C g/L) were higher than control (346.32 solids, 40.13%, 32.03%), respectively. These digestates Ni-N-C, showed excellent stability (mass loss: 22.97%-32.75%) nutrient (4.43%-4.61%). Based on synergistic effects different components accelerant, an understanding enhanced methanogenesis illustrated.
Language: Английский
Citations
13