Two-Stage Anaerobic Digestion for Green Energy Production: A Review

Processes, Journal Year: 2025, Volume and Issue: 13(2), P. 294 - 294

Published: Jan. 21, 2025

Anaerobic digestion (AD) is a biotechnological process in which the microorganisms degrade complex organic matter to simpler components under anaerobic conditions produce biogas and fertilizer. This has many environmental benefits, such as green energy production, waste treatment, protection, greenhouse gas emissions reduction. It long been known that two main species (acidogenic bacteria methanogenic archaea) community of AD differ aspects, optimal for their growth development are different. Therefore, if performed single bioreactor (single-phase process), selected taking into account slow-growing methanogens at expense fast-growing acidogens, affecting efficiency whole process. led two-stage (TSAD) recent years, where processes divided cascade separate bioreactors (BRs). division consecutive BRs leads significantly higher yields two-phase system (H2 + CH4) compared traditional single-stage CH4 production review presents state art, advantages disadvantages, some perspectives (based on more than 210 references from 2002 2024 our own studies), including all aspects TSAD—different parameters’ influences, types bioreactors, microbiology, mathematical modeling, automatic control, energetical considerations TSAD processes.

Language: Английский

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Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects

Bioresource Technology, Journal Year: 2022, Volume and Issue: 370, P. 128501 - 128501

Published: Dec. 17, 2022

Language: Английский

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Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioresource Technology, Journal Year: 2022, Volume and Issue: 369, P. 128451 - 128451

Published: Dec. 9, 2022

Language: Английский

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Recent technological advancements in biomass conversion to biofuels and bioenergy for circular economy roadmap

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122714 - 122714

Published: Feb. 1, 2025

Language: Английский

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Applications of machine learning tools for biological treatment of organic wastes: Perspectives and challenges

Circular 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

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Two-stage biohydrogen and methane production from sugarcane-based sugar and ethanol industrial wastes: A comprehensive review

Bioresource Technology, Journal Year: 2023, Volume and Issue: 386, P. 129519 - 129519

Published: July 18, 2023

Language: Английский

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A soft sensor model based on CNN-BiLSTM and IHHO algorithm for Tennessee Eastman process

Measurement, Journal Year: 2023, Volume and Issue: 218, P. 113195 - 113195

Published: June 11, 2023

Language: Английский

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Recent advancements in biomass to bioenergy management and carbon capture through artificial intelligence integrated technologies to achieve carbon neutrality

Sustainable Energy Technologies and Assessments, Journal Year: 2024, Volume and Issue: 73, P. 104123 - 104123

Published: Dec. 7, 2024

Language: Английский

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Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction

Bioresource Technology, Journal Year: 2022, Volume and Issue: 370, P. 128518 - 128518

Published: Dec. 21, 2022

Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability incompatibility make it challenging to apply ML complicated bioprocesses, which rely on the delicate metabolic interplay among living cells. This overview attempts delineate applications bioprocess from different perspectives, their inherent limitations (i.e., uncertainties prediction) were then discussed unique supplement models. A clear classification can made depending purpose (supervised vs unsupervised) per application, as well system boundaries (engineered natural). Although a limited number hybrid approaches meaningful outcomes (e.g., improved accuracy) are available, there is still need further enhance interpretability, compatibility, user-friendliness

Language: Английский

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A quality-driven multi-attribute channel hybrid neural network for soft sensing in refining processes

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117061 - 117061

Published: March 1, 2025

Language: Английский

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