Application of machine learning in anaerobic digestion: Perspectives and challenges
[Display omitted] •Machine learning assisted decision-making is an emerging approach.•Various machine learning models have been applied for prediction and optimization.•Anaerobic digestion instability can be reduced using machine learning algorithms.•Further research on practical application and alg...
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| Vydáno v: | Bioresource technology Ročník 345; s. 126433 |
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| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Elsevier Ltd
01.02.2022
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| Témata: | |
| ISSN: | 0960-8524, 1873-2976, 1873-2976 |
| On-line přístup: | Získat plný text |
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| Abstract | [Display omitted]
•Machine learning assisted decision-making is an emerging approach.•Various machine learning models have been applied for prediction and optimization.•Anaerobic digestion instability can be reduced using machine learning algorithms.•Further research on practical application and algorithm combination is needed.
Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions. |
|---|---|
| AbstractList | [Display omitted]
•Machine learning assisted decision-making is an emerging approach.•Various machine learning models have been applied for prediction and optimization.•Anaerobic digestion instability can be reduced using machine learning algorithms.•Further research on practical application and algorithm combination is needed.
Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions. Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions. Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions.Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions. |
| ArticleNumber | 126433 |
| Author | Renata Santos Andrade, Larissa Andrade Cruz, Ianny Long, Fei Bilal, Muhammad Chuenchart, Wachiranon Tavares Figueiredo, Renan Surendra, K.C. Khanal, Samir Kumar Liu, Hong Fernando Romanholo Ferreira, Luiz |
| Author_xml | – sequence: 1 givenname: Ianny surname: Andrade Cruz fullname: Andrade Cruz, Ianny organization: Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil – sequence: 2 givenname: Wachiranon surname: Chuenchart fullname: Chuenchart, Wachiranon organization: Department of Civil and Environmental Engineering, University of Hawaiʻi at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA – sequence: 3 givenname: Fei surname: Long fullname: Long, Fei organization: Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97333, USA – sequence: 4 givenname: K.C. surname: Surendra fullname: Surendra, K.C. organization: Department of Molecular Biosciences and Bioengineering, University of Hawaiʻi at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA – sequence: 5 givenname: Larissa surname: Renata Santos Andrade fullname: Renata Santos Andrade, Larissa organization: Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil – sequence: 6 givenname: Muhammad surname: Bilal fullname: Bilal, Muhammad organization: School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian 223003, China – sequence: 7 givenname: Hong surname: Liu fullname: Liu, Hong organization: Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97333, USA – sequence: 8 givenname: Renan surname: Tavares Figueiredo fullname: Tavares Figueiredo, Renan organization: Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil – sequence: 9 givenname: Samir Kumar surname: Khanal fullname: Khanal, Samir Kumar email: khanal@hawaii.edu organization: Department of Civil and Environmental Engineering, University of Hawaiʻi at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA – sequence: 10 givenname: Luiz surname: Fernando Romanholo Ferreira fullname: Fernando Romanholo Ferreira, Luiz organization: Graduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil |
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•Machine learning assisted decision-making is an emerging approach.•Various machine learning models have been applied for prediction and... Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate.... |
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| SubjectTerms | Anaerobic digestion Anaerobiosis Biofuels Bioreactors gas production (biological) Machine Learning Methane Modeling prediction Process instability Process optimization renewable energy sources |
| Title | Application of machine learning in anaerobic digestion: Perspectives and challenges |
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