Application of machine learning in anaerobic digestion: Perspectives and challenges
Ianny Andrade Cruza, Wachiranon Chuenchartb c d e, Fei Longf, K.C. Surendrac e g, Larissa Renata Santos Andradea, Muhammad Bilalh, Hong Liuf, Renan Tavares Figueiredoa i, Samir Kumar Khanalb d e j✉, Luiz Fernando Romanholo Ferreiraa i✉
- aGraduate Program in Process Engineering, Tiradentes University, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil
- bDepartment of Civil and Environmental Engineering, University of Hawai'i at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA
- cDepartment of Molecular Biosciences and Bioengineering, University of Hawai'i at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA
- dDepartment of Civil and Environmental Engineering, University of Hawaiʻi at Mānoa, 2540 Dole Street, Honolulu, HI 96822, USA
- eDepartment of Molecular Biosciences and Bioengineering, University of Hawaiʻi at Manoa, 1955 East-West Road, Honolulu, HI 96822, USA
- fDepartment of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97333, USA
- gGlobal Institute for Interdisciplinary Studies, 44600 Kathmandu, Nepal
- hSchool of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian 223003, China
- iInstitute of Technology and Research, Av. Murilo Dantas, 300, Farolândia, 49032-490 Aracaju, SE, Brazil
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Abstract
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.
TL;DRAn in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling and outlines some challenges and perspectives of ML. (Semantic Scholar)