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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Vydané v:	Bioresource technology Ročník 345; s. 126433
Hlavní autori:	Andrade Cruz, Ianny, Chuenchart, Wachiranon, Long, Fei, Surendra, K.C., Renata Santos Andrade, Larissa, Bilal, Muhammad, Liu, Hong, Tavares Figueiredo, Renan, Khanal, Samir Kumar, Fernando Romanholo Ferreira, Luiz
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Elsevier Ltd 01.02.2022
Predmet:	Anaerobic digestion Anaerobiosis Biofuels Bioreactors gas production (biological) Machine Learning Methane Modeling prediction Process instability Process optimization renewable energy sources TAN FVW DL CHP AHR HRT ZVI FFN DT MLP TVS PNN RNN OLR RBF ANFIS ML RFR AD VFAs PSO RSM PCA GIS BPA AT FCMC GEP RF VS OFMSW ANN GRN ENN FL KNN NNET SVM MNN ORP FS Modeling BP Anaerobic digestion Process instability CBP SC LS-SVM SRT C:N XGBoost COD GMLET GA WFM COM Process optimization AcoD GP CM RMSE MSS ACO LMM CSTR Machine learning FOS/TAC FF-BPNN MFs UASB ICV TS
ISSN:	0960-8524, 1873-2976, 1873-2976
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Shrnutí:	[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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23
ISSN:	0960-8524 1873-2976 1873-2976
DOI:	10.1016/j.biortech.2021.126433