Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data. (1st July 2021)

• Record Type: Journal Article
• Authors: Long, Fei; Wang, Luguang; Cai, Wenfang; Lesnik, Keaton; Liu, Hong
• Is Part Of: Water research. Volume 199(2021)
• Publication Date: 2021-07-01
• Subjects: Machine learning -- Anaerobic digestion -- Genomic data -- Performance prediction -- Control strategy; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2021.117182
• Languages: English
• ISSNs: 0043-1354

Abstract

Highlights: Machine learning was used for evaluating anaerobic digesters using genomic data. Classification and regression models were developed to predict reactor performance. High prediction accuracy was achieved incorporating with genomic data. Critical operational parameters and microbial species were identified. The model has potential to provide guidance for early warning and process control. Abstract: Modeling of anaerobic digestion (AD) is crucial to better understand the process dynamics and to improve the digester performance. This is an essential yet difficult task due to the complex and unknown interactions within the system. The application of well-developed data mining technologies, such as machine learning (ML) and microbial gene sequencing techniques are promising in overcoming these challenges. In this study, we investigated the feasibility of 6 ML algorithms using genomic data and their corresponding operational parameters from 8 research groups to predict methane yield. For classification models, random forest (RF) achieved accuracies of 0.77 using operational parameters alone and 0.78 using genomic data at the bacterial phylum level alone. The combination of operational parameters and genomic data improved the prediction accuracy to 0.82 ( p <0.05). For regression models, a low root mean square error of 0.04 (relative root mean square error =8.6%) was acquired by neural network using genomic data at the bacterial phylum level alone. Feature importance analysis by RF suggested that Chloroflexi, Actinobacteria, Proteobacteria, Fibrobacteres, and Spirochaeta were the top 5 most important phyla although their relative abundances were ranging only from 0.1% to 3.1%. The important features identified could provide guidance for early warning and proactive management of microbial communities. This study demonstrated the promising application of ML techniques for predicting and controlling AD performance. Graphical abstract: Image, graphical abstract