Abstract
The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational parameters. Five ML models, which included tree-, regression-, and neural network-based algorithms, were applied to predict the methane yield in BEAD reactor. The results showed that various 1-step ahead ML models, which utilized prior data of BEAD performances, could enhance prediction accuracy. In addition, 1-step ahead with retraining algorithm could improve prediction accuracy by 37.3% compared with the conventional multi-step ahead algorithm. The improvement was particularly noteworthy in tree- and regression-based ML models. Moreover, 1-step ahead with retraining algorithm showed high potential of achieving efficient prediction using pH as a single input data, which is plausibly an easier monitoring parameter compared with the other parameters required in bioprocess models.
Highlights
Anaerobic digestion (AD) is gaining attention as a promising technology for biogas production from various organic wastes, such as food waste, waste activated sludge, livestock manure, and landfill leachate [1]
Results from the Principal Component Analysis (PCA) showed that pH had the highest correlation with the methane yield in the bio-electrochemical anaerobic digestion (BEAD) reactor, which meant that quickly overcoming the inhibition caused by a pH decrease could contribute to stable methane production. 1-step ahead prediction method could predict and analyze time-series data with high accuracy and prediction efficiency (Table 3)
This study confirmed that the 1-step ahead with the retraining method applied to various machine learning (ML) models was able to improve prediction accuracy of BEAD performance by retraining the prior state performances in the time series data
Summary
Anaerobic digestion (AD) is gaining attention as a promising technology for biogas production from various organic wastes, such as food waste, waste activated sludge, livestock manure, and landfill leachate [1]. Bio-electrochemical anaerobic digestion (BEAD) is gaining attention as an advanced technology that improves microbial activity and growth rates as well as organic removal efficiency and biogas productivity by supplying low voltage (0.2~1.0 V) through bioelectrodes in an AD reactor [7,8]. BEAD systems are superior to AD systems with respect to organic substances removal and biogas production, and that a decrease in pH and VFA accumulation has a low inhibitory effect on methane production [9,10,11]. Previous lab-scale studies have sufficiently demonstrated the superiority of BEAD through basic studies such as reaction mechanism identification, changes in microbial community structure, electrode configuration, and material suitability [12,13,14]
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