Application of Various Machine Learning Models for Process Stability of Bio-Electrochemical Anaerobic Digestion

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.

A Systematic Review of Machine-Learning Solutions in Anaerobic Digestion

“…The complexity of AD, influenced by numerous variables such as substrate composition, temperature, pH levels, hydraulic retention time, and microbial community dynamics, poses challenges in monitoring and optimisation. Machine Learning (ML) has emerged as a pivotal tool in interpreting the nonlinear relationships inherent in these AD systems, enhancing control, operational safety, and performance forecasting [1,2]. Literature reviews in the domain of ML applications for AD, such as those by Cruz et al [3] and Gupta et al [4], acknowledge the nascent stage of ML-based solutions in AD.…”