Ensemble machine learning approach for examining critical process parameters and scale-up opportunities of microbial electrochemical systems for hydrogen peroxide production.

Hydrogen peroxide (H2O2) production in microbial electrochemical systems (MESs) is an attractive option for enabling a circular economy in the water/wastewater sector. Here, a machine learning algorithm was developed, using a meta-learning approach, to predict the H2O2 production rates in MES based on the seven input variables, including various design and operating parameters. The developed models were trained and cross-validated using the experimental data collected from 25 published reports. The final ensemble meta-learner model (combining 60 models) demonstrated a high prediction accuracy with very high R2 (0.983) and low root-mean-square error (RMSE) (0.647 kg H2O2 m-3 d-1) values. The model identified the carbon felt anode, GDE cathode, and cathode-to-anode volume ratio as the top three most important input features. Further scale-up analysis for small-scale wastewater treatment plants indicated that proper design and operating conditions could increase the H2O2 production rate to as high as 9 kg m-3 d-1.

Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion.

The land application of digestate from anaerobic digestion (AD) is considered a significant route for transmitting antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) to ecosystems. To date, efforts towards understanding complex non-linear interactions between AD operating parameters with ARG/MGE abundances rely on experimental investigations due to a lack of mechanistic models. Herein, three different machine learning (ML) algorithms, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), were compared for their predictive capacities in simulating ARG/MGE abundance changes during AD. The models were trained and cross-validated using experimental data collected from 33 published literature. The comparison of model performance using coefficients of determination (R2) and root mean squared errors (RMSE) indicated that ANN was more reliable than RF and XGBoost. The mode of operation (batch/semi-continuous), co-digestion of food waste and sewage sludge, and residence time were identified as the three most critical features in predicting ARG/MGE abundance changes. Moreover, the trained ANN model could simulate non-linear interactions between operational parameters and ARG/MGE abundance changes that could be interpreted intuitively based on existing knowledge. Overall, this study demonstrates that machine learning can enable a reliable predictive model that can provide a holistic optimization tool for mitigating the ARG/MGE transmission potential of AD.

A critical review of process parameters influencing the fate of antibiotic resistance genes in the anaerobic digestion of organic waste.

The overuse and inappropriate disposal of antibiotics raised severe public health risks worldwide. Specifically, the incomplete antibiotics metabolism in human and animal bodies contributes to the significant release of antibiotics into the natural ecosystems and the proliferation of antibiotic-resistant bacteria carrying antibiotic-resistant genes. Moreover, the organic feedstocks used for anaerobic digestion are often highly-rich in residual antibiotics and antibiotic-resistant genes. Hence, understanding their fate during anaerobic digestion has become a significant research focus recently. Previous studies demonstrated that various process parameters could considerably influence the propagation of the antibiotic-resistant genes during anaerobic digestion and their transmission via land application of digestate. This review article scrutinizes the influences of process parameters on antibiotic-resistant genes propagation in anaerobic digestion and the inherent fundamentals behind their effects. Based on the literature review, critical research gaps and challenges are summarized to guide the prospects for future studies.