Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion.

Microplastics as emerging pollutants have been heavily accumulated in the waste activated sludge (WAS) during biological wastewater treatment, which showed significantly diverse impacts on the subsequent anaerobic sludge digestion for methane production. However, a robust modeling approach for predicting and unveiling the complex effects of accumulated microplastics within WAS on methane production is still missing. In this study, four automated machine learning (AutoML) approach was applied to model the effects of microplastics on anaerobic digestion processes, and integrated explainable analysis was explored to reveal the relationships between key variables (e.g., concentration, type, and size of microplastics) and methane production. The results showed that the gradient boosting machine had better prediction performance (mean squared error (MSE) = 17.0) than common neural networks models (MSE = 58.0), demonstrating that the AutoML algorithms succeeded in predicting the methane production and could select the best machine learning model without human intervention. Explainable analysis results indicated that the variable of microplastic types was more important than the variable of microplastic diameter and concentration. The existence of polystyrene was associated with higher methane production, whereas increasing microplastic diameter and concentration both inhibited methane production. This work also provided a novel modeling approach for comprehensively understanding the complex effects of microplastics on methane production, which revealed the dependence relationships between methane production and key variables and may be served as a reference for optimizing operational adjustments in anaerobic digestion processes.

Formation of microbial products by activated sludge in the presence of a metabolic uncoupler o-chlorophenol in long-term operated sequencing batch reactors.

Metabolic uncouplers are widely used for reducing excess sludge in biological wastewater treatment systems. However, the formation of microbial products, such as extracellular polymeric substances, polyhydroxyalkanoate and soluble microbial products by activated sludge in the presence of metabolic uncouplers remains unrevealed. In this study, the impacts of a metabolic uncoupler o-chlorophenol (oCP) on the reduction of activated sludge yield and formation of microbial products in laboratory-scale sequencing batch reactors (SBRs) were evaluated for a long-term operation. The results show the average reduction of sludge yield in the four reactors was 17.40%, 25.80%, 33.02% and 39.50%, respectively, when dosing 5, 10, 15, and 20 mg/L oCP. The oCP addition slightly reduced the pollutant removal efficiency and decreased the formation of soluble microbial products in the SBRs, but stimulated the productions of extracellular polymeric substances and polyhydroxyalkanoate in activated sludge. Furthermore, the significant reduction of electronic transport system activity occurred after the oCP addition. Microbial community analysis of the activated sludge indicates dosing oCP resulted in a decrease of sludge richness and diversity in the SBRs. Hopefully, this study would provide useful information for reducing sludge yield in biological wastewater treatment systems and behaviors of activated sludge in the presence of uncouplers.