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

Water Research 2022 99 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 60 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Bing‐Jie Ni Bing‐Jie Ni Jingyang Luo, Runze Xu, Jiashun Cao, Bing‐Jie Ni Jiashun Cao, Bing‐Jie Ni Jingyang Luo, Jingyang Luo, Bing‐Jie Ni Ye Tian, Runze Xu, Runze Xu, Runze Xu, Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Suna Wang, Fang Fang, Bing‐Jie Ni Bing‐Jie Ni Fang Fang, Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Jingyang Luo, Bing‐Jie Ni Jingyang Luo, Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Jingyang Luo, Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Fang Fang, Bing‐Jie Ni Jiashun Cao, Bing‐Jie Ni Jiashun Cao, Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni Bing‐Jie Ni

Summary

Researchers used automated machine learning to predict how microplastics in wastewater sludge affect methane production during anaerobic digestion. The study found that the type of microplastic mattered more than its size or concentration, with polystyrene associated with higher methane output while larger particles generally inhibited production. This approach offers a new data-driven tool for understanding how plastic contamination in sewage affects waste treatment processes.

Polymers

Body Systems

Nervous

Study Type Environmental

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.

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