Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China.
Centre for Technology in Water and Wastewater (CTWW), School of Civil and Environmental Engineering, University of Technology Sydney (UTS), Sydney, NSW 2007, Australia.
Water Res. 2022 Sep 1;223:118975. doi: 10.1016/j.watres.2022.118975. Epub 2022 Aug 14.
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.
微塑料作为新兴污染物，在生物废水处理过程中会大量积聚在废活性污泥（WAS）中，这对随后的甲烷生产厌氧污泥消化产生了明显不同的影响。然而，对于预测和揭示 WAS 中积累的微塑料对甲烷生产的复杂影响的稳健建模方法仍然缺失。在本研究中，应用了四种自动化机器学习（AutoML）方法来模拟微塑料对厌氧消化过程的影响，并探索了可解释性分析，以揭示关键变量（例如微塑料的浓度、类型和尺寸）与甲烷生产之间的关系。结果表明，梯度提升机具有更好的预测性能（均方误差（MSE）= 17.0），优于常见的神经网络模型（MSE = 58.0），这表明 AutoML 算法成功地预测了甲烷生产，并且可以在没有人为干预的情况下选择最佳的机器学习模型。可解释性分析结果表明，微塑料类型的变量比微塑料直径和浓度的变量更为重要。聚苯乙烯的存在与更高的甲烷产量相关，而增加微塑料直径和浓度都会抑制甲烷产量。这项工作还为全面了解微塑料对甲烷生产的复杂影响提供了一种新的建模方法，揭示了甲烷产量与关键变量之间的依赖关系，可为优化厌氧消化过程中的操作调整提供参考。
