- Automated machine learning-based prediction of microplastics induced . . .
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
- Machine learning-based analysis of microplastic-induced changes in . . .
This study applied four machine learning algorithms to predict methane yield using two datasets-one with and one without MPs Among these, gradient boosting regression demonstrated the highest prediction accuracy, with testing R 2 values of 0 996 for systems without MP pollution and 0 998 with MP pollution
- Automated machine learning-based prediction of microplastics induced . . .
In this study, four automated machine learning (AutoML) approach was applied to model the effects of microplastics on anaerobic digestion processes, and integrated explainable
- Microplastics and nanoplastics in urban waters - ScienceDirect
Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion Run-Ze Xu, Jia-Shun Cao, Tian Ye, Su-Na Wang,
- Hohai University Reports Findings in Machine Learning (Automated . . .
According to news reporting out of Nanjing, People's Republic of China, by NewsRx editors, research stated, "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
- Automated machine learning-based prediction of microplastics induced . . .
Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion 基于自动机器学习的微塑料对厌氧消化甲烷产生影响的预测
- Machine learning-based analysis of microplastic-induced changes in . . .
This study applied four machine learning algorithms to predict methane yield using two datasets—one with and one without MPs Among these, gradient boosting regression demonstrated the highest prediction accuracy, with testing R2 values of 0 996 for systems without MP pollution and 0 998 with MP pollution
- 基于自动机器学习的微塑料预测对厌氧消化中甲烷产生的影响,Water Research - X-MOL
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
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