Machine learning-assisted prediction and identification of key factors affecting nitrogen metabolism for aerobic granular sludge.

Journal: Environmental research
PMID: 39978627

Abstract

To achieve higher denitrification efficiency with reduced energy consumption in aerobic granular sludge (AGS) system, a systematic evaluation of the carbon and nitrogen metabolism process for AGS under different stage is essential. Herein, this study established the prediction models via interpretable machine learning (ML) for simulating the nitrogen metabolism by using 312 sets of data collected from four reactors with different kinds of AGS. The results indicated Gradient Boosting Decision Trees (GBDT) achieved R values of 0.729, 0.875, and 0.807, respectively by selecting water temperature, carbon source components and particle size as input factors and NH-N, NO-N, and NO-N as prediction targets. Furthermore, Shapley Additive Explanations (SHAP) analysis was used to make global and local interpretations of the GBDT models. The global explanation revealed that particle size of AGS and carbon source of component 4 (C4) with the Ex/Em at 225/335 nm significantly influenced denitrification process. And local analysis results proved that enhanced nitrogen removal performance is attainable when the DX50 and DX10 range between 500-600 and 200-500 μm and the F value of C4 exceeds 0.2 R.U. These findings provide an effective tool for evaluating nitrogen removal performance and identifying the key factors influencing nitrogen metabolism in AGS systems.

Authors

  • Huiping Li
    Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.
  • Li Xie
    Department of Pharmacy The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Baiqin Zhou
    Gansu Academy of Eco-environmental Sciences, Lanzhou, 730030, China; School of Municipal and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China. Electronic address: ziegler.zhou@foxmail.com.
  • Mengxian Hu
    Shanghai Research Institute for Intelligence Autonomous Systems, Tongji University, 1239 Siping Road, Shanghai, 200092, China.
  • Yingying He
    Research Center for Innovative Technology of Pharmaceutical Analysis, College of Pharmacy, Harbin Medical University, Heilongjiang, 150081, PR China.
  • Runyao Huang
    Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.
  • Haosheng Yang
    Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.
  • Kailin Liu
    Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.
  • Jianhua Yuan
    Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.
  • Dianhai Yang
    Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China. Electronic address: yangdianhai@tongji.edu.cn.
  • Weihai Pang
    Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China. Electronic address: pangweihai@tongji.edu.cn.

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