Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification.

Journal: Bioresource technology
PMID: 39522619

Abstract

Interpretable causal machine learning (ICML) was used to predict the performance of denitrification and clarify the relationships between influencing factors and denitrification. Multiple models were examined, and XG-Boost model provided the best prediction (R = 0.8743). Based on the ICML framework, hydraulic retention time (HRT), mixture chemical oxygen demand/total nitrogen (COD/TN = C/N), mixture COD concentration, and pretreatment technology were identified as important features affecting the denitrification performance. Further, tapping point and partial dependence analyses provided the range of key factors that precisely regulate denitrification. In the application analysis, HRT (6-10.5 h), mixture C/N (6-12), and mixture COD concentration (300-600 mg L) were the appropriate operating ranges, achieving TN removal of approximately 73 %-77 %. The effluent TN and COD concentrations met the discharge standards for wastewater in China (class 1A) and EU. These findings provide support for regulating excess sludge as internal carbon source to promote denitrification.

Authors

  • Shiqi Liu
    School of Energy & Environmental Engineering, Hebei University of Technology, Tianjin 300401, China.
  • Zeqing Long
    Department of Public Health and Preventive Medicine, Changzhi Medical College, Changzhi 046000, China; Shanxi Higher Education Institutions of Science and Technology Innovation Plan Platform, Laboratory of Environmental Factors and Population Health, Changzhi 046000, China; The Key Laboratory of Environmental Pathogenic Mechanisms and Prevention of Chronic Diseases at Changzhi Medical College, Changzhi 046000, China.
  • Jinsong Liang
    School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China.
  • Jie Zhang
    College of Physical Education and Health, Linyi University, Linyi, Shandong, China.
  • Duofei Hu
    School of Energy & Environmental Engineering, Hebei University of Technology, Tianjin 300401, China.
  • Pengfei Hou
    School of Computer Engineering, Jiangsu Ocean University, China.
  • Guangming Zhang
    School of Environment and Natural Resource, Renmin University of China, Beijing 100872, China. Electronic address: zgm@ruc.edu.cn.

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