Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach

• Published in: Environmental science & technology Vol. 58; no. 29; pp. 12989 - 12999
• Main Authors: Jiang, Jinqi, Xiang, Xiang, Zhou, Qinhao, Zhou, Lichang, Bi, Xinqi, Khanal, Samir Kumar, Wang, Zongping, Chen, Guanghao, Guo, Gang
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 23.07.2024
• Subjects: Anaerobic processes; Biological activity; Biotransformation; Carbon; Carbon - metabolism; Data Science; Denitrification; Ecosystem; Environmental factors; Learning algorithms; Machine Learning; Microorganisms; Nitrogen; Nitrogen - metabolism; Optimization; Parameter identification; Phosphorus; Phosphorus - metabolism; Phosphorus removal; Regression models; Solid suspensions; Sulfate reduction; Sulfur; Sulfur - metabolism; Suspended solids; Waste Disposal, Fluid - methods; Wastewater - chemistry; Wastewater treatment; sulfur conversion; machine learning; optimization and control; biological phosphorus removal; saline wastewater
• ISSN: 0013-936X; 1520-5851; 1520-5851
• DOI: 10.1021/acs.est.4c03160

The denitrifying sulfur (S) conversion-associated enhanced biological phosphorus removal (DS-EBPR) process for treating saline wastewater is characterized by its unique microbial ecology that integrates carbon (C), nitrogen (N), phosphorus (P), and S biotransformation. However, operational instability arises due to the numerous parameters and intricates bacterial interactions. This study introduces a two-stage interpretable machine learning approach to predict S conversion-driven P removal efficiency and optimize DS-EBPR process. Stage one utilized the XGBoost regression model, achieving an R 2 value of 0.948 for predicting sulfate reduction (SR) intensity from anaerobic parameters with feature engineering. Stage two involved the CatBoost classification and regression model integrating anoxic parameters with the predicted SR values for predicting P removal, reaching an accuracy of 94% and an R 2 value of 0.93, respectively. This study identified key environmental factors, including SR intensity (20–45 mg S/L), influent P concentration (<9.0 mg P/L), mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio (0.55–0.72), influent C/S ratio (0.5–1.0), anoxic reaction time (5–6 h), and MLSS concentration (>6.50 g/L). A user-friendly graphic interface was developed to facilitate easier optimization and control. This approach streamlines the determination of optimal conditions for enhancing P removal in the DS-EBPR process.