Modeling and simulation of the biological process for treating wastewater with a low COD:N ratio under different dissolved oxygen conditions using an extended ASM2d model

Zhang, Xinhui; Nan, Jun; Liu, Bohan; Xiao, Qiliang

doi:10.1016/j.jwpe.2022.103105

Cited by 3 publications

(1 citation statement)

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“…A key approach for addressing operational instability involves using activated sludge models (ASMs), such as ASM-2d. , The ASM system, which relies on the Monod equations and several implicit parameters, , lacks the flexibility to handle changing environmental factors. Consequently, any shifts in these conditions require recalibration of the model parameters.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang,

Xiang,

Zhou

et al. 2024

Environ. Sci. Technol.

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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.

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