Calibrating activated sludge models through hyperparameter optimization: a new framework for wastewater treatment plant simulation

• Published in: npj clean water Vol. 8; no. 1; pp. 80 - 12
• Main Authors: Yu, Huarong, Wang, Yue, Li, Tan, Gan, Qibo, Qu, Dan, Qu, Fangshu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.08.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 704/172; 704/172/169; Accuracy; Activated sludge; Aquatic Pollution; Automation; Calibration; Denitrification; Earth and Environmental Science; Efficiency; Effluents; Environment; Errors; Genetic algorithms; Libraries; Machine learning; Methods; Nanotechnology; Optimization; Parameters; Pareto optimum; Python; Reproducibility; Sensitivity analysis; Simulation; Waste Water Technology; Wastewater treatment; Wastewater treatment plants; Water Industry/Water Technologies; Water Management; Water Pollution Control; Water Quality/Water Pollution
• ISSN: 2059-7037; 2059-7037
• DOI: 10.1038/s41545-025-00513-y

Traditional calibration of Activated Sludge Models (ASM) is often manual, expert-dependent, and inefficient. This study introduces a hyperparameter optimisation framework using Optuna to automate the calibration of the ASM2d model. Built on Python, the model integrates the Tree-structured Parzen Estimator (TPE) for single-objective and NSGA-II for multi-objective optimisation. A 50-day dataset from a full-scale wastewater treatment plant in Shenzhen, China, validates the approach. Compared to traditional methods, TPE reduced average relative errors for TN and COD from 4.587 and 24.846% to 0.798 and 15.291%, respectively, while decreasing iterations by 15–20%. NSGA-II lowered TN and COD errors to 4.72 and 15.17%, further improving to 0.095% and 8.43% with full-parameter tuning. Calibration efficiency increased by 65–75%. By effectively exploring parameter interdependencies, TPE and NSGA-II enhance calibration robustness and generalisation. This automated optimisation method significantly improves the accuracy and efficiency of ASM calibration, advancing intelligent wastewater process modelling.