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

IF 11.4 1区 工程技术 Q1 ENGINEERING, CHEMICAL
Huarong Yu, Yue Wang, Tan Li, Qibo Gan, Dan Qu, Fangshu Qu
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Abstract

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.

Abstract Image

通过超参数优化校准活性污泥模型:污水处理厂模拟的新框架
活性污泥模型(ASM)的传统校准通常是手动的,依赖于专家,效率低下。本研究介绍了一个使用Optuna的超参数优化框架,以自动校准ASM2d模型。该模型基于Python,集成了用于单目标优化的树结构Parzen Estimator (TPE)和用于多目标优化的NSGA-II。来自中国深圳一家全规模污水处理厂的50天数据验证了该方法。与传统方法相比,TPE将TN和COD的平均相对误差分别从4.587和24.846%降低到0.798和15.291%,迭代次数减少了15-20%。NSGA-II将TN和COD误差分别降低到4.72和15.17%,全参数调优后分别提高到0.095%和8.43%。校正效率提高65-75%。通过有效地探索参数的相互依赖性,TPE和NSGA-II增强了校准的鲁棒性和泛化性。这种自动化优化方法显著提高了ASM校准的准确性和效率,推进了智能废水处理建模。
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来源期刊
npj Clean Water
npj Clean Water Environmental Science-Water Science and Technology
CiteScore
15.30
自引率
2.60%
发文量
61
审稿时长
5 weeks
期刊介绍: npj Clean Water publishes high-quality papers that report cutting-edge science, technology, applications, policies, and societal issues contributing to a more sustainable supply of clean water. The journal's publications may also support and accelerate the achievement of Sustainable Development Goal 6, which focuses on clean water and sanitation.
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