Predictive monitoring of wastewater treatment performance: Seasonal microbial activity and data-informed water quality model

IF 6.7 2区工程技术 Q1 ENGINEERING, CHEMICAL

Journal of water process engineering Pub Date : 2025-04-19 DOI:10.1016/j.jwpe.2025.107718

HeeJu Song , TaeYong Woo , SangYoun Kim , ChanHyeok Jeong , MinHan Kim , SungKu Heo , ChangKyoo Yoo

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Abstract

Biological treatment processes in wastewater treatment plants (WWTPs) require effective monitoring systems to rapidly detect process anomalies and identify causes. Traditional statistical approaches struggle with capturing dynamic microorganism characteristics influenced by seasonal variations and fluctuating conditions. To address this, a predictive and adaptive water quality monitoring system was developed, integrating an adaptive quality monitoring chart (AQUA) with a water quality auto-regressive variational mode enhanced model (WAVE). The WAVE model combines partial least-squares regression, autoregression, and variational mode decomposition to capture temporal and seasonal dynamics in microbial activity. Data from a full-scale M-city WWTP were analyzed to identify significant features and seasonal patterns. The WAVE model showed high prediction performances for chemical oxygen demand (COD) and total nitrogen (TN) removal efficiencies with root mean square error (RMSE) values of 0.45 and 1.12, respectively, while the AQUA chart detected abnormal changes in microbial activity. This system effectively accounts for seasonal fluctuations resulting from microbial activity's variations, reduces false alarm rates, and enhances process monitoring, contributing to stable effluent water quality and optimized biological treatment processes in full-scale WWTPs.

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废水处理性能的预测监测：季节性微生物活动和数据知情的水质模型

污水处理厂（WWTPs）的生物处理过程需要有效的监控系统来快速检测过程异常并找出原因。传统的统计方法难以捕捉受季节变化和波动条件影响的动态微生物特征。为解决这一问题，我们开发了一种预测性和自适应水质监测系统，将自适应水质监测图 (AQUA) 与水质自动回归变异模式增强模型 (WAVE) 相结合。WAVE 模型结合了偏最小二乘回归、自回归和变异模式分解，以捕捉微生物活动的时间和季节动态。对来自 M 市污水处理厂的完整数据进行了分析，以确定重要特征和季节模式。WAVE 模型对化学需氧量（COD）和总氮（TN）的去除率有较高的预测性能，均方根误差（RMSE）值分别为 0.45 和 1.12，而 AQUA 图表则能检测到微生物活动的异常变化。该系统有效地考虑了微生物活性变化引起的季节性波动，降低了误报率，并加强了工艺监测，有助于稳定出水水质和优化全规模污水处理厂的生物处理工艺。

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来源期刊

Journal of water process engineering Biochemistry, Genetics and Molecular Biology-Biotechnology

CiteScore

10.70

自引率

8.60%

发文量

846

审稿时长

24 days

期刊介绍： The Journal of Water Process Engineering aims to publish refereed, high-quality research papers with significant novelty and impact in all areas of the engineering of water and wastewater processing . Papers on advanced and novel treatment processes and technologies are particularly welcome. The Journal considers papers in areas such as nanotechnology and biotechnology applications in water, novel oxidation and separation processes, membrane processes (except those for desalination) , catalytic processes for the removal of water contaminants, sustainable processes, water reuse and recycling, water use and wastewater minimization, integrated/hybrid technology, process modeling of water treatment and novel treatment processes. Submissions on the subject of adsorbents, including standard measurements of adsorption kinetics and equilibrium will only be considered if there is a genuine case for novelty and contribution, for example highly novel, sustainable adsorbents and their use: papers on activated carbon-type materials derived from natural matter, or surfactant-modified clays and related minerals, would not fulfil this criterion. The Journal particularly welcomes contributions involving environmentally, economically and socially sustainable technology for water treatment, including those which are energy-efficient, with minimal or no chemical consumption, and capable of water recycling and reuse that minimizes the direct disposal of wastewater to the aquatic environment. Papers that describe novel ideas for solving issues related to water quality and availability are also welcome, as are those that show the transfer of techniques from other disciplines. The Journal will consider papers dealing with processes for various water matrices including drinking water (except desalination), domestic, urban and industrial wastewaters, in addition to their residues. It is expected that the journal will be of particular relevance to chemical and process engineers working in the field. The Journal welcomes Full Text papers, Short Communications, State-of-the-Art Reviews and Letters to Editors and Case Studies