An integrated machine learning framework for effluent quality prediction in Sewage Treatment Units

IF 1.6 3区环境科学与生态学 Q3 WATER RESOURCES Urban Water Journal Pub Date : 2023-03-08 DOI:10.1080/1573062X.2023.2183137

Chandra Sainadh Srungavarapu, A. G. Sheik, E. Tejaswini, Sheik Mohammed Yousuf, S. R. Ambati

引用次数: 0

Abstract

ABSTRACT The influent and effluent data from wastewater treatment plants being highly correlated with multi-variable coupling and time-varying features may degrade the performance of conventional soft sensors over time. Adaptive strategies based on just-in-time learning (JIT), moving windows (MW), and time difference (TD) are used in this work to develop an adaptive soft sensor. Multi-output Gaussian-process regression (MGPR) is selected and hybrid methods such as TD JIT, MW TD, and JIT MW TD along with TD and MGPR methods are implemented. Data from the benchmark simulation model No.1, closed-loop architecture after applying PI controller, and real-time data from the Rithala Plant of Delhi are obtained. The improved error percentage is 15.03% for total phosphorus (open-loop) using the JIT TD method when compared with the MW TD method. Fair results are observed with JIT TD on real time data with a strong correlation between predicted and observed values, above 0.8 for any variable being estimated.

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污水处理装置出水水质预测的集成机器学习框架

随着时间的推移，污水处理厂的进水和出水数据与多变量耦合和时变特征高度相关，可能会降低传统软传感器的性能。本文采用基于即时学习(JIT)、移动窗口(MW)和时差(TD)的自适应策略来开发自适应软传感器。选择多输出高斯过程回归(MGPR)，实现了TD JIT、MW TD、JIT MW TD以及TD和MGPR方法的混合方法。得到了基准仿真模型1、应用PI控制器后的闭环体系结构和德里Rithala电厂的实时数据。与MW法相比，JIT法测定总磷(开环)的误差提高了15.03%。在实时数据上使用JIT TD可以观察到良好的结果，预测值和实测值之间具有很强的相关性，估计的任何变量都在0.8以上。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Urban Water Journal WATER RESOURCES-

CiteScore

4.40

自引率

11.10%

发文量

101

审稿时长

3 months

期刊介绍： Urban Water Journal provides a forum for the research and professional communities dealing with water systems in the urban environment, directly contributing to the furtherance of sustainable development. Particular emphasis is placed on the analysis of interrelationships and interactions between the individual water systems, urban water bodies and the wider environment. The Journal encourages the adoption of an integrated approach, and system''s thinking to solve the numerous problems associated with sustainable urban water management. Urban Water Journal focuses on the water-related infrastructure in the city: namely potable water supply, treatment and distribution; wastewater collection, treatment and management, and environmental return; storm drainage and urban flood management. Specific topics of interest include: network design, optimisation, management, operation and rehabilitation; novel treatment processes for water and wastewater, resource recovery, treatment plant design and optimisation as well as treatment plants as part of the integrated urban water system; demand management and water efficiency, water recycling and source control; stormwater management, urban flood risk quantification and management; monitoring, utilisation and management of urban water bodies including groundwater; water-sensitive planning and design (including analysis of interactions of the urban water cycle with city planning and green infrastructure); resilience of the urban water system, long term scenarios to manage uncertainty, system stress testing; data needs, smart metering and sensors, advanced data analytics for knowledge discovery, quantification and management of uncertainty, smart technologies for urban water systems; decision-support and informatic tools;...