Dissolved oxygen generic model control of wastewater treatment process based on immune optimization least squares support vector machine

Ye, Hongtao; Luo, Fei; Xu, Yuge

doi:10.1109/wcica.2010.5554565

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Kernel ICA has been studied by [13] to detect exterior disturbance from rainfall, and has shown competitive accuracy, efficiency, and reliability. Support vector machine (SVM) has been proposed by [14] to improve oxygen uptake rate on-line measurement and control. Kernel methods demanded comparably larger storage during computation, where parallel computing could offer potential solutions.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Influent Conditions of Wastewater Treatment Plants by Nonlinear Data-Based Techniques

et al. 2019

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To operate wastewater treatment plants (WWTPs) with optimized efficiency, influent conditions (ICs) as initial states of inflow fed to WWTPs were monitored to identify potential anomalies that would trigger adverse events or system crash. To employ voluminous measurements for data-driven decisions, the non-linear, non-Gaussian, non-stationary, auto-correlated, cross-correlated, hetero-skedastic, case-specific nature of multivariate environmental datasets must be considered. This research proposed kernel machine learning models, the kernel principal components analysis based one-class support vector machine (KPCA-OCSVM) with various kernels, to learn anomaly-free training set then classify the testing set. A seven-years multivariate ICs time series was introduced with exploratory analysis performed to reveal temporal behaviors and statistical properties. KPCA with polynomial kernels sufficiently output representative features, based on which OCSVM with Gaussian kernels sensitively and specifically identified anomalies in ICs that were previously omitted by WWTP operators. The proposed kernel algorithms surpassed previous linear PCA-based K-nearest-neighbors models, and improved outcomes with limited increase in computation cost. Without requiring linear, Gaussian, stationary, independent, and homo-skedastic qualities from data, the proposed flexible environmental data science approach could be transferred, rebuilt, and tuned conveniently for ICs from different WWTPs.

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Section: Introductionmentioning

confidence: 99%

Monitoring Influent Conditions of Wastewater Treatment Plants by Nonlinear Data-Based Techniques

et al. 2019

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“…The design is based on different approaches, such as partial least squares, multiple linear regression and principal component regression. In [5], [6], [7], [8], [9] a soft sensor for estimating the oxygen-dissolving rate and the respiration of microorganisms is proposed. The two variables are estimated from the dissolved oxygen in the aerobic tank and the air flow.…”

Section: Introductionmentioning

confidence: 99%

“…The two variables are estimated from the dissolved oxygen in the aerobic tank and the air flow. In the papers different algorithms are proposed: modified Luenberg's algorithm [5], generalized damped least squares [6], support vector machine [7] and Kalman filter [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process

Dovžan

Logar

Škrjanc

2015

IEEE Trans. Fuzzy Syst.

134

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Increasing demands on effluent quality and loads call for an improved control, monitoring and fault detection of waste-water treatment plants (WWTPs). Improved control and optimization of WWTP lead to increased pollutant removal, a reduced need for chemicals as well as energy savings. An important step towards the optimal functioning of a WWTP is to minimize the influence of sensor faults on the control quality. To achieve this a fault-detection system should be implemented. In this paper the idea of using an evolving method as a base for the faultdetection/monitoring system is tested. The system is based on the evolving-fuzzy-model method. This method allows us to model the nonlinear relations between the variables with the Takagi-Sugeno fuzzy model. The method uses basic evolving mechanisms to add and remove clusters and the adaptation mechanism to adapt the clusters' and local models' parameters. The proposed faultdetection system is tested on measured data from a real WWTP. The results indicate the potential improvement of the WWTP's control during a sensor malfunction.

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Advances in control technologies for wastewater treatment processes: status, challenges, and perspectives

Iratni¹,

Chang

2019

IEEE/CAA J. Autom. Sinica

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Dissolved oxygen generic model control of wastewater treatment process based on immune optimization least squares support vector machine

Cited by 3 publications

References 4 publications

Monitoring Influent Conditions of Wastewater Treatment Plants by Nonlinear Data-Based Techniques

Monitoring Influent Conditions of Wastewater Treatment Plants by Nonlinear Data-Based Techniques

Implementation of an Evolving Fuzzy Model (eFuMo) in a Monitoring System for a Waste-Water Treatment Process

Advances in control technologies for wastewater treatment processes: status, challenges, and perspectives

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