A novel two-step adaptive multioutput semisupervised soft sensor with applications in wastewater treatment

Liu, Dong; Huang, Daoping; Liu, Yiqi

doi:10.1007/s11356-021-12656-9

Cited by 10 publications

(8 citation statements)

References 37 publications

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“…From formula (4), and formula (5), it can be seen that EC is affected by SO、SNO、MLSS and other variables, while EQ is mainly related to SO、SNO、SS、SNH and other variables (Li et al 2021). Therefore, SO、SNO、MLSS、SNH are selected as model input variables, EC and EQ as output variables, since online measurement of mixed liquor solid suspended concentration (MLSS) is easier than SS.…”

Section: Optimization Objectivesmentioning

confidence: 99%

An effective dynamic immune optimization control for the wastewater treatment process

Wang

2021

Environ Sci Pollut Res

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Aiming at the conflict of multiple performance indicators in the complex wastewater treatment process (WWTPs), an effective optimization control based on dynamic multi-objective immune (DMOIA-OC) is designed. This method first designs a dynamic optimization control scheme. It also divides the control process into a dynamic optimization layer and a tracking control layer. Secondly, based on analyzing the performance of WWTPs, the optimization layer establishes energy consumption and effluent quality models that adapt to the environment. Thirdly, an adaptive dynamic immune optimization algorithm is proposed to optimize complex and conflicting performance indicators. Besides, a suitable preference solution is selected from many Pareto solutions. It is to obtain the best set values of dissolved oxygen and nitrate nitrogen. Finally, the solution will be tested on the benchmark simulation platform (BSM1). The results show that the DMOIA-OC method can solve the complex optimization problem of multiple performance indicators in WWTPs. And it has a competitive advantage in the control effect.

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Section: Optimization Objectivesmentioning

confidence: 99%

An effective dynamic immune optimization control for the wastewater treatment process

Wang

2021

Environ Sci Pollut Res

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“…In particular, when the faulty process data present strong nonlinearity, the linear model of the CD-ICA algorithm cannot theoretically guarantee good fault detection performance. It is envisaged that the detection efficiency for the aforementioned problem can be improved by combining linearizing methods, such as the kernel methods , and support vector machine (SVM) algorithms. , The performances of these variants require further testing and study.…”

Section: Discussionmentioning

confidence: 99%

“…It is envisaged that the detection efficiency for the aforementioned problem can be improved by combining linearizing methods, such as the kernel methods 36,37 and support vector machine (SVM) algorithms. 38,39 The performances of these variants require further testing and study.…”

Section: Discussionmentioning

confidence: 99%

Novel Process Monitoring Approach Enhanced by a Complex Independent Component Analysis Algorithm with Applications for Wastewater Treatment

Huang

Liu

et al. 2021

Ind. Eng. Chem. Res.

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The process monitoring of industries by means of multivariate statistical methods has gained popularity in academic and industrial communities. However, nonlinearity, autocorrelation, and high dimensionality can render traditional approaches inadequate. This necessitates a more powerful method for implementing better process monitoring in reality. Therefore, a novel independent component analysis (ICA) algorithm, termed complex dynamic independent component analysis (CD-ICA), is proposed for information refinement and feature extraction in this paper. This proposed algorithm extracts dominant features from a complex-valued matrix containing raw data information and their changing rates through complex ICA and properly shifting phase operations. The novel fault detection index, based on the three traditional monitoring statistics of the ICA algorithm, is enhanced and applied to monitor real chemical and biological processes efficiently by combining the aforementioned algorithm. The performance of the presented method is assessed based on data from a simple multivariate mathematical simulation and data from a real wastewater treatment plant (WWTP). The results show that this approach provides higher efficiency and performance than traditional approaches.

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Section: Models Parametersmentioning

confidence: 92%

“…Although the main principle of this paper is different from the reference [44], it is very easy to extend this study to the quantify the uncertainty of effluent variables forecasting. Compared with the application of Li et al [45,46], this paper provides a better prediction performance and a simple sequential way to update the parameters online. Thus, this study can be successfully envisioned to be applied to process monitoring of effluent variables in wastewater plants.…”

Section: Models Parametersmentioning

confidence: 99%

Process Monitoring of Quality-Related Variables in Wastewater Treatment Using Kalman-Elman Neural Network-Based Soft-Sensor Modeling

Liu

Yuan

et al. 2021

Water

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Proper monitoring of quality-related but hard-to-measure effluent variables in wastewater plants is imperative. Soft sensors, such as dynamic neural network, are widely used to predict and monitor these variables and then to optimize plant operations. However, the traditional training methods of dynamic neural network may lead to poor local optima and low learning rates, resulting in inaccurate estimations of parameters and deviation of predictions. This study introduces a general Kalman-Elman method to monitor the effluent qualities, such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total nitrogen (TN). The method couples an Elman neural network with the square-root unscented Kalman filter (SR-UKF) to build a soft-sensor model. In the proposed methodology, adaptive noise estimation and weight constraining are introduced to estimate the unknown noise and constrain the parameter values. The main merits of the proposed approach include the following: First, improving the mapping accuracy of the model and overcoming the underprediction phenomena in data-driven process monitoring; second, implementing the parameter constraint and avoid large weight values; and finally, providing a new way to update the parameters online. The proposed method is verified from a dataset of the University of California database (UCI database). The obtained results show that the proposed soft-sensor model achieved better prediction performance with root mean square error (RMSE) being at least 50% better than the Elman network based on back propagation through the time algorithm (Elman-BPTT), Elman network based on momentum gradient descent algorithm (Elman-GDM), and Elman network based on Levenberg-Marquardt algorithm (Elman-LM). This method can give satisfying prediction of quality-related effluent variables with the largest correlation coefficient (R) for approximately 0.85 in output suspended solids (SS-S) and 0.95 in BOD and COD.

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A novel two-step adaptive multioutput semisupervised soft sensor with applications in wastewater treatment

Cited by 10 publications

References 37 publications

An effective dynamic immune optimization control for the wastewater treatment process

An effective dynamic immune optimization control for the wastewater treatment process

Novel Process Monitoring Approach Enhanced by a Complex Independent Component Analysis Algorithm with Applications for Wastewater Treatment

Process Monitoring of Quality-Related Variables in Wastewater Treatment Using Kalman-Elman Neural Network-Based Soft-Sensor Modeling

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