Modeling of Wastewater Treatment Processes Using Dynamic Bayesian Networks Based on Fuzzy PLS

The complicated characteristics of wastewater treatment plants (WWTPs) significantly hinder the monitoring of industrial processes, and thus much attention has been paid to process modeling and prediction. A fuzzy partial least squares-based dynamic Bayesian networks (FPLS-DBN) is proposed to improve the modeling ability in WWTPs. To adapt the nonlinear process data, fuzzy partial least squares (FPLS) is introduced by using a fuzzy system to extract nonlinear features from process data. In addition, a dynamic extension is included by embedding augmented matrices into Bayesian networks to fit the uncertainty and time-varying characteristics. Regarding the quality indices for effluent suspended solid in the WWTP, the root mean square error of the FPLS-DBN model is decreased by 28.63% and 69.47%, respectively, in comparison with that for partial least squares and Bayesian networks. The results demonstrate the superiority of FPLS-DBN in modeling performance for an actual industrial WWTP application.

[1]  You Lv,et al.  Nonlinear PLS Integrated with Error-Based LSSVM and Its Application to NOX Modeling , 2012 .

[2]  ChangKyoo Yoo,et al.  Statistical monitoring of dynamic processes based on dynamic independent component analysis , 2004 .

[3]  Zonghai Sun,et al.  Statistical Monitoring of Wastewater Treatment Plants Using Variational Bayesian PCA , 2014 .

[4]  Biao Huang,et al.  Design of inferential sensors in the process industry: A review of Bayesian methods , 2013 .

[5]  Mingzhi Huang,et al.  Multivariate statistical monitoring of subway indoor air quality using dynamic concurrent partial least squares , 2019, Environmental Science and Pollution Research.

[6]  Min Xie,et al.  A Real-Time Fault Diagnosis Methodology of Complex Systems Using Object-Oriented Bayesian Networks , 2016, Bayesian Networks in Fault Diagnosis.

[7]  ChangKyoo Yoo,et al.  A fuzzy neural network-based soft sensor for modeling nutrient removal mechanism in a full-scale wastewater treatment system , 2013 .

[8]  Qiang Liu,et al.  Dynamic concurrent kernel CCA for strip-thickness relevant fault diagnosis of continuous annealing processes , 2017, Journal of Process Control.

[9]  Si-Zhao Joe Qin,et al.  Dynamic latent variable analytics for process operations and control , 2017, Comput. Chem. Eng..

[10]  Longbing Cao,et al.  Minimax Probability TSK Fuzzy System Classifier: A More Transparent and Highly Interpretable Classification Model , 2015, IEEE Transactions on Fuzzy Systems.

[11]  Francesco Corona,et al.  Data-derived soft-sensors for biological wastewater treatment plants: An overview , 2013, Environ. Model. Softw..

[12]  Zhiqiang Ge,et al.  Weighted Linear Dynamic System for Feature Representation and Soft Sensor Application in Nonlinear Dynamic Industrial Processes , 2018, IEEE Transactions on Industrial Electronics.

[13]  Peter J. Schmid,et al.  Recursive dynamic mode decomposition of transient and post-transient wake flows , 2016, Journal of Fluid Mechanics.

[14]  Jie Yu,et al.  Identification of probabilistic graphical network model for root-cause diagnosis in industrial processes , 2014, Comput. Chem. Eng..

[15]  Zhiqiang Ge,et al.  Adaptive soft sensors for quality prediction under the framework of Bayesian network , 2018 .

[16]  Yu Tian,et al.  A Framework and Modeling Method of Data-Driven Soft Sensors Based on Semisupervised Gaussian Regression , 2016 .

[17]  Faisal Khan,et al.  Improved latent variable models for nonlinear and dynamic process monitoring , 2017 .

[18]  Bengt Carlsson,et al.  Dynamic Nonlinear Partial Least Squares Modeling Using Gaussian Process Regression , 2019, Industrial & Engineering Chemistry Research.

[19]  Dexian Huang,et al.  Data-driven soft sensor development based on deep learning technique , 2014 .

[20]  K. Funatsu,et al.  Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors , 2013 .

[21]  Chin-Teng Lin,et al.  A TSK-Type-Based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications , 2014, IEEE Transactions on Industrial Electronics.

[22]  Haizhen Yang,et al.  Prediction analysis of a wastewater treatment system using a Bayesian network , 2013, Environ. Model. Softw..

[23]  S. Wold,et al.  Nonlinear PLS modeling , 1989 .

[24]  Christos Georgakis,et al.  Disturbance detection and isolation by dynamic principal component analysis , 1995 .

[25]  Paolo Gastaldo,et al.  Bayesian network based extreme learning machine for subjectivity detection , 2017, J. Frankl. Inst..

[26]  ChangKyoo Yoo,et al.  Soft sensor modeling of industrial process data using kernel latent variables-based relevance vector machine , 2020, Appl. Soft Comput..

[27]  Marios K. Chryssanthopoulos,et al.  Bridge condition modelling and prediction using dynamic Bayesian belief networks , 2015 .

[28]  Dan Li,et al.  Bayesian Network Based Approach for Diagnosis of Modified Sequencing Batch Reactor , 2019 .

[29]  Min Woo Lee,et al.  Robust Adaptive Partial Least Squares Modeling of a Full-Scale Industrial Wastewater Treatment Process , 2007 .

[30]  C. Yoo,et al.  Nonlinear PLS modeling with fuzzy inference system , 2002 .