Sensor-fault tolerance in a wastewater treatment plant by means of ANFIS-based soft sensor and control reconfiguration
This work presents a sensor-fault-tolerant design applied to a decentralized dissolved oxygen control in an activated sludge process subject to sensor faults such as bias and slow drifts. The core idea is to use a data-driven soft sensor based on adaptive neuro-fuzzy inference system to act as a bac...
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| Published in | Neural computing & applications Vol. 30; no. 10; pp. 3265 - 3276 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.11.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0941-0643 1433-3058 |
| DOI | 10.1007/s00521-017-2901-3 |
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| Abstract | This work presents a sensor-fault-tolerant design applied to a decentralized dissolved oxygen control in an activated sludge process subject to sensor faults such as bias and slow drifts. The core idea is to use a data-driven soft sensor based on adaptive neuro-fuzzy inference system to act as a backup of the joint sensor and controller block, and to exploit the data/analytical correlations existing in the system. After fault detection and isolation, a control reconfiguration technique takes action in order to surmount/counteract the effect of the fault until the faulty sensor is repaired. The approach presented here was applied to the Benchmark Simulation Model n.1 and was able to demonstrate the improvements on the control system dependability. |
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| AbstractList | This work presents a sensor-fault-tolerant design applied to a decentralized dissolved oxygen control in an activated sludge process subject to sensor faults such as bias and slow drifts. The core idea is to use a data-driven soft sensor based on adaptive neuro-fuzzy inference system to act as a backup of the joint sensor and controller block, and to exploit the data/analytical correlations existing in the system. After fault detection and isolation, a control reconfiguration technique takes action in order to surmount/counteract the effect of the fault until the faulty sensor is repaired. The approach presented here was applied to the Benchmark Simulation Model n.1 and was able to demonstrate the improvements on the control system dependability. |
| Author | Belchior, Carlos Alberto C. Araújo, Rui Alexandre M. Landeck, Jorge Afonso C. Souza, Francisco Alexandre A. |
| Author_xml | – sequence: 1 givenname: Carlos Alberto C. surname: Belchior fullname: Belchior, Carlos Alberto C. email: cabelchior@isr.uc.pt organization: Department of Electrical and Computer Engineering, ISR - Institute for Systems and Robotics, University of Coimbra – sequence: 2 givenname: Rui Alexandre M. surname: Araújo fullname: Araújo, Rui Alexandre M. organization: Department of Electrical and Computer Engineering, ISR - Institute for Systems and Robotics, University of Coimbra – sequence: 3 givenname: Francisco Alexandre A. surname: Souza fullname: Souza, Francisco Alexandre A. organization: Department of Electrical and Computer Engineering, ISR - Institute for Systems and Robotics, University of Coimbra – sequence: 4 givenname: Jorge Afonso C. surname: Landeck fullname: Landeck, Jorge Afonso C. organization: Instrumentation Center, Department of Physics, University of Coimbra |
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| Keywords | Data-driven soft sensor Decentralized dissolved oxygen control Activated sludge process ANFIS Sensor-fault-tolerant control |
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| References | HongguiHYingLJunfeiQA fuzzy neural network approach for online fault detection in waste water treatment processComput Electr Eng20144072216222610.1016/j.compeleceng.2014.08.011 WangLXMendelJMFuzzy basis functions, universal approximation, and orthogonal least-squares learningIEEE Trans Neural Netw19923580781410.1109/72.159070 ErtuncHMOcakHAliustaogluCANN- and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faultsNeural Comput Appl201322143544610.1007/s00521-012-0912-7 LinMJLuoFAn adaptive control method for the dissolved oxygen concentration in wastewater treatment plantsNeural Comput Appl20152682027203710.1007/s00521-015-1858-3 FortunaLGrazianiSXibiliaMSoft sensors for product quality monitoring in debutanizer distillation columnsControl Eng Pract200513449950810.1016/j.conengprac.2004.04.013 YagerRRFilevDPGeneration of fuzzy rules by mountain clusteringJ Intell Fuzzy Syst199423209219 ZumoffenDBasualdoMImprovements in fault tolerance characteristics for large chemical plants: 1. Waste water treatment plant with decentralized controlInd Eng Chem Res200847155464548110.1021/ie800098t Niemann H (2010) A model-based approach for fault-tolerant control. In: 2010 conference on control and fault-tolerant systems (SysTol), pp 481–492. doi:10.1109/SYSTOL.2010.5675947 PostalcıoğluSErkanKSoft computing and signal processing based active fault tolerant control for benchmark processNeural Comput Appl2009181778510.1007/s00521-007-0159-x JangJSRANFIS: adaptive-network-based fuzzy inference systemIEEE Trans Syst Man Cybern199323366568510.1109/21.256541 HuangMMaYWanJChenXA sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment processAppl Soft Comput20152711010.1016/j.asoc.2014.10.034 HenzeMGradyCJrGujerWMaraisGvRMatsuoTHenzeMGujerWMinoTvan LoosdrechtMActivated sludge model no. 1Activated sludge models ASM1, ASM2, ASM2d and ASM32000LondonIWA Publishing137 ShakerMSPattonRJActive sensor fault tolerant output feedback tracking control for wind turbine systems via T–S modelEng Appl Artif Intell20143411210.1016/j.engappai.2014.04.005 TanCPHabibMKTolerance towards sensor faults: an application to a flexible arm manipulatorInt J Adv Robot Syst20063434335010.5772/5720 LiuJChenDSFault isolation using modified contribution plotsComput Chem Eng2014611191910.1016/j.compchemeng.2013.10.004 Bolles S (2006) Modeling wastewater aeration systems to discover energy savings opportunities. Process Energy Services LLC Copp JB (2002) The COST simulation benchmark: description and simulator manual. Directions in development (Washington): Environment, Directorate-General for Research WimbergerDVerdeCFault diagnosticability for an aerobic batch wastewater treatment processControl Eng Pract200816111344135310.1016/j.conengprac.2008.03.002 JeppssonUPonsMNThe cost benchmark simulation model-current state and future perspectiveControl Eng Pract200412329930410.1016/j.conengprac.2003.07.001 KhalajGKhalajMJApplication of ANFIS for modeling of layer thickness of chromium carbonitride coatingNeural Comput Appl201424368569410.1007/s00521-012-1290-x AouaoudaSChadliMKhadirMTBouararTRobust fault tolerant tracking controller design for unknown inputs T–S models with unmeasurable premise variablesJ Process Control201222586187210.1016/j.jprocont.2012.02.016 Alex J, Benedetti L, Copp J, Gernaey KV, Jeppsson U, Nopens I, Pons MN, Rieger L, Rosen C, Steyer JP, Vanrolleghem P, Winkler S (2008) Benchmark simulation model no. 1 (bsm1). Technical Report LTH-IEA-7229. 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| References_xml | – reference: LiuJChenDSFault isolation using modified contribution plotsComput Chem Eng2014611191910.1016/j.compchemeng.2013.10.004 – reference: Bolles S (2006) Modeling wastewater aeration systems to discover energy savings opportunities. Process Energy Services LLC – reference: Nagy-KissAMSchutzGEstimation and diagnosis using multi-models with application to a wastewater treatment plantJ Process Control201323101528154410.1016/j.jprocont.2013.09.027 – reference: SouzaFAAraújoRMendesJReview of soft sensor methods for regression applicationsChemom Intell Lab Syst2016152697910.1016/j.chemolab.2015.12.011 – reference: HongguiHYingLJunfeiQA fuzzy neural network approach for online fault detection in waste water treatment processComput Electr Eng20144072216222610.1016/j.compeleceng.2014.08.011 – reference: AouaoudaSChadliMKhadirMTBouararTRobust fault tolerant tracking controller design for unknown inputs T–S models with unmeasurable premise variablesJ Process Control201222586187210.1016/j.jprocont.2012.02.016 – reference: JeppssonUPonsMNThe cost benchmark simulation model-current state and future perspectiveControl Eng Pract200412329930410.1016/j.conengprac.2003.07.001 – reference: YagerRRFilevDPGeneration of fuzzy rules by mountain clusteringJ Intell Fuzzy Syst199423209219 – reference: JangJSRANFIS: adaptive-network-based fuzzy inference systemIEEE Trans Syst Man Cybern199323366568510.1109/21.256541 – reference: Hamdan H (2003) An exploration of the adaptive neuro-fuzzy inference system (ANFIS) for modelling survival, Ph.D. thesis. The University of Nottingham – reference: GilPSantosFPalmaLCardosoARecursive subspace system identification for parametric fault detection in nonlinear systemsAppl Soft Comput20153744445510.1016/j.asoc.2015.08.036 – reference: FortunaLGrazianiSXibiliaMSoft sensors for product quality monitoring in debutanizer distillation columnsControl Eng Pract200513449950810.1016/j.conengprac.2004.04.013 – reference: ShakerMSPattonRJActive sensor fault tolerant output feedback tracking control for wind turbine systems via T–S modelEng Appl Artif Intell20143411210.1016/j.engappai.2014.04.005 – reference: WimbergerDVerdeCFault diagnosticability for an aerobic batch wastewater treatment processControl Eng Pract200816111344135310.1016/j.conengprac.2008.03.002 – reference: ZumoffenDBasualdoMImprovements in fault tolerance characteristics for large chemical plants: 1. Waste water treatment plant with decentralized controlInd Eng Chem Res200847155464548110.1021/ie800098t – reference: HuangMMaYWanJChenXA sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment processAppl Soft Comput20152711010.1016/j.asoc.2014.10.034 – reference: TanCPHabibMKTolerance towards sensor faults: an application to a flexible arm manipulatorInt J Adv Robot Syst20063434335010.5772/5720 – reference: WangLXMendelJMFuzzy basis functions, universal approximation, and orthogonal least-squares learningIEEE Trans Neural Netw19923580781410.1109/72.159070 – reference: Alex J, Benedetti L, Copp J, Gernaey KV, Jeppsson U, Nopens I, Pons MN, Rieger L, Rosen C, Steyer JP, Vanrolleghem P, Winkler S (2008) Benchmark simulation model no. 1 (bsm1). Technical Report LTH-IEA-7229. 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| SubjectTerms | Activated sludge process Adaptive systems Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer simulation Correlation analysis Data Mining and Knowledge Discovery Fault detection Fault tolerance Fuzzy logic Fuzzy systems Image Processing and Computer Vision Original Article Probability and Statistics in Computer Science Reconfiguration Sensors Wastewater treatment |
| Title | Sensor-fault tolerance in a wastewater treatment plant by means of ANFIS-based soft sensor and control reconfiguration |
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