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Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler

Author

Listed:
  • Palash Sarkar

    (Department of Biotechnology and Chemical Technology, School of Chemical Engineering, Aalto University, 00076 Aalto, Finland)

  • Jukka Kortela

    (Department of Biotechnology and Chemical Technology, School of Chemical Engineering, Aalto University, 00076 Aalto, Finland)

  • Alexandre Boriouchkine

    (Department of Biotechnology and Chemical Technology, School of Chemical Engineering, Aalto University, 00076 Aalto, Finland)

  • Elena Zattoni

    (Department of Electrical, Electronic and Information Engineering “G. Marconi”, Alma Mater Studiorum · University of Bologna, 40136 Bologna, Italy)

  • Sirkka-Liisa Jämsä-Jounela

    (Department of Biotechnology and Chemical Technology, School of Chemical Engineering, Aalto University, 00076 Aalto, Finland)

Abstract

This paper presents a novel, effective method to handle critical sensor faults affecting a control system devised to operate a biomass boiler. In particular, the proposed method consists of integrating a data reconciliation algorithm in a model predictive control loop, so as to annihilate the effects of faults occurring in the sensor of the flue gas oxygen concentration, by feeding the controller with the reconciled measurements. Indeed, the oxygen content in flue gas is a key variable in control of biomass boilers due its close connections with both combustion efficiency and polluting emissions. The main benefit of including the data reconciliation algorithm in the loop, as a fault tolerant component, with respect to applying standard fault tolerant methods, is that controller reconfiguration is not required anymore, since the original controller operates on the restored, reliable data. The integrated data reconciliation–model predictive control (MPC) strategy has been validated by running simulations on a specific type of biomass boiler—the KPA Unicon BioGrate boiler.

Suggested Citation

  • Palash Sarkar & Jukka Kortela & Alexandre Boriouchkine & Elena Zattoni & Sirkka-Liisa Jämsä-Jounela, 2017. "Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler," Energies, MDPI, vol. 10(2), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:194-:d:89777
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    References listed on IDEAS

    as
    1. Kortela, J. & Jämsä-Jounela, S.-L., 2014. "Model predictive control utilizing fuel and moisture soft-sensors for the BioPower 5 combined heat and power (CHP) plant," Applied Energy, Elsevier, vol. 131(C), pages 189-200.
    2. Wang, Wei & Liu, Jizhen & Zeng, Deliang & Niu, Yuguang & Cui, Can, 2015. "An improved coordinated control strategy for boiler-turbine units supplemented by cold source flow adjustment," Energy, Elsevier, vol. 88(C), pages 927-934.
    3. Aslani, Alireza & Helo, Petri & Naaranoja, Marja, 2014. "Role of renewable energy policies in energy dependency in Finland: System dynamics approach," Applied Energy, Elsevier, vol. 113(C), pages 758-765.
    4. Wang, Wei & Zeng, Deliang & Liu, Jizhen & Niu, Yuguang & Cui, Can, 2014. "Feasibility analysis of changing turbine load in power plants using continuous condenser pressure adjustment," Energy, Elsevier, vol. 64(C), pages 533-540.
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    Cited by:

    1. Michel Noussan & Benedetto Nastasi, 2018. "Data Analysis of Heating Systems for Buildings—A Tool for Energy Planning, Policies and Systems Simulation," Energies, MDPI, vol. 11(1), pages 1-15, January.
    2. Michał Kozioł & Joachim Kozioł, 2021. "Application of Data Validation and Reconciliation to Improve Measurement Results in the Determination Process of Emission Characteristics in Co-Combustion of Sewage Sludge with Coal," Sustainability, MDPI, vol. 13(9), pages 1-19, May.

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