Fault tolerant model predictive control (FTMPC) of the BioGrate boiler

    Research output: ThesisDoctoral ThesisCollection of Articles


    Climate change and environmental concerns are forcing process industries to increase the share of sustainable resources in energy production. The utilization of biomass receives an increasing attention as a replacement for fossil fuels due to its wide availability and sustainability. However, the unpredictable variability of biomass properties, including moisture content, composition and heating value, results in disturbances, faults, and failures during the power plant operation, which creates additional barriers for a wider utilization of biomass. This thesis focusses on the development of a fault tolerant model predictive control (FTMPC) scheme that addresses the challenges associated with the biomass utilization for power production in BioGrate boilers. The novelty of this scheme lies in the integration of soft-sensors measuring the unpredictable biomass properties with a fault accommodation mechanism. The effectiveness of the developed FTMPC scheme is successfully tested with a dynamic simulator of the BioGrate boiler. This simulator is constructed using the industrial test data from the BioPower 5 CHP plant. In addition, industrial tests, conducted to evaluate the performance of the developed soft-sensors, confirm the prediction accuracy of the fuel moisture content and combustion power in the furnace. Subsequently, the economic evaluation of the soft-sensors integrated FTMPC scheme is presented.
    Translated title of the contributionVikasietoinen malliprediktiivinen säätö (FTMPC) BioGrate-kattilalle
    Original languageEnglish
    QualificationDoctor's degree
    Awarding Institution
    • Aalto University
    • Jämsä-Jounela, Sirkka-Liisa, Supervising Professor
    Print ISBNs978-952-60-6079-8
    Electronic ISBNs978-952-60-6080-4
    Publication statusPublished - 2015
    MoE publication typeG5 Doctoral dissertation (article)


    • fault tolerant control
    • model predictive control
    • biomass
    • fuel quality
    • fuel moisture
    • industrial process
    • combustion


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