Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass BoilerReport as inadecuate


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


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

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Department of Electrical, Electronic and Information Engineering -G. Marconi-, Alma Mater Studiorum · University of Bologna, 40136 Bologna, Italy





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Author to whom correspondence should be addressed.



Academic Editor: Tariq Al-Shemmeri

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. View Full-Text

Keywords: data reconciliation; model predictive control; fault-tolerant contro; BioGrate boiler data reconciliation; model predictive control; fault-tolerant contro; BioGrate boiler





Author: Palash Sarkar 1,* , Jukka Kortela 1, Alexandre Boriouchkine 1, Elena Zattoni 2 and Sirkka-Liisa Jämsä-Jounela 1

Source: http://mdpi.com/



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