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

Palash Sarkar, Jukka Kortela, Alexandre Boriouchkine, Elena Zattoni and Sirkka-Liisa Jämsä-Jounela
Additional contact information
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

Energies, 2017, vol. 10, issue 2, 1-14

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.

Keywords: data reconciliation; model predictive control; fault-tolerant contro; BioGrate boiler (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/2/194/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/2/194/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:2:p:194-:d:89777

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().