State Estimation and Model Based Control of a Biomass Furnace

Research output: ThesisMaster's ThesisResearch

Abstract

As a sustainable energy source, the combustion of solid biomass becomes more important in comparison to fossil fuels. Thereby, especially the demand for low emission and high efficiency operation of biomass furnaces represents a challenge to their control. The aim of this thesis is the evaluation and improvement of an existing, model-based control strategy, which comprises a controller using the input-output linearization technique and an extended Kalman filter for state estimation. The work was performed on the basis of a moving grate furnace with a nominal boiler capacity of 180 kW, in cooperation with the competence center Bioenergy 2020+.

In this thesis a series of disturbances is identified. Especially, particularly high fluctuations of the thermal fuel decomposition could be observed in the investigated plant. Subsequently, appropriate shaping filters are discussed to model these disturbances in the Kalman filter. Furthermore, sensor models are developed to take into account delays of the measured variables due to sensor dynamics and dead times. Based on these results a new Kalman filter is designed and implemented.

The disturbances additionally affect the transfer behavior of the plant linearized by means of input-output linearization. Therefore, the disturbances estimated by the extended Kalman filter are also considered in the determination of the non-linear state feedback control-law.

Finally, the implementation and experimental verification of the modified control strategy at the investigated plant are discussed. A comparison to the original strategy shows a significant improvement in the stabilization of the water feed temperature of the boiler and the temperature in the secondary combustion chamber, as well as more precise control of the air-fuel ratio in the fuel bed.
Translated title of the contributionState Estimation and Model Based Control of a Biomass Furnace
Original languageGerman
QualificationMaster of Science
Awarding Institution
  • Graz University of Technology (90000)
Supervisors/Advisors
  • Dourdoumas, Nicolaos, Supervisor
  • Gölles, Markus, Supervisor
Publication statusPublished - Aug 2012

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