Emission limited model predictive control of a small-scale biomass furnace. (1st March 2021)

• Record Type: Journal Article
• Authors: Böhler, Lukas; Fallmann, Markus; Görtler, Gregor; Krail, Jürgen; Schittl, Florian; Kozek, Martin
• Is Part Of: Applied energy. Volume 285(2021)
• Publication Date: 2021-03-01
• Subjects: Biomass combustion -- Combustion modeling -- Emission reduction -- Predictive control -- Carbon monoxide; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.116414
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: This paper presents the application of an emission limiting model-based predictive controller for a small-scale biomass grate furnace. The furnace has a nominal power of 100 kW with wood pellets as fuel, but it can be operated with different fuels as well. The model predictive approach extends the existing static feedforward controller of the investigated furnace with a dynamic feedback controller that is able to improve the combustion performance. Simultaneously, the formation of carbon monoxide emissions is minimized within the prediction horizon based on an available emission estimation model for pellets. The results obtained from closed-loop measurements show that the control concept is able to reduce carbon monoxide emissions in partial load operation up to four times while the control error of the supply water temperature for heating is nearly halved during transient operation. This is achieved by incorporating the emission estimation model into the constrained optimization of the predictive controller. Additional results obtained from closed-loop experiments for different fuel types with varying water contents demonstrate the advantages of the proposed model-based approach and its robustness with respect to typical uncertainties of the combustion process. Highlights: Applied emission limiting model predictive controller for a small-scale furnace. Carbon monoxide emission reduction validated with closed-loop measurements. Presented model predictive controller is robust with respect to different fuels. Application of an efficient nonlinear biomass model for control purposes. Integration of a carbon monoxide soft-sensor into the control algorithm.