Particulate emissions are formed during the combustion of biogenic fuels depending on the type of furnace, the operating conditions in terms of the combustion quality and the different fuel properties. The release of especially small particles often leads to health problems such as the development or worsening of lung diseases. Downstream electrostatic precipitators (ESP) represent a state of the art separation technology in medium and large biomass plants. However, these precipitators are often difficult to implement in smaller furnaces due to economic aspects and space constraints. This study deals with the integration and experimental investigation of an ESP system into the boiler body of a small scaled biomass furnace (< 100 kW). In Addition to the full load behaviour of the firing system, further test arrangements with different part load conditions of the boiler are being considered in order to analyse the particle precipitation under realistic plant operation with regard to flue gas properties and flow conditions. Furthermore, different fuels are considered. Both, discontinuous as well as time-resolved aerosol measuring methods are used to determine particulate matter emissions. The results of the discontinuous dust measurements show that with the integrated ESP, at least 50 % of the particles in the fine dust range are separated, both at full and partial load operation of the boiler, irrespective of the fuel used. Furthermore, it is shown that partial load conditions favour the separation efficiency due to low velocities and low temperatures of the gas flow over the discharge electrode, which is situated in the reversing chamber. Accordingly, the separation efficiency in part load is between 65 and 85 %, depending on fuel used. In order to enable a more precise observation of the separation behaviour with regard to particle size, additional continuous ELPI (electrical low pressure impactor) measurements are carried out for a selected fuel (wood chips). These measurements show that for small particle collectives (dP < 1 μm) separation efficiencies of over 55 % (full load) and over 80 % (part load) are achieved.
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.