Combustion of biomass, especially in small-scale applications, produces high emissions of particulate matter (PM) that have been linked to adverse health effects and global climate change. Current industrial particulate control solutions are prohibitively expensive for use in small scale biomass burners. To overcome these challenges, inexpensive and effective combustion control solutions for small scale applications are needed. The proposed intelligent combustion control system for biofuel combustion in small scale applications can curb PM and gaseous emissions. If successful, the proposed pollution control system will be a disruptive technology and will be low cost enough to be implemented in thousands of small scale installations. The proposed project will demonstrate the feasibility of developing an advanced combustion control module to maximize the efficiency of biomass combustion while minimizing emissions of PM, CO, UHC and NOx. Our hypothesis is that by measuring the temperature, levels of main combustion product species and particulate matter, a predictive model can be developed to intelligently control combustion of biomass fuels of varying composition, moisture content and formats. This is possible due to a combination of recent advances in sensor development and implementation of novel modeling techniques, e.g. (i) real-time exhaust measurements using low cost sensors, (ii) real-time prediction of combustor conditions using CRN (chemical reactor network) modeling, and (iii) an intelligent combustion control algorithm for minimizing pollutant emissions and maximizing combustor efficiency. Development of an advanced combustion control system relies on the ability of the CRN to model combustion processes in the critical pollution formation zones in the biomass combustor. A CRN model will be constructed based on the results of CFD simulations; the CRN model will establish the degree of modeling complexity (detailed chemistry and network arrangement) needed to predict the emissions. A combustion control algorithm for reduction of PM and NOx emission will be developed; this algorithm will be applicable to a wide variety of biofuels. The algorithm will (i) record sensor measurements, (ii) interpret inputs using a predictive CRN model, and (iii) adjust the wood burner controls to minimize pollution formation. This control strategy will be implemented in a commercially available biomass combustion system.
