Combustion of biomass, especially in small-scale applications, produces high emissions of particulate matter (PM). Particulate matter emissions have been linked to adverse health effects, such as cardiovascular and respiratory tract diseases. For instance, asthma accounts for more than 4,000 deaths per year in the US; the estimated annual direct, productivity and mortality cost in the US alone is $56 billion per annum (2007 estimate). Uncertainty remains about the contributing factors, but air quality and, in particular, PM pollution is one substantial contributor. Therefore, PM emissions from biomass combustion can hinder the competitiveness of bioenergy sources. Current industrial particulate control solutions such as scrubbers, baghouses, and electrostatic precipitators (ESP) are prohibitively expensive for use on small scale biomass burners and require significant maintenance. To overcome this disadvantage, inexpensive and effective combustion control solutions for small scale applications are needed.

High-efficiency, low-emission biomass burners are generally configured for a narrow range of fuel type and fuel size/shape. For example, typical wood moisture content used in downdraft burners is limited to 15-22%, while, in many residential applications, the wood moisture content varies significantly and often is as high as 30-35%. The fuel format (size and shape), as well as the fuel type, limit the application of combustors in residential and commercial environments. To minimize PM and NO_x emissions, significant changes in the air flow rate and air flow split between primary and secondary air must be made as these emissions are highly dependent on the temperature of the primary pyrolysis zone and the burnout zone.

These challenges provide an opportunity to develop an advanced, but inexpensive, combustion control system consisting of an array of low cost sensors that feed a real-time analysis module which in turn directs both the primary and secondary air flow control in a way that continuously minimizes emissions. The successful application of this approach will increase the efficiency and reduce PM and gaseous emissions (CO, NO_x, and unburned hydrocarbons), which will make biomass technology more acceptable, and will in turn reduce the reliance on fossil fuels in many residential communities and commercial applications. We investigate sensor-based combustion controls with a CRN model can capture and correct the inefficiencies and pollution spikes associated with different fuel composition and fuel size/shape (format). This new control approach will be optimized to maximize combustion efficiencies and minimize emissions of particulate matter, NO_x, CO, and unburned hydrocarbons that have been linked to detrimental human health effects, global warming, and photochemical smog.