| This thesis presents a feasibility study for a low cost sensor-based combustion control system using a predictive chemical kinetic model that captures efficiencies and pollution emissions during biomass combustion. Low cost sensor module was developed, the sensors were calibrated to measure carbon monoxide and particulate matter (PM) in combustion exhaust. Major combustion species in the exhaust of a commercial biomass furnace, operating with white oak, were measured. The species concentrations were measured using the low cost sensors and commercially available diagnostics. The low cost sensor outputs compare well with the reference instruments and the sensors can be employed to measure varying concentration of CO and particulate matter in combustion exhaust.;A predictive chemical kinetic model was generated to simulate biomass processes. The model uses a four element chemical reactor network (CRN) and successfully simulates smoldering, ignition and flaming combustion events. The model agrees with concentration of CO and particulate matter from experiments.;The sensors and CRN model can be integrated in a control system for biomass combustion that can potentially improve combustion efficiency and reduce emissions of particulate matter, CO and unburned hydrocarbons that have been linked to urban and rural air pollution resulting in adverse health effects. |
