Near infrared hyperspectral imaging for the prediction of gaseous and particulate matter emissions from pine wood pellets

Abstract

Near infrared hyperspectral imaging was used to predict the particulate matter (PM) and gaseous emissions of pellets produced from pinewood with varying moisture contents. PM10 and SO2 emissions were successfully determined using partial least squares regression coupled with spectral data of the pellets. The resulting models had R2 values of 0.74 and 0.75 and RMSEPs of 0.29 mg m−3 and 2.01 mg m−3 for the PM10 and SO2 emissions, respectively. Correlations for the remaining emissions factors were also determined. The product of the feed rate and flue gas temperature was found to correlate with the PM1, TSP and CO emissions with Pearson's r values of 0.48, 0.54 and 0.74, respectively. PM2.5 and PM0 emissions correlated with the flue gas temperature (r = 0.48) and bulk density (r = 0.62), respectively. Gaseous emissions of NOx correlated with a Pearson's r–value of 0.57 to the product of the bulk density and the flue gas temperature. The results indicate that near infrared spectroscopy can be used to predict the PM10 and SO2 emissions of a single biomass type sample set. Correlations between physical parameters of the pellets and emissions factors can be used for approximate predictions of emissions from wood pellets.