The Use of Near-Infrared Spectroscopy for the Prediction of Gaseous and Particulate Emissions from Agricultural Feedstock Pellets

Abstract

The potential of near-infrared spectroscopy in conjunction with chemometric techniques to predict the particulate matter and gaseous emissions of biomass pellet blends was assessed in this study. A diverse range of biomass was used, including wood, Miscanthus, wheat straw, and the herbaceous energy grass Szarvasi-1 (Elymus elongatus subsp. ponticus cv. Szarvasi-1). The particulate matter emissions were predicted with root-mean-square errors of prediction (RMSEP) of 6.83 (R2 = 0.57), 8.71 (R2 = 0.66), and 11.25 (R2 = 0.65) mg m–3 for the PM10, PM0, and TSP emissions, respectively. The gaseous emissions of oxides of nitrogen (NOx), sulfur dioxide (SO2), and carbon monoxide (CO) were predicted with RMSEPs of 14.28 (R2 = 0.93), 4.59 (R2 = 0.88), and 9.08 (R2 = 0.48) mg m–3, respectively. No significant models could be developed for the PM2.5 or PM1 emissions. The results indicate that near-infrared spectroscopy has the potential to predict the emissions of biomass pellets in a multibiomass stream.