Effect of additives on the reduction of gas and particulate emissions from rice straw pellet fuel: Experimental study and modeling

Abstract

Rice straw, an abundant agricultural residue, holds strong potential for biomass energy, but its open field burning causes severe air pollution. While pelletizing offers a cleaner alternative, it still presents emission and slagging challenges due to its composition and combustion characteristics. This study designed emission tests on rice straw pellets and developed regression models to optimize additive combinations for reducing CO, NO_x, and fine particulate matter (PM₁₀) emissions. The integrated use of three cost-effective additives (kaolin, CaO, and black liquor) with predictive modeling represents a novel, scalable, and economically viable approach for cleaner biomass fuel production. The regression models demonstrated strong predictive accuracy, evidenced by high correlation coefficient (R²) values (CO: 0.90, NO_x: 0.95, PM₁₀: 0.98) and low root mean square error (RMSE) and mean absolute error (MAE). The additive-blended samples reduced the CO emission by 54.2%, NO_x by 31.0%, and PM₁₀ by 30%. The most effective additive combinations for simultaneously reducing CO, NO_x, and PM₁₀ were 2% kaolin with either 0–2% CaO and 8% black liquor. Kaolin, CaO, and black liquor effectively reduce emissions from rice straw pellets, highlighting the need for further research on emission control, life cycle assessment, and large-scale feasibility.