Alkaline gases emission estimation and paraconsistent logic techniques application to label bagasse combustion conditions

Abstract

When some renewable energy sources are used, such as biomass solid waste, it is necessary to measure and control pollutant emissions. Since raw sugarcane comes from a wide variety of supply sources, it is necessary to monitor the behavior of the biomass residues during the burning process to control flame stability, emissions, and combustion efficiency. The variation in the combustion temperature of biomass affects the combustion efficiency and also the emission of alkaline metals, such as potassium (K) and sodium (Na), which can cause corrosion in boilers. The objective of the present work is to estimate the content of alkali gases in the flames during the burning of solid biomass residues, specifically sugarcane bagasse in this case. The flame emission spectroscopy (FES) spectrometry technique is used and flame emission spectra is captured during the combustion of bagasse in a pilot furnace. The flame temperature is calculated using the two-color method together with the inverse Levenberg-Marquard method. Models and correlations from literature are used to obtain a database of emission content of sodium and potassium. Using paraconsistent logic techniques, a predictor algorithm is implemented and bagasse samples were labeled to identify similar physical combustion conditions. The results of the emission of sodium and potassium estimated for groups labeled using the algorithm of paraconsistent logic are compared and show good agreement between them. A good and reasonable agreement was found in the estimation of the emission content of potassium and sodium.