Computational Prediction of Co-firing with Various Biomass Waste Using Turbulent Non-Premixed Combustion

Abstract

Co-firing in coal power plants has limitations because the existing combustion systems are designed to provide optimal performance only with coal. Therefore, investigating the combustion aspects of co-firing by mixing coal with biomass before applying it to existing coal power plants is necessary. To address this, a new numerical model was developed to predict the co-firing behavior of coal with various types of biomass waste, specifically focusing on temperature and pollutant behavior. This study developed a co-firing model in a Drop Tube Furnace (DTF) using a composition of 25% Wood Chips (WC), 25% Solid Recovered Fuel (SRF), 25% Empty Fruit Bunch Fibers (EFFR), and 25% Rice Husk (RH). A structured grid arrangement and the Probability Density Function (PDF) were utilized to depict the relationship between chemical combustion and turbulence. The distributions of temperature and mass fractions of pollutants along the furnace axis were predicted. The highest temperature was observed with 25% EFFR, attributed to its highest volatile matter content. The simulation predicted that 25% RH would be the lowest SO2 emitter. However, it also showed a slight increase in NO and CO levels due to the increased oxygen content when coal was mixed with biomass. The simulation with 25% EFFR predicted a decrease in CO2 emissions compared to other biomass types. The results of this parametric investigation could support the implementation of biomass co-firing technology in existing coal-fired power plants.