Investigation of ash fusion characteristics on co-combustion of coal and biomass (straw, sludge, and herb residue) based on experimental and machine learning method.

Abstract

Co-combustion of coal and biomass has the potential to reduce the cost of power generation in plants. However, because of the high content of the alkali metal of biomass ash, co-combustion of these two fuels leads to unpredictable ash fusion temperature (AFT). This study conducted experiments to measure the AFT of straw, sludge, and herb residue when they were blended with coal at different ratios. Additionally, a machine learning algorithm called tuna swarm optimization (TSO) was employed to optimize the support vector regression (SVR) model to predict the softening temperature (ST) of samples. The results indicate that straw and sludge were found to be suitable for blending in small proportions, while herb residue was suitable for blending in larger proportions. In comparison to the traditional grid search optimization model, the TSO algorithm significantly enhances the prediction accuracy of both training and test sets, and improves the generalization ability of SVR.