Numerical simulation of the carbon burnout process in solid fuel combustion

The degree of carbon burnout is very important for power plants since it directly affects the value of the fly ash as a salable byproduct. Many factors and the interactions between them can all affect carbon burnout. This work presents a Carbon Burnout Kinetic (CBK) model, a coal-general kinetics package that is specifically designed to predict the total extent of carbon burnout and ultimate fly ash carbon content for prescribed temperature/oxygen histories typical of pulverized coal combustion systems. Key to the modeling is the inclusion of three deactivation mechanisms: thermal annealing, ash inhibition, and extinction/near-extinction phenomena. They are responsible for the low reactivity and low burning rate in the late stages of coal combustion, and are implemented in three sub-models of CBK. Then the mechanisms of extinction and near extinction in pulverized solid fuel combustion are discussed based on CBK calculations. It is found that the non-linear equations describing heterogeneous reaction and transport predict extinction-like events during combustion of single pulverized char particles under some conditions. The most common cause of extinction-like events under pulverized coal combustion conditions is predicted to be reductions in particle diameter and gas temperature, accompanied by a transition from Zone II to Zone I burning. In high reactivity materials extinction can be delayed to very high conversion, where the primary cause may be transport modification by mineral matter under conditions when the ash is partially fused. Annealing influences char reactivity, but is rarely predicted to be the primary cause of extinction in the late stages of combustion. Finally, a commercial implementation of the CBK model—LOI Predictor™ is presented. The LOI Predictor™ can make relative carbon burnout predictions based only on proximate and ultimate fuel analyses. It is validated against three sets of data: lab, pilot, and full-scale data. In most cases it predicts the right trend (either LOI increase or decrease after switching coals) and in many cases, it predicts reasonably accurate magnitude of LOI change. The good agreement with different sets of data justifies its use as a useful tool for exploring inherent sensitivity of the burnout process to various coal properties. It is found that burnout is particularly sensitive to the char yield, swelling factor, coal particle size distribution, and coal ash content.