Applied Energy, Vol.204, 1124-1137, 2017
Development of high-temperature corrosion risk monitoring system in pulverized coal boilers based on reducing conditions identification and CFD simulations
Low-emission combustion (for example the use of low-NOx burners and air staging) contributes to formation of a reducing atmosphere in the furnace, that is accompanied by oxygen depletion and excess of CO in the vicinity (boundary layer) of waterwalls. Corrosion of boiler tubes is often caused by reducing atmosphere. O-2 and CO measurement in the boundary layer of evaporators can be a good indicator of corrosion risk assessment. System based on the on-line measurement of the O-2 and CO concentration in the boundary layer of the industrial scale boiler walls was described. To improve the functionality of the monitoring system Computational Fluid Dynamics may appear helpful. A validated CFD model capable of properly predicting the CO and O-2 concentration in the vicinity of the combustion chamber walls may help to adjust the monitoring system during variable boiler operating conditions or different fuel properties without the necessity to repeat the measurements for new conditions. The scientific part of the current research is concentrated on volatiles combustion simulation with the emphasis on CO burnout. Four popular global mechanisms have been implemented into CFD code and their CO and O-2 predictive capabilities are demonstrated. Additionally global mechanisms have been compared to detailed one in Perfectly Stirred Reactor model. It appears that the choice of global mechanism has significant influence on CO and O-2 prediction. The measurements of the CO and O-2 in the waterwalls boundary layer have been extracted from the monitoring system and compared to simulation results. One of the tested mechanisms demonstrated acceptable qualitative agreement with the measurement in terms of O-2 predictions. The quantitative accuracy of CFD-based oxygen prediction in the boundary layer was described as moderate. CFD-based CO prediction was less satisfactory. (C) 2017 Elsevier Ltd. All rights reserved.