The paper presents experimental and numerical investigation of pulverized coal combustion process analysis and optimization. The research was conducted on the front-fired pulverized coal boiler with dedicated low-NOx furnace installation. In order to find optimal boiler operating conditions the acoustic gas temperature measurement system and mass flow rate of pulverized coal measurement system was applied. The uniform temperature distribution as a result of uniform coal and air flow provides the optimal combustion process with low level of NOx emission and total organic carbon content in ash. Experimental results confirm that the monitoring and control of fuel and air flow distribution allows to optimize combustion process by increasing thermal efficiency of the boiler. In the numerical part of investigation, the complex CFD model of pulverized coal boiler was made. The calculations of turbulent, reactive, and thermal flow processes were performed at different boiler operating conditions retrieved from power plant on-line monitoring system. The results of numerical simulations enable to identify the optimal boiler operating conditions.
Part of the book: Developments in Combustion Technology
Coal mill malfunctions are some of the most common causes of failing to keep the power plant crucial operating parameters or even unplanned power plant shutdowns. Therefore, an algorithm has been developed that enable online detection of abnormal conditions and malfunctions of an operating mill. Based on calculated diagnostic signals and defined thresholds, this algorithm informs about abnormal operating conditions. Diagnostic signals represent the difference between the measured and the modeled values of two selected mill operating parameters. Models of mill motor current and outlet temperature of pulverized fuel were developed based on the linear regression theory. Various data analysis and feature selection procedures have been performed to obtain the best possible model. The model based on linear regression has been compared with two alternative models. The algorithm validation was carried out based on historical data containing values of operating parameters from 10 months of mill operation. Historical data were downloaded from distributed control system (DCS) of a 200-MW coal-fired power plant. Tests carried out on historical data show that this algorithm can be successfully used to detect certain abnormal conditions and malfunctions of the operating mill, such as feeder blockage, lack of coal and mill overload.
Part of the book: Thermal Power Plants