Boiler combustion condition monitoring and optimization system based on LabVIEW

Abstract

To realize the boiler combustion condition monitoring and optimal adjustment, a boiler combustion conditions monitoring and optimization system based on LabVIEW was proposed. The system used OPC communication technology to realize the remote monitoring and display of boiler combustion operation parameters. In addition, the system applied neural network prediction modeling to realize the on-line soft measurement of flue gas temperature, oxygen content, carbon content of fly ash and nitrogen oxides content, and combined with differential evolution algorithm to seek the boiler optimal secondary air, over-fired air throttle opening parameters under current operating condition. The application results of the system on a 600MW coal-fired boiler showed that the system achieved remote combustion condition monitoring by connecting the OPC tags to the flue gas measuring points of the temperature, oxygen content etc.; combustion optimization tests were carried out in two conditions of 40% load and 90% load of the unit, found the flue gas temperature, carbon content of fly ash and nitrogen oxides(NOx) content averagely declined by 20%, 23% and 31% after combustion optimization in the 40% load, which is of great significance to improve the combustion efficiency of the boiler and reduce NOx emissions.