Use of differential evolution in low NOx combustion optimization of a coal-fired boiler

The present work focuses on low NO_x emissions combustion modification of a 300MW dual-furnaces coal-fired utility boiler through a combination of support vector regression (SVR) and a novel and modern differential evolution optimization technique (DE). SVR, used as a more versatile type of regression tool, was employed to build a complex model between NO_x emissions and operating conditions by using available experimental results in a case boiler. The trained SVR model performed well in predicting the NO_x emissions with an average relative error of less than 1.14% compared with the experimental results in the case boiler. The optimal ten inputs (namely operating conditions to be optimized by operators of the boiler) of NO_x emissions characteristics model were regulated by DE so that low NO_x emissions were achieved, given that the boiler load is determined. Two cases were optimized in this work to check the possibility of reducing NO_x emissions by DE under high and low boiler load. The time response of DE was typical of 20 sec, at the same time with the better quality of optimized results. Remarkable good results were obtained when DE was used to optimize NO_x emissions of this boiler, supporting its applicability for the development of an advanced on-line and real-time low NO_x emissions combustion optimization software package in modern power plants.