Conference Paper

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

DOI: 10.1109/ICNC.2010.5583524 Conference: Natural Computation (ICNC), 2010 Sixth International Conference on, Volume: 8
Source: IEEE Xplore


The present work focuses on low NOx 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 NOx emissions and operating conditions by using available experimental results in a case boiler. The trained SVR model performed well in predicting the NOx 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 NOx emissions characteristics model were regulated by DE so that low NOx emissions were achieved, given that the boiler load is determined. Two cases were optimized in this work to check the possibility of reducing NOx 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 NOx emissions of this boiler, supporting its applicability for the development of an advanced on-line and real-time low NOx emissions combustion optimization software package in modern power plants.

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Available from: Ligang Zheng, Sep 08, 2015
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    • "Up to now, several global optimization algorithms were employed to optimize NO x emission from coal combustion process such as genetic algorithm (GA) [1] [2], particle swarm optimization (PSO) [3] [4], ant colony optimization (ACO) [5], estimation of distribution algorithm (EDA) [6], simulated annealing (SA) [7], and differential evolution (DE) [8]. However, the slow convergence speed and the poor solution quality are two unresolved problems of above mentioned global optimization algorithms. "
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    ABSTRACT: This study presents a new approach based on a pattern search algorithm to solve combustion optimization problem (i.e. achieving expected goals by optimizing the parameters of the combustion process). In the optimization problem, the objective function was implicitly expressed by a surrogate model, which was illustrated by a support vector machine. Ten inputs for this “black box” surrogate model were chosen as design variables of the pattern search algorithm. Then, the proposed method was applied to a coal combustion process, aiming at reducing the nitrogen oxides (NOx). The outcome is very encouraging and suggests that PS methods may be very efficient when solving low NOx combustion optimization problem.
    Seventh International Conference on Natural Computation, ICNC 2011, Shanghai, China, 26-28 July, 2011; 07/2011