Optimization of NOx emission from coal combustion process using pattern search

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 (NO_x). The outcome is very encouraging and suggests that PS methods may be very efficient when solving low NO_x combustion optimization problem.