Optimizing boiler combustion parameters based on evolution teaching-learning-based optimization algorithm for reducing NO_x emission concentration

How to reduce a boiler's NO_x emission concentration is an urgent problem for thermal power plants. Therefore, in this paper, we combine an evolution teaching-learning-based optimization algorithm with extreme learning machine to optimize a boiler's combustion parameters for reducing NO_x emission concentration. Evolution teaching-learning-based optimization algorithm (ETLBO) is a variant of conventional teaching-learning-based optimization algorithm, which uses a chaotic mapping function to initialize individuals' positions and employs the idea of genetic evolution into the learner phase. To verify the effectiveness of ETLBO, 20 IEEE congress on Evolutionary Computation benchmark test functions are applied to test its convergence speed and convergence accuracy. Experimental results reveal that ETLBO shows the best convergence accuracy on most functions compared to other state-of-the-art optimization algorithms. In addition, the ETLBO is used to reduce boilers' NO_x emissions by optimizing combustion parameters, such as coal supply amount and the air valve. Result shows that ETLBO is well-suited to solve the boiler combustion optimization problem.