Optimization of coal-fired boiler using neural network improved by genetic algorithm

With the energy shortage and environment crisis, it draws public attention to improve the efficiency of coal-fired boiler combustion and reduce pollutant emission. However, operators adjust the coal-fired boiler by the production experience which has less scientific and much more randomness. At the same time, the method between improving efficiency and reducing the NOx emissions is so different that it is hard to get the adjustment point by the experiment. It is meaningful to research the coal-fired boiler optimization simulation. The study improves the neural network by genetic algorithm, and uses it to develop a model on the basis of optimal combustion experiment data, and optimizes the combustion parameters by the genetic algorithm to guide employee to adjust the fuel, air rate to achieve the optimum production. The experiment shows that the method of developing a mode of data of optimal combustion experiment by improved neural network and optimizing the parameters by genetic algorithm can guide the production better.