The Study of Optimizing Circulating Fluidized Bed Boiler Operational Parameters Based on Neural Network and Genetic Algorithm

The conventional routes of optimizing boiler operational parameters were mainly according to boiler design value and historically optimum value, but these methods had certain limitation in the real-time renewal and excavating optimization potential. This paper takes the circulating fluidized bed boiler (CFB) as the research object, by using the Absolute Mean Impact Value (AMIV) to optimize the structure of the network model for forecasting boiler efficiency, and enhance the model´s predictive ability. Then it carries the genetic algorithm on this model to search the optimized value, and realizes to optimize boiler operational parameters under different loads.