The Study On Optimization Sootblowing System Based On Fuuzy Neural Net

Slag and ash deposited on heating surface of coal-fired boiler affected boiler efficiency and safe operation. Sootblowing is an effective method to solve this problem. Aiming at the common phenomenon of ash deposit on heat transfer surface, the sootblowing optimization system of convection heating surface was developed based on a 300MW boiler.The thesis introduced that sootblowing was beneficial to energy-saving and emission reduction, the slag and ash deposited on the heating surface reduced the boiler efficiency, The monitoring methods of heating surface contamination at home and abroad were also introduced; According to the actual situation of boiler operation, the intelligent control sootblowing system based onneural networks and fuzzy control was proposed.Based on the data from DCS system of a 300MW boiler, fuzzy neural network computing model was bulit using the heat balance calculation theory, the model sootblowers net incoming(NET) was the monitoring parameters of the model. The change of sootblowers net incoming of heating surface with time was analyzed, but the model using the NET as the monitoring parameter had some shortcomings. So the model was improved. In the improved medel the sootblowers net incoming, temperature of the heating surface and the load of boiler were used as the input parameters, the sootblowers confidence level was used as the output parameter. The improved model was used to calculate the fouling degree of the boiler heating surface. The results of silulation inferred that the efficiency of heating surface was improved. The improved model can be used to monitoring the fouling degree of boiler heating surface. Finally, the realization ideas of sootblowing optimization system in computer control system was introduced, and the organization of the network, data acquisition from DCS were also analyzed. The main functional module of the sootblowing opimization control system was developed.Finally, the research content of the thesis was summarized and a number of recommendation for the development of the sootblowers optimization model were put forward.