Research And Development Of Sootblow Optimization System Of Utility Boiler

As to serious ash deposition on the heating surfaces of coal-fired boilers being a common phenomenon, this paper took QinBei 1# boiler as an object. Taking advantage of the data from the DAS of the plant and fixing new gas temperature measure points on lower-temperature convection heating surfaces, we built the fouling monitoring model with three layers of BP neural network under the condition of observing the variation of fouling conditions for a long time, which is described by clean factor.After building the proper model, we developed a suit of monitoring system to monitor the soot deposition level of the boiler heat exchanger surfaces. We analyzed all sorts of costs and incomes of boiler sootblowing relying on boiler running. The result and analysis prove the accuracy and reliability of both the monitoring model and system. At the same time,we discussed the main factors that affect the monitoring result and explained the new idea of sootblow optimization.At last, we conclude the paper, and discuss the further development of the model and monitoring system in the future.