Intelligent Prediction of Clinker Formation Condition for Steam Boiler Tubes Using Artificial Neural Network

Abstract

Ash, as one of the by-product of combustion either accumulates onto boiler tubes as slag or is collected by electrostatic precipitators attached to the backend of the boiler. Flue gas will transport these ash particles either to the inner surfaces of the boiler or to the dust collecting facilities at the backend of the boiler. Sintered ash deposits formed in the radiant section of the boiler are known as clinkers and they contribute to a wide variety of problems to the boiler. Preventative measures to combat clinker formation is in dire need to the energy sector. In this study, a prediction model using real plan data was developed for detection of clinker formation conditions. Several variations of Artificial Neural Networks were tried and test, with emphasis given on the feed-forward neural network, cascade neural network and recurrent neural network. In addition, sensitivity analysis was also conducted to determine the influence of random input variables to their respective response variables. The Tornado Diagram is selected as the method to determine the most influential parameter for clinker formation. It is expected that the Recurrent Neural Network prediction model and the identified most influential input parameter for clinker formation will assist operators in decision making for the maintenance of boilers.