|Author:||Mo, Kwok-wai Edmond|
|Title:||Identification of boiler using artificial neural network|
|Other Title:||System identification of boiler using artificial neural network|
Neural networks (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department of Electrical Engineering
|Pages:||iv, 72,  leaves : ill. ; 30 cm|
|Abstract:||This report presents the application of artificial neural networks in modeling the dynamic behavior of three major operation parameters of a 350 MW radiant reheat, controlled circulation drum boiler. Major parameters include main steam pressure, drum level and excess air level. Neural network is designed base on converting their linear transfer function to discrete time model, and the model is trained using three different algorithm including Backpropagation, Levenberg Marquardt and Linear Least Square Algorithm. Evaluation and validation of the models are performed by predicting the dynamic behavior using different sets of actual plant data. The result indicated that the neural network model is shown to be capable of providing accurate predictions. Further, based on performance among three training algorithms which indicated that Levenberg Marquardt algorithm shows the fastest convergence time, a Neural Network Controller is built by applying the algorithm with Internal Mode Control (IMC) strategy, and controllability of the controller is examined and the controlled response indicated Neural Network could be applied in both system identification as well as in control engineering.|
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