Analysis Of Boiler Tube Leakage Using Artificial Neural Network

• Main Author: Wan Norizan B Wan Amat, Wan Norizan
• Format: Final Year Project
• Language: English; English; English; English; English
• Published: Universiti Teknologi Petronas 2010
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://utpedia.utp.edu.my/1473/1/6%29_CHAPTER_1.pdf; http://utpedia.utp.edu.my/1473/2/7%29_CHAPTER_2.pdf; http://utpedia.utp.edu.my/1473/3/8%29_CHAPTER_3.pdf; http://utpedia.utp.edu.my/1473/4/9%29_CHAPTER_4.pdf; http://utpedia.utp.edu.my/1473/5/10%29_CHAPTER_5.pdf; http://utpedia.utp.edu.my/1473/

Artificial neural network (ANN) models, developed by training the network with data from an existing plant, are very useful especially for large systems such as Thermal Power Plant. The project is focusing on the ANN modeling development and to examine the relative importance of modeling and processing variables in investigating the unit trip due to steam boiler tube leakage. The modeling and results obtained will be used to overcome the effect of the boiler tube leakage which influenced the boiler to shutdown if the tube leakage continuously producing the mixture of steam and water to escape from the risers into the furnace. The Artificial Intelligent-ANN has been chosen as the system to evaluate the behavior of the boiler because it has the ability to forecast the trips. Hence, the objective of this study has been developed to design an ANN to detect and diagnosis the boiler tube leakage and to simulate the ANN using real data obtained from Thermal Power Plant. The feed-forward with back-propagation, (BP) ANN model will be trained with the real data obtained from the plant. Training and validation of ANN models, using real data from an existing plant, are very useful to minimize or avoid the trip occurrence in the plants. The study will focus on investigating the unit trip due to tube leakage of risers in the boiler furnace and developing the ANN model to forecast the trip.