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
