Abstract. Repeated boiler tube leak trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. In this study two artificial intelligent monitoring systems specialized in boiler tube leak trips have been proposed. The first intelligent warning system (IWS-1) represents the use of pure artificial neural network system whereas the second intelligent warning system (IWS-2) represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. The Extreme Learning Machine (ELM) methodology was also adopted in IWS-1 and compared with traditional training algorithms. Genetic algorithm (GA) was adopted in IWS-2 to optimize the ANN topology and the boiler parameters. An integrated data preparation framework was established for 3 real cases of boiler tube leak trip based on a thermal power plant in Malaysia. Both the IWSs were developed using MATLAB coding for training and validation. The hybrid IWS-2 performed better than IWS-1.The developed system was validated to be able to predict trips before the plant monitoring system. The proposed artificial intelligent system could be adopted as a reliable monitoring system of the thermal power plant boilers.

Frequent boiler tube trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. Several methodologies for the fault diagnosis in a plant have been developed. However these methodologies are difficult to be implemented. In this study, two artificial intelligent monitoring systems specialized in boiler trips have been proposed. The first intelligent monitoring system represents the use of pure artificial neural network system whereas the second intelligent monitoring system represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. In the first system using pure artificial neural network, the trip was predicted 5 minutes before the actual trip occurrence. The hybrid intelligent system was able to optimize the selection of the most influencing variables successfully and predict the trip 2 minutes before the actual trip. The first intelligent system performed better than the second one based on the prediction time. The proposed artificial intelligent system could be adopted on-line as a reliable controller of the thermal power plant boiler.

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Abstract. Tube leakage in boilers has been a major contribution to trips which eventually leads to power plant shut downs. Training of network and developing artificial neural network (ANN) models are essential in fault detection in critically large systems. This research focusses on the ANN modelling through training and validation of real data acquired from a sub-critical boiler unit. The artificial neural network (ANN) was used to develop a compatible model and to evaluate the working properties and behaviour of boiler. The training and validation of real data has been applied using the feed-forward with back-propagation (BP). The right combination of number of neurons, number of hidden layers, training algorithms and training functions was run to achieve the best ANN model with lowest error. The ANN was trained and validated using real site data acquired from a coal fired power plant in Malaysia. The results showed that the Neural Network (NN) with one hidden layers performed better than two hidden layer using feed-forward back-propagation network. The outcome from this study give us the best ANN model which eventually allows for early detection of boiler tube leakages, and forecast of a trip before the real shutdown. This will eventually reduce shutdowns in power plants.

Abstract.Steam economizer represents one of the main equipment in the power plant. Some steam economizer's behavior lead to failure and shutdown in the entire power plant. This will lead to increase in operating and maintenance cost. By detecting the cause in the early stages maintain normal and safe operational conditions of power plant. However, these methodologies are hard to be achieved due to certain boundaries such as system learning ability and the weakness of the system beyond its domain of expertise. The best solution for these problems, an intelligent modeling system specialized in steam economizer trips have been proposed and coded within MATLAB environment to be as a potential solution to insure a fault detection and diagnosis system (FDD). An integrated plant data preparation framework for 10 trips was studied as framework variables. The most influential operational variables have been trained and validated by adopting Artificial Neural Network (ANN). The Extreme Learning Machine (ELM) neural network methodology has been proposed as a major computational intelligent tool in the system. It is shown that ANN can be implemented for monitoring any process faults in thermal power plants. Better speed of learning algorithms by using the Extreme Learning Machine has been approved as well.