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.
Highlights
Steam boilers represent one of the main component in the power plant
Using the optimal results achieved from Intelligent Warning Systems (IWSs)-2, the validation for each case was done with the same validation data sets used for IWS-1 validation
Similar approach of IWS-1 was adopted in indicating the fault in IWS-2
Summary
Boiler tubes continuously pass fluids, withstanding extremely high temperatures and pressures of the steam generator [1]. This eventually leads to leakages in boiler tubes. Prediction of boiler tube leak trips is crucial to maintain normal and safe operational conditions of the plant [3]. Benefits of an early detection of boiler tube leak trip are: to increase operating profit by reducing repair costs and secondary damage, increase safety of the plant, increase availability and tube life, and avoid unplanned outages [4]. Using information provided by measurements from several sensors and actuators that are abundantly stored throughout operations, the intelligent warning system is able to predict a failure [5]
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