%0 Conference Paper
%A Ismail, F.B.
%A Al-Kayiem, H.H.
%D 2010
%F scholars:976
%K Artificial intelligent; Broyden-Fletcher-Goldfarb-Shanno; Critical time; Fault Detection and Diagnosis Neural Network (FDDNN); High temperature; Integrated plants; Intelligent monitoring systems; Levenberg-Marquardt; Malaysia; Monitoring system; Operational variables; Potential solutions; Power units; Quasi-Newton; Root mean square errors; Safe operation; Steam boiler; Thermal Power Plant (TPP); Thermal power plants; Training algorithms; Trial-and-error approach, Algorithms; Computer vision; Fault detection; Monitoring; Network architecture; Network layers; Neural networks; Plant shutdowns; Robotics; Steam power plants; Superheaters; Thermoelectric power plants, Boilers
%P 2421-2426
%R 10.1109/ICARCV.2010.5707322
%T Multidimensional minimization training algorithms for steam boiler high temperature superheater trip using artificial intelligence monitoring system
%U https://khub.utp.edu.my/scholars/976/
%X Steam Boilers are important equipment in power plants and the boiler trips may lead to the entire plant shutdown. To maintain performance in normal and safe operation conditions, detecting of the possible boiler trips in critical time is crucial. As a potential solution to these problems, an artificial intelligent monitoring system specialized in boiler high temperature superheater trip has been developed in the present paper. The Broyden Fletcher Goldfarb Shanno Quasi-Newton (BFGS Quasi Newton) and Levenberg-Marquardt (LM) have been adopted as training algorithms for the developed system. Real site data was captured from MNJ coal-fired thermal power plant-Malaysia. Among three power units in the plant, the boiler high temperature superheater of unit one was considered. An integrated plant data preparation framework for boiler high temperature superheater trip with related operational variables, have been proposed for the training and validation of the developed system. Both one-hidden-layer and two-hidden-layers network architectures are explored using neural network with trial and error approach. The obtained results were analyzed based on the Root Mean Square Error for developed intelligent monitoring system. ©2010 IEEE.
%Z cited By 3; Conference of 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010 ; Conference Date: 7 December 2010 Through 10 December 2010; Conference Code:84059