Multidimensional minimization training algorithms for steam boiler high temperature superheater trip using artificial intelligence monitoring system

Firas Basim Ismail, Hussain H. Al-Kayiem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010
Pages2421-2426
Number of pages6
DOIs
Publication statusPublished - 01 Dec 2010
Event11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010 - Singapore, Singapore
Duration: 07 Dec 201010 Dec 2010

Publication series

Name11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010

Other

Other11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010
CountrySingapore
CitySingapore
Period07/12/1010/12/10

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering

Cite this

Ismail, F. B., & Al-Kayiem, H. H. (2010). Multidimensional minimization training algorithms for steam boiler high temperature superheater trip using artificial intelligence monitoring system. In 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010 (pp. 2421-2426). [5707322] (11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010). https://doi.org/10.1109/ICARCV.2010.5707322