eprintid: 907
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/09/07
datestamp: 2023-11-09 15:49:03
lastmod: 2023-11-09 15:49:03
status_changed: 2023-11-09 15:38:41
type: conference_item
metadata_visibility: show
creators_name: Alnaimi, F.B.I.
creators_name: Al-Kayiem, H.H.
title: Multidimensional minimization training algorithms for steam boiler drum level trip using artificial intelligent monitoring system
ispublished: pub
keywords: Artificial intelligent; Artificial Neural Network; Boiler drums; Broyden-Fletcher-Goldfarb-Shanno; Drum level trip; Fault detection and diagnosis; Fault Detection and Diagnosis(FDD); Hidden layers; Low level; Malaysia; Neural network model; Neural network structures; Power units; Quasi-Newton; Root mean square errors; Steam boiler; Thermal power plants; Training algorithms, Algorithms; Boilers; Fault detection; Mathematical operators; Network layers; Steam; Structural optimization; Thermoelectric power plants, Neural networks
note: cited By 3; Conference of 2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010 ; Conference Date: 15 June 2010 Through 17 June 2010; Conference Code:84196
abstract: This paper deals with the Fault Detection and Diagnosis of steam boiler using developed artificial Neural networks model. Water low level trip of steam boiler is artificially monitored and analyzed in this study, using two different interpretation algorithms. The Broyden-Fletcher-Goldfarb-Shanno quasi-Newton and Levenberg-Marquart are adopted as training algorithms of the developed neural network model. Real site data is captured from a coal-fired thermal power plant in Perak state - Malaysia. Among three power units in the plant, the boiler drum data of unit3 was considered. The selection of the relevant variables to train and validate the neural networks is based on the merging between the theoretical base and the operators experience and the procedure is described in the paper. Results are obtained from one hidden layer and two hidden layers neural network structures for both adopted algorithms. Detailed comparisons have been made based on the Root Mean Square Error. The results are demonstrating that the one hidden layer with one neuron using BFGS training algorithm provides the best optimum NN structure.
date: 2010
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952768751&doi=10.1109%2fICIAS.2010.5716197&partnerID=40&md5=9c57819d56a8090c7eab43a87ef6f1c1
id_number: 10.1109/ICIAS.2010.5716197
full_text_status: none
publication: 2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010
place_of_pub: Kuala Lumpur
refereed: TRUE
isbn: 9781424466238
citation:   Alnaimi, F.B.I. and Al-Kayiem, H.H.  (2010) Multidimensional minimization training algorithms for steam boiler drum level trip using artificial intelligent monitoring system.  In: UNSPECIFIED.