Publication:
Development and implementation of Intelligent Soot Blowing Optimization System for TNB Janamanjung

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dc.citedby1
dc.contributor.authorSundaram T.en_US
dc.contributor.authorBasim Ismail F.en_US
dc.contributor.authorGunnasegaran P.en_US
dc.contributor.authorGurusingam P.en_US
dc.contributor.authorid57196438069en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid35778031300en_US
dc.contributor.authorid57196439549en_US
dc.date.accessioned2023-05-29T06:37:42Z
dc.date.available2023-05-29T06:37:42Z
dc.date.issued2017
dc.descriptionBackpropagation; Boilers; Coal; Computational fluid dynamics; Fossil fuel power plants; Neural networks; Soot; Coal-fired power plant; Combination of neural-network; Data preparation; Energy demands; Optimization system; Predictive tools; Training algorithms; Training function; Dusten_US
dc.description.abstractWith an ever increasing demand for energy, Malaysia has become a nation that thrives on solid power generation sector to meet the energy demand and supply market. In a coal fired power plant, soot blowing operation is commonly used as a cleaning mechanism inside the boiler. There are many types of sequence available for this soot blowing operation. Hence, there is no efficient ways in utilizing the soot blowing operation to enhance the efficiency of boiler. Soot blowing optimization requires specific set of data preparation and simulation in order to achieve the best modal. Computational Fluid Dynamics (CFD) is used to model a 700MW super-critical boiler, whereby parameters with effect to soot blowing operation is studied. Two different boiler condition is studied to analyze parameters in a clean and faulty boiler. Artificial Neural Network (ANN) is used to train neural network modal with back propagation method to determine the best modal that will be used to predict soot blowing operation. Combination of neural network different number of neurons, hidden layers, training algorithm, and training functions is trained to find the modal with lowest error. By improving soot blowing sequence, efficiency of boiler can be improved by providing best parameter and model. This model is then used as a reference for advisory tool whereby a Neural Network Predictive Tool is suggested to the station to predict the soot blowing operation that occurs. � The authors, published by EDP Sciences, 2017.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1006
dc.identifier.doi10.1051/matecconf/201713101006
dc.identifier.scopus2-s2.0-85033217584
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85033217584&doi=10.1051%2fmatecconf%2f201713101006&partnerID=40&md5=cf7b9760696e31fe0c5782c73a75a6e9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23083
dc.identifier.volume131
dc.publisherEDP Sciencesen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleMATEC Web of Conferences
dc.titleDevelopment and implementation of Intelligent Soot Blowing Optimization System for TNB Janamanjungen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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