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

Taneshwaren Sundaram, Firas Basim Ismail, Prem Gunnasegaran, Pogganeswaren Gurusingam

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

With 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.

Original languageEnglish
Article number01006
JournalMATEC Web of Conferences
Volume131
DOIs
Publication statusPublished - 25 Oct 2017
Event2017 UTP-UMP Symposium on Energy Systems, SES 2017 - Perak, Malaysia
Duration: 26 Sep 201727 Sep 2017

Fingerprint

Soot
Blow molding
Boilers
Neural networks
Coal
Backpropagation
Neurons
Power generation
Cleaning
Power plants
Computational fluid dynamics

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Materials Science(all)
  • Engineering(all)

Cite this

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title = "Development and implementation of Intelligent Soot Blowing Optimization System for TNB Janamanjung",
abstract = "With 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.",
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Development and implementation of Intelligent Soot Blowing Optimization System for TNB Janamanjung. / Sundaram, Taneshwaren; Ismail, Firas Basim; Gunnasegaran, Prem; Gurusingam, Pogganeswaren.

In: MATEC Web of Conferences, Vol. 131, 01006, 25.10.2017.

Research output: Contribution to journalConference article

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