A support vector based CO2 gas emission prediction system for generation power plant

C. P. Chen, S. K. Tiong, F. Y.C. Albert, S. P. Koh

Research output: Contribution to journalArticle

Abstract

The work presents an intelligent system Support Vector Regression Emission Monitoring System (SuVEMS) developed for Tenaga Nasional Berhad (TNB) Sdn. Bhd. in Peninsular Malaysia for the prediction of harmful gas emissions from electricity generating power plants in Tuanku Jaafar Power Station (TJPS). The CO2, emissions is modelled on this work using Support Vector Regression (SVR), a statistical machine learning tool with a regression-based extension towards Support Vector Machines (SVMs). The gas is predicted using independent models and the gas prediction model is trained using feature subsets selected using the forward selection approach. The SuVEMS results are compared and measured the performance with the Continuous Emission Monitoring System, CEMS results. The SuVEMS results implemented at TJPS indicate that it has the ability for the online prediction with average prediction accuracy of 95%.

Original languageEnglish
Pages (from-to)4518-4522
Number of pages5
JournalAdvanced Science Letters
Volume23
Issue number5
DOIs
Publication statusPublished - May 2017

Fingerprint

Power Plants
Support Vector
power plant
Power Plant
Gas emissions
Power plants
Support Vector Regression
Gases
Monitoring System
monitoring system
Monitoring
Prediction
regression
prediction
gas
monitoring
Electricity
Malaysia
Intelligent systems
Set theory

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Health(social science)
  • Mathematics(all)
  • Education
  • Environmental Science(all)
  • Engineering(all)
  • Energy(all)

Cite this

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abstract = "The work presents an intelligent system Support Vector Regression Emission Monitoring System (SuVEMS) developed for Tenaga Nasional Berhad (TNB) Sdn. Bhd. in Peninsular Malaysia for the prediction of harmful gas emissions from electricity generating power plants in Tuanku Jaafar Power Station (TJPS). The CO2, emissions is modelled on this work using Support Vector Regression (SVR), a statistical machine learning tool with a regression-based extension towards Support Vector Machines (SVMs). The gas is predicted using independent models and the gas prediction model is trained using feature subsets selected using the forward selection approach. The SuVEMS results are compared and measured the performance with the Continuous Emission Monitoring System, CEMS results. The SuVEMS results implemented at TJPS indicate that it has the ability for the online prediction with average prediction accuracy of 95{\%}.",
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A support vector based CO2 gas emission prediction system for generation power plant. / Chen, C. P.; Tiong, S. K.; Albert, F. Y.C.; Koh, S. P.

In: Advanced Science Letters, Vol. 23, No. 5, 05.2017, p. 4518-4522.

Research output: Contribution to journalArticle

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