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Forecasting models for developing control scheme to improve furnace run length

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Listed:
  • Prasun Das
  • Arup Kumar Das
  • Saddam Hossain

Abstract

In petrochemical industries, the gaseous feedstock like ethane and propane are cracked in furnaces to produce ethylene and propylene as main products and the inputs for the other plant in the downstream. A problem of low furnace run length (FRL) increases furnace decoking and reduces productivity along with the problem of reducing life of the coil. Coil pressure ratio (CPR) and tube metal temperature (TMT) are the two most important performance measures for the FRL to decide upon the need for furnace decoking. This article, therefore, makes an attempt to develop the prediction models for CPR and TMT based on the critical process parameters, which would lead to take the necessary control measures along with a prior indication for decoking. Regression-based time series and double exponential smoothing techniques are used to build up the models. The effective operating ranges of the critical process parameters are found using a simulation-based approach. The models are expected to be the guiding principles eventually to increase the average run length of furnace.

Suggested Citation

  • Prasun Das & Arup Kumar Das & Saddam Hossain, 2009. "Forecasting models for developing control scheme to improve furnace run length," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(9), pages 1009-1019.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:1009-1019
    DOI: 10.1080/02664760902803255
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    References listed on IDEAS

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    1. Prasun Das & Sasadhar Bera, 2007. "Standardization of Process Norms in Baker's Yeast Fermentation through Statistical Models in Comparison with Neural Networks," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(5), pages 511-527.
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