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A Study on the Decision Tree Approach for Predicting EO/EG Facility Leakage and Diffusion Hazards.

Qi Cao1,2, Zhi Zhang1,2, Yuntao Mu1,2,3

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A new decision tree model accurately predicts ethylene oxide (EO) and ethylene glycol (EG) leakage dispersion over large areas. This rapid prediction aids in preventing severe petrochemical accidents.

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Area of Science:

  • Petrochemical safety engineering
  • Chemical process hazard analysis
  • Environmental risk assessment

Background:

  • Ethylene oxide (EO) and ethylene glycol (EG) are vital petrochemicals.
  • EO/EG facilities face significant fire and explosion risks, leading to severe consequences.
  • Accurate prediction of wide-area accident consequences is crucial for risk management.

Purpose of the Study:

  • To develop a rapid prediction model for wide-area accidents involving ethylene oxide and ethylene glycol.
  • To assess the consequences of leakage scenarios in EO/EG facilities.
  • To enhance risk prevention and control strategies for the petrochemical industry.

Main Methods:

  • Decision tree theory was applied to develop a prediction model.
  • Ninety leakage scenarios for EO/EG facilities were defined and simulated using FLACS software.
  • Eighty datasets of gas-phase concentration distribution data were used for training and validation (60/20 split).

Main Results:

  • The decision tree model accurately predicted EO/EG leakage dispersion over areas exceeding 1 km².
  • Prediction accuracy of the developed model surpassed 90%.
  • The model demonstrated rapid prediction capabilities for dispersion patterns and concentration distributions.

Conclusions:

  • The study successfully achieved rapid prediction of EO/EG leakage dispersion using limited simulation data.
  • The developed model provides critical support for risk prevention and control in EO/EG facilities.
  • This approach enhances the ability to manage potential wide-area accidents in the petrochemical sector.