A Study on the Decision Tree Approach for Predicting EO/EG Facility Leakage and Diffusion Hazards
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View abstract on PubMed
Summary
This summary is machine-generated.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.
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.
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