Dynamic Bayesian network-based risk assessment for gas treatment station.

Area of Science:

• Chemical Engineering
• Risk Management
• System Safety

Background:

• Gas processing plants face significant risks of catastrophic accidents and economic losses.
• Conventional Event Tree (ET) analysis is widely used but has limitations in handling uncertainty and dynamic event sequences.
• Existing methods struggle to adequately represent the interplay and temporal progression of safety barriers.

Purpose of the Study:

• To propose and validate a Dynamic Bayesian Network (DBN) framework for advanced risk assessment in gas processing.
• To overcome the static nature and uncertainty limitations of traditional Event Tree (ET) models.
• To provide a unified analytical framework for modeling event dependencies and safety barrier performance over time.

Main Methods:

• Development of a Dynamic Bayesian Network (DBN) model to represent accident scenarios and safety barriers.
• Incorporation of temporal progression and probabilistic updating within the DBN framework.
• Application and validation of the DBN model through a case study of a gas processing treatment unit.

Main Results:

• The DBN framework effectively models complex accident sequences and the dynamic interactions of safety barriers.
• Continuous updating of probabilities allows for more accurate and timely risk assessment.
• The DBN approach demonstrated superior flexibility and depth compared to traditional ET methods in the case study.

Conclusions:

• Dynamic Bayesian Networks offer a more robust and flexible approach to risk assessment in gas processing plants.
• The DBN framework facilitates unified management and evaluation of safety barriers, enabling adaptive risk treatment.
• This study provides valuable guidance for enhancing safety and security in gas processing systems through improved risk-informed decision-making.