Dynamic risk assessment of a coal slurry preparation system based on the structure-variable Dynamic Bayesian Network

Area of Science:

• Industrial Safety
• Risk Management
• Bayesian Networks

Background:

• Coal slurry preparation systems face significant safety challenges.
• Effective safety management requires robust risk assessment methodologies.
• Dynamic analysis is crucial for understanding evolving system risks.

Purpose of the Study:

• To establish a dynamic risk assessment method for coal slurry preparation systems.
• To integrate bow-tie (BT) models with Structure-variable Dynamic Bayesian Networks (SVDBN).
• To identify key risk factors and weak links for targeted safety interventions.

Main Methods:

• Transformation of BT models into static Bayesian Network (BN) models using SVDBN algorithms.
• Deduction of risk factors and preventive measures using Python programming.
• Simulation and comparison with GeNIe software for validation.

Main Results:

• The proposed method accurately assesses system failure rates over time.
• Maintenance factors were shown to significantly increase system reliability.
• Simulation results closely matched those from established software (GeNIe).

Conclusions:

• The developed dynamic risk assessment method provides a theoretical basis for enhancing coal slurry system safety.
• The integration of BT and SVDBN models offers a powerful tool for risk analysis.
• Consideration of maintenance factors is vital for optimizing system reliability and safety.