A new method for assessing domino effect in chemical process industry

Area of Science:

• Industrial safety engineering
• Risk assessment methodologies
• Process safety management

Background:

• Traditional analytical methods struggle with the complexity and uncertainty of domino effect phenomena in industrial processes.
• Accurate forecasting of accident impacts across interconnected process units is crucial for industrial safety.
• Existing methods lack the robustness to handle cascading failures effectively.

Purpose of the Study:

• To present a novel methodology for forecasting and assessing the impact of accidents across industrial process units.
• To overcome the limitations of current analytical techniques in modeling domino effect phenomena.
• To provide a validated tool for enhanced industrial risk assessment.

Main Methods:

• Development of a new methodology based on Monte Carlo Simulation.
• Application of probabilistic modeling to simulate accident propagation.
• Validation through two distinct industrial case studies.

Main Results:

• The proposed Monte Carlo Simulation methodology effectively forecasts and assesses accident impacts on interconnected process units.
• The new approach successfully addresses the limitations of analytical methods in handling uncertainty and complexity.
• Case studies demonstrate the practical applicability and reliability of the methodology.

Conclusions:

• The Monte Carlo Simulation-based methodology offers a significant advancement in predicting industrial accident domino effects.
• This approach provides a more robust and accurate tool for process safety and risk management.
• The validated methodology can be applied to improve safety protocols and mitigate potential cascading failures in industries.