Risk-based integrity and inspection modeling (RBIIM) of process components/system

Area of Science:

• * Engineering
• * Risk Management
• * Reliability Engineering

Background:

• * Process plants handle hazardous chemicals under extreme conditions, necessitating robust safety measures.
• * Ensuring safe and continuous operation requires diligent inspection and maintenance protocols.
• * Existing methods may not fully account for material degradation and inspection uncertainties.

Purpose of the Study:

• * To propose a risk-based methodology for integrity and inspection modeling (RBIIM).
• * To optimize inspection and replacement intervals for enhanced facility safety and reliability.
• * To develop a framework for designing effective integrity inspection plans.

Main Methods:

• * Modeled material degradation using a gamma distribution.
• * Employed Bayesian updating to refine degradation models with inspection data.
• * Incorporated perfect and imperfect inspection scenarios, including measurement error modeling.
• * Calculated risk based on probability of failure and time-dependent cost consequences.

Main Results:

• * Developed an optimal inspection and replacement interval strategy.
• * Demonstrated the methodology through case studies on autoclave and pipeline maintenance.
• * Autoclave inspection interval determined to be 19 years; pipeline inspection due in 5 years.
• * Showcased the ability to update degradation rates and failure distributions with new inspection data.

Conclusions:

• * The proposed RBIIM methodology effectively enhances the safety and reliability of process plant operations.
• * Bayesian updating with inspection results provides a dynamic approach to risk management.
• * The optimized inspection intervals contribute to proactive maintenance and risk mitigation strategies.