Thickness Probabilistic and Deterministic Prediction Models for Enhancing Risk-Based Inspection for Structure Integrity and Failure Prevention

Abstract This study presents the development of a comprehensive thickness prediction framework to enhance risk-based inspection (RBI) for pressure vessels. The research focuses on recommending essential maintenance strategies to maintain structural integrity and prevent failures using a systematic reliability assessment approach. Five predictive models were employed: Bayesian, exponential, linear, power, and quadratic, validated using historical thickness data collected between 2002 and 2008, with projections extended to 2040. The results demonstrated that the power model achieved the highest predictive performance with a coefficient of determination (R2) of 0.9394, while the exponential model showed strong reliability with an R2 of 0.8982 and the lowest mean absolute error (MAE) of 0.1117 ± 0.0473 mm. The Bayesian model excelled in uncertainty quantification, achieving an R2 of 0.8500 and an MAE of 0.1141 ± 0.0480 mm. Safety and risk assessments conducted for critical vessel sections (E head 1, nozzle A1, nozzle N4, and shell 1) identified nozzle N4 as requiring immediate attention, with power and quadratic models predicting failure conditions by 2040. Shell 1 demonstrated excellent safety margins, allowing for extended inspection intervals, while nozzle A1 showed lower safety margins, requiring more frequent monitoring. The integration of probabilistic and deterministic predictions into probability of failure (PoF) calculations significantly improved inspection interval optimization and enhanced risk management effectiveness. This framework enables proactive integrity management strategies, potentially achieving cost savings of 20–50% through efficient resource utilization while establishing a robust foundation for improved safety and regulatory compliance in chemical plant operations.