Methods to Improve Process Safety Performance through Flange Connection Leak Prediction and Control
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Process safety is a task of preventing leaks. Leak prevention is critical because pressure vessels and piping assets in chemical plants are fabricated from materials which have limited corrosion resistance. When corrosive compounds are processed in these assets, they may suffer degradation over time due to thinning, cracking, or loss of their material properties. This problem is partially controlled by applying a safety margin known called a corrosion allowance. The corrosion allowance is determined by predicting the asset’s expected corrosion rate and its service life. However, this fixed safety margin does not consider the inherent uncertainty in an individual asset’s degradation rate due to variability in the material’s corrosion resistance, the operating parameters of the process, and the inspection techniques used to measure the progression of corrosion damage over time. Consequently, deterministic analysis is not capable of precisely estimating an asset’s safe operating life during its design stage. One of the most likely areas for leakage to occur in process equipment is at the flange connections that join assets together. Risk analyses for planning inspections of fixed equipment and piping usually treat flanges as components of their parent asset. This thesis focuses on methods to improve prediction and control of corrosion and leakage at flange connections in particular. Flange connection seal tightness can be monitored through vibration-based Non-Destruction Testing (NDT). The data gathered from this monitoring can be used to update risk models for flange connection leakage. Hierarchical Bayesian Network methods of modeling risk are demonstrated in this thesis to be capable of predicting probability of seal failure based on the mean and variance of failure rates in a population of flange connections. This allows for prediction of the probabilities based on corrosion and leak events in the plant. The results of inspection techniques are used as inputs to this risk model, enabling probabilistic decision-making.
Structural Health Monitoring
Nelson, Jeremy (2014). Methods to Improve Process Safety Performance through Flange Connection Leak Prediction and Control. Master's thesis, Texas A & M University. Available electronically from