Abstract: Techniques for implementing Bayesian model for refining and petrochemical wall thickness monitoring are presented. The construction of a model may be automated for each piping circuit or piece of major fixed equipment, utilizing, for example, specific component data, historical thickness measurements, inspection practices and related inspection program information. The model contains nodes describing the most significant sources of variability, namely component original thicknesses, wall thickness degradation over time, corrosion rates and thickness measurement error.

Abstract: Techniques for implementing Bayesian model for refining and petrochemical wall thickness monitoring are presented. The construction of a model may be automated for each piping circuit or piece of major fixed equipment, utilizing, for example, specific component data, historical thickness measurements, inspection practices and related inspection program information. The model contains nodes describing the most significant sources of variability, namely component original thicknesses, wall thickness degradation over time, corrosion rates and thickness measurement error.

Abstract: Techniques for implementing Bayesian model for refining and petrochemical wall thickness monitoring are presented. The construction of a model may be automated for each piping circuit or piece of major fixed equipment, utilizing, for example, specific component data, historical thickness measurements, inspection practices and related inspection program information. The model contains nodes describing the most significant sources of variability, namely component original thicknesses, wall thickness degradation over time, corrosion rates and thickness measurement error.

Abstract: Techniques for implementing Bayesian model for refining and petrochemical wall thickness monitoring are presented. The construction of a model may be automated for each piping circuit or piece of major fixed equipment, utilizing, for example, specific component data, historical thickness measurements, inspection practices and related inspection program information. The model contains nodes describing the most significant sources of variability, namely component original thicknesses, wall thickness degradation over time, corrosion rates and thickness measurement error.

Abstract: A method of selective corrosion rate analysis is shown for generating an inspection and replacement schedule for a fluid processing plant. The method includes the steps of identifying a system from a division of the plant into one or more systems wherein each system has mechanical components that are expected to experience a common corrosion environment, and wherein each system has at least one thickness measurement location for performing thickness inspections. The next steps are establishing system corrosion data for each thickness measurement location and running the data through a plurality of corrosion engineering models. Running the corrosion engineering models includes coordinating the system corrosion data of the thickness measurement locations into a plurality of physical relationship data groups, and applying a plurality of statistical distribution/goodness-of-fit tests to each physical relationship data group.