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51318-11412-Risk Based Inspection Utilizing Monte Carlo Simulation

This paper expands on this work by first reviewing the basis for utilizing Monte Carlo simulation to predict future corrosion in fixed equipment.

Product Number: 51318-11412-SG
Author: Chris Hurd / Joseph Nunez / Frank Sapienza
Publication Date: 2018
$0.00
$20.00
$20.00

Industry research over the last decade has demonstrated the potential for numerical simulation, such as Monte Carlo, to project future corrosion of fixed equipment in refineries and chemical plants. This research has shown the potential for numerical simulation to provide metallurgists and inspection engineers advanced tools in managing fixed equipment in refineries and chemical plants. This paper expands on this work by first reviewing the basis for utilizing Monte Carlo simulation to predict future corrosion in fixed equipment. This paper then explores implementing Monte Carlo simulation based on the quality of inspection data by proposing methods for high-quality, statistically-based inspection data as well as historic, non-statistically-based data. This paper proposes how to incorporate the simulation results into a risk based inspection plan aligned with the philosophy of industry risk based inspection practices.

Key words: Monte Carlo, simulation, RBI, POF

Industry research over the last decade has demonstrated the potential for numerical simulation, such as Monte Carlo, to project future corrosion of fixed equipment in refineries and chemical plants. This research has shown the potential for numerical simulation to provide metallurgists and inspection engineers advanced tools in managing fixed equipment in refineries and chemical plants. This paper expands on this work by first reviewing the basis for utilizing Monte Carlo simulation to predict future corrosion in fixed equipment. This paper then explores implementing Monte Carlo simulation based on the quality of inspection data by proposing methods for high-quality, statistically-based inspection data as well as historic, non-statistically-based data. This paper proposes how to incorporate the simulation results into a risk based inspection plan aligned with the philosophy of industry risk based inspection practices.

Key words: Monte Carlo, simulation, RBI, POF

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