Development of dynamic models to estimate ice accumulation rate and risk-based inspection interval

Abstract

Evolving process operations in harsh environments and stringent safety regulations have created increasing complexity in the assessment and management of risks. This demands an advanced approach to monitor and manage the process system's risk profile. This thesis presents two contributions: i)a model for dynamic risk-based inspection planning, and ii)a predictive model for the ice-accumulation rate for operations in extremely cold conditions. The traditional risk-based inspection (RBI) guidelines assume that the system is safe for operation throughout the planned interval. This assumption has led to several unfortunate accidents. A novel dynamic RBI model has been proposed in this study to monitor the system’s degradation rate and estimate its impact on the risk profile. The results were compared with the risk profile obtained industrial guideline: API-581. It is demonstrated that the use of this framework would provide a better understanding and monitoring of the system's risk. This will help to plan for optimal inspection intervals rendering the maximum cost savings possible while ensuring the system’s safety. A new model is developed to predict the ice accumulation rate on sea vessels and offshore rigs operating in harsh and cold regions. This model use Bayesian approach to predict the icing rate. It be easily be applied to a wide range of vessels and rigs and can include several parameters. The model was tested and validated using an experimental setup designed to simulate the spray-icing observed on sea vessels. It was concluded that the ice accumulation rate predicted using the proposed model was reasonably close to the values observed in the experiment.