A Bayesian approach integrating real-time monitoring data for the estimation of a dynamic RUL of equipment affected by sulfidation and naphthenic acids corrosion

Abstract

Sulfidation and naphthenic acids corrosion is a common mechanism in processing crude oils with high sulphur content. This research addresses the need for reliable corrosion management strategies in chemical industry in an energy transition context and simultaneously affected by emerging scenarios (economic, geopolitical, etc.). The objective is to develop a dynamic model for estimating the remaining lifetime of equipment, using predictive modelling techniques and monitoring data of process variables that influence the deterioration mechanism. The developed model supports the prevention of negative consequences due to the equipment deterioration and the optimisation of inspection and maintenance programs. By offering an innovative tool for the corrosion management, this research aims to contribute to ongoing efforts improving the efficiency and sustainability of energy production processes. Due to some emerging scenarios, refineries could be oriented to increase the processing of raw materials with high content of sulphur; these are more accessible and economical, but accelerate the deterioration of equipment materials and, therefore, increase the risk. The use of the method, proposed in this article, allows for more effective management of safety through the monitoring of the conditions of critical items.