Abstract
There are several Key Performance Indicators (KPI’s) which allow a judgment whether a Risk Based Inspection (RBI) Assessment & Implementation project was a success or not. One of these KPI’s is definitively the qualification, experience and seniority of the person who is working on the determination of the expected deterioration mechanisms, the prediction when and where a corrosion or other materials failure of equipment or piping system is going to occur.
The presentation describes a Knowledge Management Tool which assists materials and corrosion engineers in the prediction of process equipment based upon a mighty corrosion database and state-of-the-art artificial neuronal network technology. The reliability of results achieved certainly depends on the amount of failure relevant information gathered in the database. Corrosion Management Prediction Tool provides information about the quality of the corrosion prognosis, e.g. the confidence level of the results achieved.
The system architecture allows that all parameters a materials & corrosion engineer can think of, can be transformed into a query which will be processed by a in “problem specific network/expert”. There are such experts for the prediction of materials behavior in all kinds of chemical process streams. They are stored in a central database for future application to provide corrosion expertise in the defined areas of corrosion systems.
Beside RBI projects the software has also successfully been used to optimize existing materials and alloys or significantly lower the necessary efforts for new metallic materials developments.
