ANALYSIS OF PARTIAL STROKE TESTING FOR MASONEILAN EMERGENCY SHUTDOWN VALVE

• Main Author: AZALDIN, HAFIZ AZIZI
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi Petronas 2011
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://utpedia.utp.edu.my/6851/1/2011%20-%20Analysis%20of%20Partial%20Stroke%20Testing%20for%20Masoneilan%20Emergency%20Shutdown%20Valve.pdf; http://utpedia.utp.edu.my/6851/

This study is about the Analysis of Partial Stroke Testing for Masoneilan Emergency Shutdown Valve. This project is a collaboration between PETRONAS Skill Group 14 (SKG14) through PETRONAS Group Technical Services (GTS) and Universiti Tekuologi PETRONAS (UTP). The objectives for this project are to analyze the results obtained from Partial Stroke Test (PST) using Masoneilan ESD valves, analyze the effect of swapping the PST controller during PST experimental period and predict the breakaway pressure of ESD valves using Artificial Neural Network. In analyzing the PST for Masoneilan's ESD valve, PST data which is available in the historian were obtained. These data were based on the PST which had been done earlier for a specific time period. Later on, the data obtained will be analyzed using Microsoft Excel and MATLAB to see the PST performance. Besides, a neural network modeling also being used to predict the performance of the valve based on the data obtained from PST. The findings from PST shows that the parameter's data patterns such as friction, breakaway pressure and droop suddenly chanced starting day 54 onwards since the PST smart positioners had been swapped between ball and butterfly valves. This PST smart positioner swapping caused the analysis become inaccurate and the neural network model used to predict the breakaway pressure of the valve is unable to predict it accurately. To eliminate the influence of smart positioners swapping, the data had been divided into groups of data before the smart positioners had been swapped and the data after the smart positioners had been swapped. By doing this, the analysis become more accurate and the prediction of valve's breakaway pressure can be done by neural network modeling more accurate. As a conclusion, performing PST can help us in predicting how long the ESD valve can be used which can be as a guideline when to do the maintenance to ESD valve or replacing it.