ANALYSIS OF PARTIAL STROKE TESTING FOR MASONEILAN EMERGENCY SHUTDOWN VALVE

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.