The purpose of this study is to highlight the importance of reliability and life data analysis in manufacturing facility which allows the maintenance and reliability professionals to analyse the equipment breakdown pattern and help decide the appropriate maintenance strategy. This study is conducted in one of the manufacturing plants based in Jubail, Saudi Arabia. They experienced production loss and high maintenance cost due to pump failure. The existing maintenance strategy is preventive maintenance (time based maintenance) and the plant experienced failure even after performing the routine maintenance activities. The challenge here is to investigate why the components of pump are failing after performing maintenance activities and to find the reason for high maintenance cost. A set of critical pumps are identified which have failed multiple times during past six years from the list of many operational pumps. The identification of pumps is completed with the help of existing asset criticality ranking system and the time to failure data is collected from the past six years to perform the reliability and life data analysis. The analysis started by determining the failure modes of each pump .The highest number of failures are attributed to the failure of pump seals, bearings, and impellers. The Weibull analysis helps to determine the reliability and its parameters (beta and eta). The shape parameter beta is represents the failure rate behaviour i.e. if beta is < 1, failure rate decreases with time, if beta is equal to 1, failure rate is constant, and it beta is > 1, failure rate increase with time and the eta is the scale parameter. After performing the reliability and life data analysis on the component of a pumps, it’s found that the pump components have different failure pattern i.e. seals and bearing are having infant mortality or early failure whereas impeller is having random failure. Based on this outcome of reliability & life data analysis, recommended maintenance task for seals and bearing is predictive maintenance techniques such as maintenance procedure enhancement, developing the knowledge and skill set of maintenance crew etc. and for impeller is run to failure. The reason for high maintenance cost is due to the fact the unplanned maintenance cost is usually 30% higher than the planned maintenance cost and also the maintenance crew was preforming time based actions on random failure mode which is not adding any value but increases the maintenance cost.

This paper present a comprehensive review of different processes available and suitable for removal of CO2 from natural gas to meet LNG production specifications and explore the capability of Aspen HYSYS 8.6 process simulator to predict the CO2 removal process operating conditions range at which hydrocarbon and chemical (amine solvent) loss can be minimized. Removal of CO2 from natural gas is currently a global issue. Apart from meeting the customer‟s contract specifications and for successful liquefaction process in liquefied natural gas (LNG) plants, it is also a measure for reducing global CO2 emission. The simulation program developed in this research work has been used to modify the physical, thermodynamics and transport properties of the gas streams and process units so as to improve process efficiency and environmental performance. It was observed that as the concentration of amine increases, the percentage mole concentration of CO2 in the sweet gas decreases. This is attributed to the increase in amine solvent capacity with increase in concentration of amine in the solution. Also the mole percent of CO2 in the sweet gas increased with contactor (absorber) operating temperature and decreased with increase in pressure, while the hydrocarbon (C1) coabsorbed with the CO2 in the solvent increased with pressure and decrease with increase in temperature. The simulated results showed that for a given partial pressure of CO2 in the feed gas the amine loading increased with increasing amine wt% in the solution: with 25 wt%, 30 wt% and 35 wt% DEA, the loading [molCO2/molDEA] ranged from 0.6067 – 0.6369, 0.5316 – 0.5529 and 0.4638 – 0.4832 respectively.