No abstract

Purpose -The purpose of this paper is to propose a quantitative model for risk-based maintenance and remaining life assessment for gas turbines. Design/methodology/approach -The proposed model uses historical failure and repair data from the operation of gas turbines. The time to failure of gas turbines is modeled using Weibull distribution. Findings -The total risk is estimated considering replacement cost, repair cost, operation cost, risk of failure and turbine remaining value after a specified period of time.Originality/value -The model is an effective tool to make optimal decisions regarding maintenance strategy (repair or replacement) and to assess the remaining life based on a comparison of the total risk. The literature review focusses on developing different models to make risk-based decisions regarding the selection of a maintenance strategy and maintenance interval, however, literature is silent regarding risk-based assessment of the equipment remaining life, which is the focus of present work. The model is tested and applied to ageing gas turbines in a cross-country pipeline.

Equipment sizing decisions in the Oil and Gas Industry often have to be made based on incomplete data. Often, the exact process conditions are based on numerous assumptions about well performance, market conditions, environmental conditions and others. Since the ultimate goal is to meet production commitments, the traditional way of addressing this is, to use worst case conditions, and often adding margins onto these. This will invariably lead to plants that are oversized, in some instances by large margins. In reality, the operating conditions are very rarely the assumed worst case conditions, but they are usually more benign most of the time. Plants designed based on worst case conditions, once in operation, will therefore usually not operate under optimum conditions, have reduced flexibility, and therefore cause both higher capital expenses and operating expenses. The authors outline a new probabilistic methodology that provides a framework for more intelligent process-machine designs. A standardized framework using Monte Carlo simulation and risk analysis is presented that more accurately defines process uncertainty and its impact on machine performance. Case studies are presented that highlight the methodology as applied to critical turbo-machinery.

No abstract

Acid gas removal is a critical process step in natural gas processing and syngas production for ammonia and other uses. Application of a liquid phase turbocharger to the acid gas removal unit (AGRU) results in significant energy savings and improvement to reliability, availability and maintainability (RAM) of the plant. This paper describes conventional configurations with high pressure pumps and new configurations utilizing liquid phase turbochargers. Design of the equipment, process operations and controls and reliability analysis are included. The results of a RAM study comparing conventional configurations to those incorporating liquid phase turbochargers in multiple cases are also presented. From the RAM study, it can be concluded that flow sheet configurations that include a liquid phase turbocharger consistently provide lower plant downtime and maintenance costs as compared with conventional flow sheet configurations. This is in addition to the energy savings that result from energy recovery with the application of the liquid phase turbocharger to the AGRU. For the reference plant used in the study, the maintenance cost savings are as great as $2.5M over the 20 year lifetime of the plant and average annual downtime reduction is as much as 19.8 hours.