The activity of Life-Cycle Cost (LCC) forecasting using Reliability, Availability, and Maintainability (RAM) modeling techniques is not new, but there have been recent developments which have resulted in a fundamental change in the usefulness of LCC forecasting to designers, owners, operators, and maintainers of power plants. These changes provide the means for optimizing Operation and Maintenance (O&M) activities many years in advance with a high degree of accuracy. The primary changes have been advances in technology and the introduction of Monte Carlo-based discrete event simulation technology to perform RAM forecasting. Simulation based LCC forecasting can be used to determine the optimum operating and maintenance support scenarios. Main areas of optimization include the initial and through life cost, spares inventory holding, maintenance scheduling, logistics, etc. By allowing various scenarios to be explored in a simulation environment, LCC forecasting provides an accurate and cost effective method for optimizing costs — an activity with a very high Return On Investment (ROI) value proposition. This paper describes the methodology undertaken and the results that can be obtained from the application of automated, simulation-based LCC forecasting technology to the analysis of gas turbine based generating units.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.