Year-end Sale % OFF 
Abstract
This study presents the analysis of associated gas fueled gas turbine power plant with a view to harnessing associated gas. GASTURB performance simulation software was employed to model and simulate the design and off design performance of the various engines that made up the power plant investigated. Monte Carlo Simulation using Palisade’s @RISK software was employed to conduct the risk analysis of associated fueled gas turbine by incorporating different variables. A decline rate of -13% was applied over the 20-year period of power plant life, beginning from Year 2015. When the distribution curves for the clean and degraded conditions of DS25 engine set were compared, the plots show that the clean condition generates higher profit than the degraded condition. Also, when the clean condition for DS25 and LM6K engine sets were compared, the distribution curve plots show that the cluster of DS25 engine set generates a higher profit than the LM6K engine set.
Highlights
In the last ten years, global demand for energy has increased remarkably
Since Nigeria is one the countries that still flare gas, the best option available is to recover the energy wasted as a result of flaring and harness it for power generation
That will result in a corresponding reduction in available power from gas turbines used for power generation with AG
Summary
In the last ten years, global demand for energy has increased remarkably. This is as a result of the increased economic activities coupled with the development of the downstream sector. Since Nigeria is one the countries that still flare gas, the best option available is to recover the energy wasted as a result of flaring and harness it for power generation. When this is done, it will provide power for the rural communities were these flares are carried out, but it will greatly reduce greenhouse gas emissions. Hegde and Rokseth [1] conducted a review on publications utilizing machine learning methods to aid risk assessment in engineering systems. Artificial neutral networks were reported to be most applied machine learning method to aid risk assessment in engineering systems. A design frame work utilizing the combination of Bayesian optimization and mean objective cost of uncertainty was proposed by Imani and Ghoreishi [3], to enable the expansion of experimental design spaces and systems
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: European Journal of Engineering Research and Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
