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Research Papers: Gas Turbines: Cycle Innovations

Risk Analysis of Gas Turbines for Natural Gas Liquefaction

[+] Author and Article Information
Raja S. R. Khan1

Department of Power and Propulsion, Cranfield University, Bedforshire MK43 0AL, UKr.s.r.khan.cranfield.ac.uk

Maria C. Lagana

Department of Power and Propulsion, Cranfield University, Bedforshire MK43 0AL, UKm.c.lagana@cranfield.ac.uk

Stephen O. T. Ogaji

Department of Power and Propulsion, Cranfield University, Bedforshire MK43 0AL, UKs.ogaji@cranfield.ac.uk

Pericles Pilidis

Department of Power and Propulsion, Cranfield University, Bedforshire MK43 0AL, UKp.pilidis.cranfield.ac.uk

Ian Bennett

Team Lead Technology - Rotating Equipment, Shell Global Solutions International, B.V., The Hague, The Netherlands

1

Corresponding author.

J. Eng. Gas Turbines Power 133(7), 071704 (Mar 21, 2011) (8 pages) doi:10.1115/1.4002673 History: Received May 10, 2010; Revised May 17, 2010; Published March 21, 2011; Online March 21, 2011

Procurement of process plant equipment involves decisions based not only on an economic agenda but also on long term plant capability, which in turn depends on equipment reliability. As the greater global community raises environmental concerns and pushes for economic reform, a tool is evermore required for a specific and critical selection of plant equipment. Risk assessments based on NASA’s Technology Readiness Level (TRL) scale have been employed in many previous risk models to map technology in terms of risk and reliability. The authors envisage a scale for quantifying the technical risk. The focus of this paper is the technical risk assessment of gas turbines as mechanical drivers for producing liquefied natural gas (LNG). This risk assessment is a cornerstone of the technoeconomic environmental and risk analysis (TERA) philosophy developed by Cranfield University’s Department of Power and Propulsion in U.K. Monte Carlo simulations are used in order to compare the risks of introducing new plant equipment against existing and established plant equipment. Three scenarios are investigated using an 87MW single spool, typical industrial machine, a baseline engine followed by an engine with increased firing temperature, and finally an engine with a zero staged compressor. The results suggest that if the baseline engine was to be upgraded, then the zero staging option would be a better solution than increasing the firing temperature since zero staging gives the lower rise in total time to repair (TTTR) or downtime. The authors suggest a scaling system based on NASA’s TRL but with modified definition criteria for the separate technology readiness levels in order to better relate the scale to gas turbine technology. The intention is to link the modified TRL to downtime, since downtime has been identified as a quantitative measure of technical risk. Latest developments of the modeling are looking at integrating risk analysis and a maintenance cost and scheduling model to provide a platform for total risk assessment. This, coupled with emissions modeling, is set to provide the overall TERA tool for LNG technology selection.

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Copyright © 2011 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

TERA framework for LNG applications

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Figure 2

The bathtub curve exemplifying the three main stages of component life

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Figure 3

Sketch of engine or component life

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Figure 4

The input file for the risk model—an example of one component

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Figure 5

The threshold between premature and random failures denoted by point A

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Figure 6

The relationship between TRL and MTBF and MTTR and TTTR

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Figure 7

A plot of TTTR distributions for TRL 9 to 5

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Figure 8

A schematic for the SSI-87 gas turbine engine configuration

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Figure 9

Variation of shaft power and thermal efficiency with ambient temperature

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Figure 10

A plot of MTBF for all three variations

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Figure 11

A plot of MTTR for all three variations

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Figure 12

A plot of TTTR for all three variations

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Figure 13

A plot of cumulative MTBF distributions for the three engines

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Figure 14

A plot of cumulative TTTR distributions for the three engines

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