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Research Papers: Gas Turbines: Industrial & Cogeneration

A Novel Methodology for Optimal Design of Compressor Plants Using Probabilistic Plant Design

[+] Author and Article Information
Rainer Kurz

Solar Turbines Incorporated,
San Diego, CA 92123
e-mail: rkurz@solarturbines.com

J. Michael Thorp

Aramco Services Company,
Houston, TX 77020
e-mail: joseph.thorp@aramcoservices.com

Erik G. Zentmyer

Solar Turbines Incorporated,
San Diego, CA 92123
e-mail: zentmyer_erik_g@solarturbines.com

Klaus Brun

Southwest Research Institute,
San Antonio, TX 77087
e-mail: klaus.brun@swri.org

Contributed by the Industrial and Cogeneration Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 1, 2013; final manuscript received July 12, 2013; published online September 17, 2013. Editor: David Wisler.

J. Eng. Gas Turbines Power 135(11), 112001 (Sep 17, 2013) (8 pages) Paper No: GTP-13-1217; doi: 10.1115/1.4025069 History: Received July 01, 2013; Revised July 12, 2013

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 method 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, however, 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 and operating expenses.

The authors outline a new probabilistic methodology that provides a framework for more intelligent process-machine designs. A standardized framework using a 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 turbomachinery.

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References

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Figures

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Fig. 1

Metamodel for machinery [3]

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Fig. 2

Maximum process-machine variance

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Fig. 3

Monte Carlo simulation of a physical system [11]

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Fig. 4

(a) Nominal site available power for the new gas turbine driver and (b) engine part load efficiency

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Fig. 5

Impact of the pipe roughness on the inlet pressure

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Fig. 6

(a) Probability of meeting the flow commitment (positive power margin) in year 4, probabilistic design, 0% power margin. (b) Probability of meeting the flow commitment (positive power margin) in year 4, probabilistic design, 5% power margin. (c) Probability of meeting the flow commitment (positive power margin) in year 4, probabilistic design, 10% power margin.

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Fig. 7

(a) Fuel consumption of the probabilistic selection engine. (b) Fuel consumption of the probabilistic selection engine with 5% additional margin. (c) Fuel consumption of the probabilistic selection engine with 10% additional margin.

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Fig. 8

Fuel consumption of the traditional selection engine, based on the probabilistic variation of the operating conditions

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Fig. 9

Flow capability and fuel consumption for designs based on different power margins, in the 4th year of operation

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