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Research Papers: Gas Turbines: Industrial & Cogeneration

Selection of Climatic Design Points for Gas Turbine Power Augmentation

[+] Author and Article Information
Mustapha Chaker

 Bechtel Corporation, Houston, TXmchaker@bechtel.com

Cyrus B. Meher-Homji

 Bechtel Corporation, Houston, TXcmeherho@bechtel.com

In compressor drive applications such as refrigeration, designing for a wide range of ambient temperatures can be challenging especially due to power limitations of the drivers on hot days when the refrigeration compressor condensing discharge pressure increases.

A compressor inlet temperature of 15C has been assumed.

The effectiveness may diminish at off design conditions.

There are several considerations other than just calculating the intake temperature static depression caused by air acceleration to Mach numbers of 0.5 to 0.8. There is also some heating (although small — of the order of 1 °C) due to the condensation that occurs and also due to heat transfer from the number one bearing, etc.

Aeroderivative engines typically operate at higher inlet Mach numbers resulting in higher inlet temperature depressions.

All based on TMY data.

This approach is very data intensive with file sizes exceeding 60 MB.

To avoid icing at the compressor bell mouth.

J. Eng. Gas Turbines Power 134(4), 042001 (Jan 25, 2012) (14 pages) doi:10.1115/1.4004436 History: Received April 08, 2011; Accepted June 16, 2011; Published January 25, 2012; Online January 25, 2012

There is a widespread interest in the application of gas turbine power augmentation technologies such as evaporative cooling and mechanical chilling in the mechanical drive and power generation markets. Very often, the selection of the design point is based on the use of American Society of Heating and Refrigeration Engineers (ASHRAE) data or a design point that is in the basis of design for the project. This approach can be detrimental and can result in a non optimal solution. In order to evaluate the benefits of power augmentation, users can use locally collected weather data, or recorded hourly bin data set from databases such as typical meteorological year (TMY), engineering weather data (EWD), and integrated weather surface (IWS). This paper will cover a suggested approach for the analysis of climatic data for power augmentation applications and show how the selection of the design point can impact performance. The final selection of the design point depends on the specific application, the revenues generated and installation costs. To the authors’ knowledge, this is the first attempt to treat this topic in a structured analytical manner by comparing available database information with actual climatic conditions.

Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Daily variation of dry bulb, wet bulb temperatures, relative humidity and resulting wet bulb depression

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Figure 2

Corresponding power degradation for two different types of gas turbines during hot day

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Figure 3

Variation of DBT, WBT, RH and resulting WBD during the week of July 27, 2007

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Figure 4

Corresponding power degradation for different types of gas turbines

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Figure 5

Increase of energy prices during peak demand (Profile B) [4]

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Figure 6

Database showing hourly bin data of DBT versus RH for one year

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Figure 7

GT Output power for different combinations of RH and DBT with media evaporative cooling (evap cooler eff = 90%)

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Figure 8

Variations in refrigeration tonnage for inlet chilling of a LM6000PF for varying RH for three ambient temperatures

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Figure 9

Variation of required amount of water to saturate the air as function of dry bulb temperature (GT airflow = 83 Kg/sec)

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Figure 10

Variation of the wet bulb depression as function of dry bulb temperature and relative humidity for the fixed amount of water of 40.88 LPM (GT airflow = 83 Kg/sec)

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Figure 11

(a) Water flow required (liter/minute) for different wet bulb depressions for varying gas turbine air flow rates. (b) power boost for varying amount of water fog including overspray for selected gas turbines.

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Figure 12

Power boost versus amount of inlet air cooling for varying DBT and RH (cooling to saturation)

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Figure 13

Power Bboost versus amount of inlet air cooling for varying DBT and RH (1% overspray)

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Figure 14

Inlet chilling tonnage and power consumed for 6 X LM2500+ gas turbines with varying RH (Tamb  = 34 °C)

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Figure 15

Representation of ECDH over 12 months by daily period of 3 hs

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Figure 16

Representation of ECDH at different time of the day as function of wet bulb depression in increments of 1°F (0.56 °C)

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Figure 17

ECDH for different location within the houston area (sites within 50-80 Km)

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Figure 18

ECDH for the same location based on different weather databases and for a MWBT of 7.2 °C

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Figure 19

ECDH for the same location based on different weather databases and for a MWBT of 10 °C

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Figure 20

Selection of Ddesign Ppoint for gas turbine inlet air cooling system (evaporative cooling) showing insensitivity to evaporative cooler evaporative efficiency. All DBT values shown are greater than 41.6 °C.

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Figure 21

Selection of design point for gas turbine inlet air cooling system (evaporative cooling) showed with actual site data, DBT values shown greater than 35 °C

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Figure 22

Comparison of data base data with actual site measured data for a warm and humid region (a) monthly ECDH with MWBT of 10 °C and (b) CCDH with CTIT of 10 °C

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Figure 23

Yearly and % ECDH and CCDH with MWBT and CTIT of 10 °C for a warm and humid region

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Figure 24

Yearly occurrence in % and hours for temperatures above 10 °C for wet compression for a warm and humid region

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Figure 25

ECDH and CCDH as function of MWBT and CTIT respectively for different databases for a warm and humid region.

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Figure 26

Comparison of data base data with actual site measured data for a hot and dry region (a) monthly ECDH with MWBT = 10 °C and (b) CCDH with CTIT of 10 °C

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Figure 27

Yearly and % ECDH and CCDH with MWBT and CTIT of 10 °C respectively for a hot and dry region

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Figure 28

Yearly occurrence in % and hours greater than 10 °C for wet compression with for a hot and dry region

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Figure 29

ECDH and CCDH as function of MWBT and CTIT for different databases for a hot and dry region

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