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TECHNICAL PAPERS

# Nonisothermal Compressor Station Optimization

[+] Author and Article Information
M. Abbaspour, K. S. Chapman

National Gas Machinery Laboratory, Kansas State University, Manhattan, KS 66502

P. Krishnaswami

Department of Mechanical and Nuclear Engineering, Kansas State University, Manhattan, KS 66502

J. Energy Resour. Technol 127(2), 131-141 (Sep 14, 2004) (11 pages) doi:10.1115/1.1871248 History: Received July 16, 2004; Revised September 14, 2004

## Abstract

A detailed mathematical model of compressor stations and pipes is essential for optimizing the performance of the gas pipeline system. Most of the available literature on compressor station modeling is based on isothermal solutions for pipe flow, which is inadequate for our purposes. In the present work, the pipe flow is treated as nonisothermal unsteady one-dimensional compressible flow. This is accomplished by treating the compressibility factor as a function of pressure and temperature, and the friction factor as a function of Reynolds number. The solution method is the fully implicit finite difference method that provides solution stability, even for relatively large time steps. The compressors within the compressor station are modeled using centrifugal compressor map-based polynomial equations. This modeling technique permits the designation of different models of compressors in the compressor station. The method can be easily extended to include other types of compressors. Using this mathematical model as a basis, a nonlinear programing problem (NLP) is set up wherein the design variables are the compressor speeds and the objective function to be minimized is the total fuel consumption. The minimum acceptable throughput is imposed as a constraint. This NLP is solved numerically by a sequential unconstrained minimization technique, using the mathematical model of the system for the required function evaluations. The results show that this approach is very effective in reducing fuel consumption. An application of this methodology for selecting the number of compressors to be shut down for the most fuel-efficient operation is also presented. Our results further indicate that station-level optimization produces results comparable to those obtained by network-level optimization. This is very significant because it implies that the optimization can be done locally at the station level, which is computationally much easier.

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## Figures

Figure 6

Fuel consumption at compressor station

Figure 7

Pressure distribution for inlet pipe to compressor station with different type of compressor

Figure 8

Pressure distribution for outlet pipe from compressor station with different type of compressor

Figure 9

Temperature distribution for inlet pipe to compressor station with different type of compressor

Figure 10

Temperature distribution for outlet pipe from compressor station with different type of compressor

Figure 11

Change of power with respect to time

Figure 12

Variation of head with respect to flow rate

Figure 13

Compressor station case 2

Figure 14

Two compressor stations case 4

Figure 1

Schematic of compressor and fuel consumption

Figure 2

Compressor map (9)

Figure 3

Schematic of compressor station (case 1)

Figure 4

Mass flow rate for inlet pipe to compressor station

Figure 5

Mass flow rate for outlet pipe from compressor station

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