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Process Compressor Performance Monitoring
The performance of process compressors are of paramount importance as any performance deterioration results in increased energy/fuel consumption whilst increasing emissions such as CO2 and NOx. Additionally maximum capacity can be affected due to an increased power demand required to carry out a given compression duty. This can result in lost production and even penalties due to failure to meet production targets. Faults affecting performance may remain hidden and eventually result in compressor failure. This will reduce availability and lost revenues while increasing maintenance costs. The resultant increase in revenue and increased life cycle cost will reduce profitability.

Typical faults affecting compressor performance are:
Fouling
Rubs
Vibration
Seal Wear & Damage/Balance Piston Leakage
Start-up Problems
Poor Engine Controls
FOD & DOD
Erosion
Corrosion

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GPAL gas compressor performance monitoring system (XCOMP) can continuously monitor the compressor performance deterioration using fault indices. This is similar to GPAL gas turbine performance monitoring system (XPGTn). However, the accurate modeling of thermophysical properties of real gases (process gases such as natural gas) are considered where compressor characteristics are now dependent on compressor suction conditions and gas composition. This is illustrated in Figure 1 where the compressor characteristics are shown for two different suction conditions and gas compositions. The ability for XCOMP to predict accurately the compressor surge line due to changes in suction conditions and gas composition makes XCOMP a powerful tool in compressor antisurge control systems

When gas compostion is unknown XCOMP can use (on-line) measured densities to performacne monitor process compressors and can be a useful check the validity of chromatograp outputs.

When XCOMP and XPGTn (http://www.gpal.co.uk/xpgt3.htm) outputs are incorporated with GASCOMP http://www.gpal.co.uk/gascomp.htm GASCOMP can optimize fuel/energy consumption and predict maximum production capacity thus making a powerful gas compression production management system.




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