Accurate monitoring of gas turbine performance is a means to an early detection of performance deviation from the design point and thus to an optimized operational control. In this process, the diagnosis of the combustion process is of high importance due to strict legal pollution limits as aging of the combustor during operation may lead to an observed progression of NOx emissions. The method presented here features a semi-empirical NOx formulation incorporating aging for the GT24/GT26 heavy duty gas turbines: Input parameters to the NOx-correlation are processed from actual measurement data in a simplified gas turbine model. Component deterioration is accounted for by linking changes in air flow distribution and control parameters to specific operational measurements of the gas turbine. The method was validated on three different gas turbines of the GE GT24/GT26 fleet for part- and baseload operation with a total of 374,058 long-term data points (5 min average), corresponding to a total of 8.5 years of observation, while only commissioning data were used for the formulation of the NOx correlation. When input parameters to the correlation are adapted for aging, the NOx prediction outperforms the benchmark prediction method without aging by 35.9, 53.7, and 26.2% in terms of root mean square error (RMSE) yielding a root-mean-squared error of 1.27, 1.84, and 3.01 ppm for the investigated gas turbines over a three-year monitoring period.

References

References
1.
Palmé
,
T.
,
Liard
,
F.
, and
Cameron
,
D.
,
2014
, “
Hybrid Modeling of Heavy Duty Gas Turbines for On-Line Performance Monitoring
,”
ASME
Paper No. GT2014-26015.
2.
Güthe
,
F.
,
Gassner
,
M.
,
Bernero
,
S.
,
Meeuwissen
,
T.
, and
Wind
,
T.
,
2016
, “
Chemical Kinetic Models for Enhancing Gas Turbine Flexibility: Model Validation and Application
,”
ASME
Paper No. GT2016-57223.
3.
Andreini
,
A.
, and
Facchini
,
B.
,
2004
, “
Gas Turbines Design and Off-Design Performance Analysis With Emissions Evaluation
,”
ASME J. Eng. Gas Turbines Power
,
126
(
1
), pp.
83
91
.
4.
Held
,
T.
,
Mueller
,
M.
,
Li
,
S.
, and
Mongia
,
H.
,
2001
, “
A Data-Driven Model for NOx, CO and UHC Emissions for a Dry Low Emissions Gas Turbine Combustor
,”
AIAA
Paper No. 2001-3425.
5.
Allaire
,
D. L.
,
Waitz
,
I. A.
, and
Willcox
,
K. E.
,
2007
, “
A Comparison of Two Methods for Predicting Emissions From Aircraft Gas Turbine Combustors
,”
ASME
Paper No. GT2007-28346.
6.
Gokulakrishnan
,
P.
,
Fuller
,
C. C.
,
Joklik
,
R. G.
, and
Klassen
,
M. S.
,
2012
, “
Chemical Kinetic Modeling of Ignition and Emissions From Natural Gas and LNG Fueled Gas Turbines
,”
ASME
Paper No. GT2012-69902.
7.
Lamont
,
W. G.
,
Roa
,
M.
, and
Lucht
,
R. P.
,
2014
, “
Application of Artificial Neural Networks for the Prediction of Pollutant Emissions and Outlet Temperature in a Fuel-Staged Gas Turbine Combustion Rig
,”
ASME
Paper No. GT2014-25030.
8.
Danis
,
A. M.
,
Pritchard
,
B. A.
, and
Mongia
,
H. C.
,
1996
, “
Empirical and Semi-Empirical Correlation of Emissions Data From Modern Turbopropulsion Gas Turbine Engines
,”
ASME
Paper No. 96-GT-86.
9.
Swanson
,
B.
,
2008
, “
A Cost Effective Advanced Emissions Monitoring Solution for Gas Turbines: Statistical Hybrid Predictive System That Accurately Measures Nitrogen Oxides, Carbon Monoxide, Sulfur Dioxide, Hydrocarbon and Carbon Dioxide Mass Emission Rates
,”
ASME
Paper No. GT2008-50401.
10.
Simon
,
E.
,
Palmer
,
J.
, and
Swanson
,
B.
,
2016
, “
Type Models Implemented as Statistical Hybrid Emission Monitors for Like-Kind Gas Turbines
,”
25th Annual CEM EPRI Utility Continuous Emissions Monitoring User Group Meeting
, Detroit, MI, May 4–5.
11.
Schneider
,
E.
,
Demircioglu
,
S.
,
Franco
,
S.
, and
Therkorn
,
D.
,
2009
, “
Analysis of Compressor On-Line Washing to Optimize Gas Turbine Power Plant Performance
,”
ASME
Paper No. GT2009-59356.
12.
Dederichs
,
S.
,
Habisreuther
,
P.
,
Zarzalis
,
N.
,
Beck
,
C.
,
Krebs
,
W.
, and
Prade
,
B.
,
2013
, “
Assessment of a Gas Turbine NOx Reduction Potential Based on a Spatiotemporal Unmixedness Parameter
,”
ASME
Paper No. GT2013-94404.
13.
Therkorn
,
D.
,
2005
, “
Remote Monitoring and Diagnostic for Combined-Cycle Power Plants
,”
ASME
Paper No. GT2005-68710.
14.
Lukachko
,
S. P.
, and
Waitz
,
I. A.
,
1997
, “
Effects of Engine Aging on Aircraft NOx Emissions
,”
ASME
Paper No. 97-GT-386.
15.
Bakken
,
L. E.
, and
Skogly
,
L.
,
1996
, “
Parametric Modeling of Exhaust Gas Emission From Natural Gas Fired Gas Turbines
,”
ASME J. Eng. Gas Turbines Power
,
118
(
3
), pp.
553
560
.
16.
Syed
,
M. S.
,
Dooley
,
K. M.
,
Madron
,
F.
, and
Knopf
,
F. C.
,
2016
, “
Enhanced Turbine Monitoring Using Emissions Measurements and Data Reconciliation
,”
Appl. Energy
,
173
, pp.
355
365
.
17.
Rudolf
,
C.
,
Wirsum
,
M.
,
Gassner
,
M.
, and
Bernero
,
S.
,
2015
, “
Analysis of Long-Term Gas Turbine Operation With a Model-Based Data Reconciliation Technique
,”
ASME
Paper No. GT2015-42497.
18.
ISO
,
2017
, “
International Organization for Standardization, Condition Monitoring and Diagnostics of Machines—General Guidelines
,” International Organization for Standardization, Berlin.
19.
Gulen
,
S. C.
,
Griffin
,
P. R.
, and
Paolucci
,
S.
,
2000
, “
Real-Time On-Line Performance Diagnostics of Heavy-Duty Industrial Gas Turbines
,”
ASME
Paper No. 2000-GT-312.
20.
Münzberg
,
H.-G.
, and
Kurzke
,
J. T.
,
1977
,
Gasturbinen: Betriebsverhalten u. Optimierung
,
Hochschultext, Springer
,
Berlin
.
21.
Biagioli
,
F.
, and
Güthe
,
F.
,
2007
, “
Effect of Pressure and Fuel–Air Unmixedness on NOx Emissions From Industrial Gas Turbine Burners
,”
Combust. Flame
,
151
(
1–2
), pp.
274
288
.
22.
Güthe
,
F.
,
Hellat
,
J.
, and
Flohr
,
P.
,
2009
, “
The Reheat Concept: The Proven Pathway to Ultralow Emissions and High Efficiency and Flexibility
,”
ASME J. Eng. Gas Turbines Power
,
131
(
2
), p. 021503.
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