Abstract
Rapid development in data science provides new methods for combustion tuning. This paper describes an artificial neural network (ANN) model that can accurately predict the key parameters in gas turbine combustion tuning and optimization, including NOx emission, combustor vibrational acceleration (ACC), and combustor dynamic pressure (DP). Wavelet denoising method was used in data preprocessing to improve the signal-to-noise ratio (SNR), which greatly improved the prediction accuracy of the neural network model. A combustion tuning simulation was then conducted to optimize NOx emissions using the acquired accurate mappings. By adjusting controllable parameters, optimization can be realized within necessary constraints. The effects of user-defined initialization parameters in the simulation were investigated for fast combustion tuning. An operating window was given considering the tradeoff between optimization results and computing time.
Issue Section:
Research Papers
References
1.
Gonzalez-Salazar
,
M. A.
,
Kirsten
,
T.
, and
Prchlik
,
L.
, 2018
, “
Review of the Operational Flexibility and Emissions of Gas- and Coal-Fired Power Plants in a Future With Growing Renewables
,” Renewable Sustainable Energy Rev.
,
82
, pp. 1497
–1513
.10.1016/j.rser.2017.05.2782.
Rokke
,
N.
,
Hustad
,
J.
, and
Berg
,
S.
, 1993
, “
Pollutant Emissions From Gas Fired Turbine Engines in Offshore Practice: Measurements and Scaling
,” ASME
Paper No. GT1993-170.10.1115/GT1993-1703.
Syed
,
K. J.
,
Roden
,
K.
, and
Martin
,
P.
, 2006
, “
A Novel Approach to Predicting NOx Emissions From Dry Low Emissions Gas Turbines
,” ASME
Paper No. GT2006-90333.10.1115/GT2006-903334.
Rizk
,
N.
, and
Mongia
,
H.
, 1993
, “
Three-Dimensional NOx Modeling for Rich/Lean Combustor
,” AIAA
Paper No. 93-0251.10.2514/6.1993-2515.
Rizk
,
N.
, and
Mongia
,
H.
, 1994
, “
Emissions Predictions of Different Gas Turbine Combustors
,” AIAA
Paper No. 94-0118.10.2514/6.1994-1186.
Rizk
,
N.
, and
Mongia
,
H.
, 1995
, “
NOx Model for Lean Combustion Concept
,” J. Propul. Power
,
11
(1
), pp. 161
–169
.10.2514/3.238547.
Kalogirou
,
S. A.
, 2003
, “
Artificial Intelligence for the Modeling and Control of Combustion Processes: A Review
,” Prog. Energy Combust. Sci.
,
29
(6
), pp. 515
–566
.10.1016/S0360-1285(03)00058-38.
Tronci
,
S.
,
Baratti
,
R.
, and
Servida
,
A.
, 2002
, “
Monitoring Pollutant Emissions in a 4.8 MW Power Plant Through Neural Network
,” Neurocomputing
,
43
(1–4
), pp. 3
–15
.10.1016/S0925-2312(01)00617-89.
Ferretti
,
G.
, and
Piroddi
,
L.
, 2001
, “
Estimation of NOx Emissions in Thermal Power Plants Using Neural Networks
,” ASME J. Eng. Gas Turbines Power
,
123
(2
), pp. 465
–471
.10.1115/1.136733910.
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. GT2014-25030.10.1115/GT2014-2503011.
Kibrya
,
G.
, and
Botros
,
K.
, 2002
, “
A Neural Network Based Predictive Emission Monitoring Model for NOx Emission From a Gas Turbine Combustor
,” ASME
Paper No. ETCE2002/CAE-29174.10.1115/ETCE2002/CAE-2917412.
Park
,
Y.
,
Choi
,
M.
,
Kim
,
K.
,
Li
,
X.
,
Jung
,
C.
,
Na
,
S.
, and
Choi
,
G.
, 2020
, “
Prediction of Operating Characteristics for Industrial Gas Turbine Combustor Using an Optimized Artificial Neural Network
,” Energy
,
213
, p. 118769
.10.1016/j.energy.2020.11876913.
Li
,
S.
,
Zhu
,
H.
,
Zhu
,
M.
,
Zhao
,
G.
, and
Wei
,
X.
, 2021
, “
Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach
,” ASME J. Eng. Gas Turbines Power
,
143
(3
), p. 031021
.10.1115/1.405002014.
Tan
,
P.
,
He
,
B.
,
Zhang
,
C.
,
Rao
,
D.
,
Li
,
S.
,
Fang
,
Q.
, and
Chen
,
G.
, 2019
, “
Dynamic Modeling of NOx Emission in a 660 MW Coal-Fired Boiler With Long Short-Term Memory
,” Energy
,
176
, pp. 429
–436
.10.1016/j.energy.2019.04.02015.
Yang
,
G.
,
Wang
,
Y.
, and
Li
,
X.
, 2020
, “
Prediction of the NOx Emissions From Thermal Power Plant Using Long-Short Term Memory Neural Network
,” Energy
,
192
, p. 116597
.10.1016/j.energy.2019.11659716.
Kayaalp
,
K.
,
Metlek
,
S.
,
Ekici
,
S.
, and
Şöhret
,
Y.
, 2021
, “
Developing a Model for Prediction of the Combustion Performance and Emissions of a Turboprop Engine Using the Long Short-Term Memory Method
,” Fuel
,
302
, p. 121202
.10.1016/j.fuel.2021.12120217.
Richards
,
G.
, and
Janus
,
M.
, 1998
, “
Characterization of Oscillations During Premix Gas Turbine Combustion
,” ASME J. Eng. Gas Turbines Power
,
120
(2
), pp. 294
–302
.10.1115/1.281812018.
Lieuwen
,
T.
, and
Zinn
,
B. T.
, 1998
, “
The Role of Equivalence Ratio Oscillations in Driving Combustion Instabilities in Low NOx Gas Turbines
,” Proc. Symp. (Int.) Combust.
,
27
(2
), pp. 1809
–1816
.10.1016/S0082-0784(98)80022-219.
Yoon
,
J.
,
Kim
,
M.-K.
,
Hwang
,
J.
,
Lee
,
J.
, and
Yoon
,
Y.
, 2013
, “
Effect of Fuel-Air Mixture Velocity on Combustion Instability of a Model Gas Turbine Combustor
,” Appl. Therm. Eng.
,
54
(1
), pp. 92
–101
.10.1016/j.applthermaleng.2013.01.03220.
Lieuwen
,
T. C.
, 2002
, “
Experimental Investigation of Limit-Cycle Oscillations in an Unstable Gas Turbine Combustor
,” J. Propul. Power
,
18
(1
), pp. 61
–67
.10.2514/2.589821.
Goodfellow
,
I.
,
Bengio
,
Y.
, and
Courville
,
A.
, 2016
, Deep Learning
,
MIT Press
, Cambridge, UK.22.
Prechelt
,
L.
, 1998
, “
Automatic Early Stopping Using Cross Validation: Quantifying the Criteria
,” Neural Networks
,
11
(4
), pp. 761
–767
.10.1016/S0893-6080(98)00010-023.
Liu
,
H.
,
Tian
,
H.
,
Pan
,
D.
, and
Li
,
Y.
, 2013
, “
Forecasting Models for Wind Speed Using Wavelet, Wavelet Packet, Time Series and Artificial Neural Networks
,” Appl. Energy
,
107
, pp. 191
–208
.10.1016/j.apenergy.2013.02.00224.
Zhao
,
R.
, and
Cui
,
H.
, 2015
, “
Improved Threshold Denoising Method Based on Wavelet Transform
,” Proceedings of the 2015 Seventh International Conference on Modelling, Identification and Control (ICMIC
), Sousse, Tunisia, Dec. 18–20,
pp. 1
–4
.10.1109/ICMIC.2015.740935225.
Coifman
,
R. R.
, and
Donoho
,
D. L.
, 1995
, “
Translation-Invariant De-Noising
,” Wavelets and Statistics
,
Springer
, New York, pp. 125
–150
.26.
Fu
,
S.
,
Muralikrishnan
,
B.
, and
Raja
,
J.
, 2003
, “
Engineering Surface Analysis With Different Wavelet Bases
,” ASME J. Manuf. Sci. Eng.
,
125
(4
), pp. 844
–852
.10.1115/1.161694727.
Zhou
,
H.
,
Cen
,
K. F.
, and
Mao
,
J. B.
, 2001
, “
Combining Neural Network and Genetic Algorithms to Optimize Low NOx Pulverized Coal Combustion
,” Fuel
,
80
(15
), pp. 2163
–2169
.10.1016/S0016-2361(01)00104-128.
Shi
,
Y.
,
Zhong
,
W.
,
Chen
,
X.
,
Yu
,
A. B.
, and
Li
,
J.
, 2019
, “
Combustion Optimization of Ultra Supercritical Boiler Based on Artificial Intelligence
,” Energy
,
170
, pp. 804
–817
.10.1016/j.energy.2018.12.17229.
Kesgin
,
U.
, 2004
, “
Genetic Algorithm and Artificial Neural Network for Engine Optimisation of Efficiency and NOx Emission
,” Fuel
,
83
(7–8
), pp. 885
–895
.10.1016/j.fuel.2003.10.02530.
Liu
,
J.
,
Ma
,
B.
, and
Zhao
,
H.
, 2020
, “
Combustion Parameters Optimization of a Diesel/Natural Gas Dual Fuel Engine Using Genetic Algorithm
,” Fuel
,
260
, p. 116365
.10.1016/j.fuel.2019.116365Copyright © 2022 by ASME
You do not currently have access to this content.
