Research Papers: Air Emissions From Fossil Fuel Combustion

Soft Analyzer for Monitoring NOx Emissions From a Gas Turbine Combustor

[+] Author and Article Information
Ali Al-Malak

Department of Systems Engineering,
King Fahd University of
Petroleum and Minerals,
Dhahran 31261, Saudi Arabia
e-mail: g199341830@kfupm.edu.sa

Moustafa Elshafei

Department of Systems Engineering,
King Fahd University of
Petroleum and Minerals,
KFUPM Box 405,
Dhahran 31261, Saudi Arabia
e-mail: elshafei@kfupm.edu.sa

Mohamed A. Habib

Department of Mechanical Engineering,
King Fahd University of
Petroleum and Minerals,
Dhahran 31261, Saudi Arabia
e-mail: mahabib@kfupm.edu.sa

Iyad Al-Zaharnah

Innovation Centre,
Technology Transfer,
Innovation and Entrepreneurship Sector (TTIE),
King Fahd University of
Petroleum and Minerals,
Dhahran 31261, Saudi Arabia
e-mail: iyadtz@kfupm.edu.sa

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received February 15, 2015; final manuscript received January 19, 2016; published online February 22, 2016. Assoc. Editor: Ashwani K. Gupta.

J. Energy Resour. Technol 138(3), 031101 (Feb 22, 2016) (9 pages) Paper No: JERT-15-1065; doi: 10.1115/1.4032617 History: Received February 15, 2015; Revised January 19, 2016

Many industrial sectors built cogeneration plants to secure their power supplies reliably and to efficiently produce the plant demand of steam through the associated heat. Due to the rise of fuel cost and tightening environmental regulations, the number of cogeneration plants will increase in lieu to individual boilers and steam turbine generators. Most of the recent cogeneration plants are equipped with hardware-based analyzer which is a continuous emission monitoring system (CEMS) to monitor the NOx emissions from the plant stack as per U.S. Environmental Protection Agency (EPA) regulations. The CEMS is unreliable due to high failure rates and requires high capital cost, high maintenance cost, high operational cost in addition to being subject to long lag time and having slow response. In this work, a software-based analyzer is designed by applying artificial neural networks (ANNs) on process data collected from cogeneration plant (156 MW X 2 combustion gas turbine generators (CGTGs)) equipped with CEMS for NOx monitoring. The developed soft analyzer will be used to verify the existing CEMS readings and used as a reliable tool to monitor the NOx emissions that will eventually replace the CEMS. By providing a relationship between the process and the emissions, the soft analyzer will also assist in understanding the NOx behavior in reference to the process variations and thus enables better emission control.

Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.


Reifman, J. , and Feldman, E. , 1998, “ Identification and Control of NOx Emissions Using Neural Networks,” J. Air Waste Manage. Assoc., 48(5), pp. 174–185. [CrossRef]
Ikonen, E. , Najim, K. , and Kortela, U. , 2000, “ Neuro-Fuzzy Modelling of Power Plant Flue-Gas Emissions,” Eng. Appl. Artif. Intell., 13(6), pp. 705–717. [CrossRef]
Azid, I. , Ripin, Z. , Aris, M. , Ahmad, A. , Seetharamu, K. , and Yusoff, R. , 2000, “ Predicting Combined-Cycle Natural Gas Power Plant Emissions by Using Artificial Neural Networks,” IEEE TENCON 2000, Kuala Lumpur, Sept. 24–27, pp. 512–517.
Ferretti, G. , and Piroddi, L. , 2001, “ Estimation of NOx Emissions in Thermal Power Plants Using Artificial Neural Networks,” ASME J. Eng. Gas Turbines Power, 123(2), pp. 465–471. [CrossRef]
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. [CrossRef]
Kalogirou, S. , 2003, “ Artificial Intelligence for the Modeling and Control of Combustion Processes: A Review,” Prog. Energy Combust. Sci., 29(6), pp. 515–566. [CrossRef]
Maurya, R. K. , Agarwal, A. K. , Rakesh, K. M. , and Avinash, U. A. , 2014, “ Combustion and Emission Characterization of n-Butanol Fueled HCCI Engine,” ASME J. Energy Resour. Technol., 137(1), p. 011101. [CrossRef]
Cubio, G. M. , Capareda, S. C. , and Alagao, F. B. , 2014, “ Real-Time Analysis of Engine Power, Thermal Efficiency, and Emission Characteristics Using Refined and Transesterified Waste Vegetable Oil, Real-Time Analysis of Engine Power, Thermal Efficiency, and Emission Characteristics Using Refined and Transesterified Waste Vegetable Oil,” ASME J. Energy Resour. Technol., 136(3), p. 032201. [CrossRef]
Capata, R. , and Sciubba, E. , 2013, “ The Low Emission Turbogas Hybrid Vehicle Concept—Preliminary Simulation and Vehicle Packaging,” ASME J. Energy Resour. Technol., 135(3), p. 032203. [CrossRef]
Graziani, S. , Pitrone, N. , Xibilia, M. , and Barbalace, N. , 2004, “ Improving Monitoring of NOx Emissions in Refineries,” 21st IEEE Instrumentation and Measurement Technology Conference (IMTC 04), Como, Italy, May 18–20, pp. 594–597.
Habib, M. , Elshafei, M. , and Dajani, M. , 2008, “ Influence of Combustion Parameters on NOx Production in an Industrial Boiler,” Comput. Fluids, 37(1), pp. 12–23. [CrossRef]
Sanusi, Y. S. , Habib, M. A. , and Mokheimer, E. M. A. , 2014, “ Experimental Study on the Effect of Hydrogen Enrichment of Methane on the Stability and Emission of Nonpremixed Swirl Stabilized Combustor,” ASME J. Energy Resour. Technol., 137(3), p. 032203. [CrossRef]
Shakil, M. , Elshafei, M. , Habib, M. , and Maleki, F. , 2009, “ Soft Sensor for NOx and O2 Using Dynamic Neural Networks,” Comput. Electr. Eng., 35(4), pp. 578–586. [CrossRef]
Fast, M. , Assadi, M. , and De, S. , 2009, “ Development and Multi-Utility of an ANN Model for an Industrial Gas Turbine,” Appl. Energy, 86(1), pp. 9–17. [CrossRef]
Davis, L. , and Black, S. , 2010, “ Dry Low NOx Combustion Systems for GE Heavy-Duty Gas Turbines,” GE Power Systems, Schenectady, NY, Report No. GER-3568G (10/00), pp. 1–22.
Bartolini, C. , Caresana, F. , Comodi, G. , Pelagalli, L. , Renzi, M. , and Vagni, S. , 2011, “ Application of Artificial Neural Networks to Micro Gas Turbines,” Energy Convers. Manage., 52(1), pp. 781–788. [CrossRef]
Khoshhal, A. , Rahimi, M. , and Alsairafi, A. , 2011, “ CFD Study on Influence of Fuel Temperature on NOx Emission in a HiTAC Furnace,” Int. Commun. Heat Mass Transfer, 38(10), pp. 1421–1427. [CrossRef]
Gobbato, P. , Masi, M. , Toffolo, A. , Lazzaretto, A. , and Tanzini, G. , 2012, “ Calculation of the Flow Field and NOx Emissions of a Gas Turbine Combustor by a Coarse Computational Fluid Dynamics Model,” Energy, 45(1), pp. 445–455. [CrossRef]


Grahic Jump Location
Fig. 1

Gas turbine generator assembly

Grahic Jump Location
Fig. 2

Nozzles of DLN-2.6 combustor

Grahic Jump Location
Fig. 3

DLN-2.6 combustion chamber [15]

Grahic Jump Location
Fig. 4

DLN-2.6 combustion temperature profile

Grahic Jump Location
Fig. 5

NOx direct correlation with process parameters

Grahic Jump Location
Fig. 6

NOx inverse correlation with process parameters

Grahic Jump Location
Fig. 7

Constant process parameters

Grahic Jump Location
Fig. 8

Process parameters with no clear NOx-correlation

Grahic Jump Location
Fig. 9

FFBPNN (four inputs–45 hidden neurons) performance at different epoch numbers

Grahic Jump Location
Fig. 10

FFBPNN (four inputs–45 hidden neurons) error histogram

Grahic Jump Location
Fig. 11

FFBPNN (four inputs–45 hidden neurons) regression test




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In