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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

Professor
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

Professor
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.

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References

Figures

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Fig. 4

DLN-2.6 combustion temperature profile

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Fig. 3

DLN-2.6 combustion chamber [15]

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Fig. 2

Nozzles of DLN-2.6 combustor

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Fig. 1

Gas turbine generator assembly

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Fig. 5

NOx direct correlation with process parameters

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Fig. 6

NOx inverse correlation with process parameters

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Fig. 7

Constant process parameters

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Fig. 8

Process parameters with no clear NOx-correlation

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Fig. 9

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

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Fig. 10

FFBPNN (four inputs–45 hidden neurons) error histogram

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Fig. 11

FFBPNN (four inputs–45 hidden neurons) regression test

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