Skip to main content

Prediction of NOx emissions throughflame radical imaging and neural network based soft computing

Li, Xinli, Sun, Duo, Lu, Gang, Krabicka, Jan, Yan, Yong (2012) Prediction of NOx emissions throughflame radical imaging and neural network based soft computing. In: Imaging Systems and Techniques (IST), 2012 IEEE International Conference, 16-17 July 2012, Manchester. (doi:10.1109/IST.2012.6295594) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/IST.2012.6295594

Abstract

The characteristics of reacting radicals in a flame are crucial for an in-depth understanding of the formation process of combustion emissions. This paper presents an algorithm for the prediction of NOx (NO and NO2) emissions in flue gas through flame radical imaging, flame temperature monitoring and application of Neural Network techniques. Radiation images of flame radicals OH*, CN*, CH* and C2* are captured using an intensified multi-wavelength imaging system. Flame temperature is determined using a spectrometer and two-color pyrometry. Based on these images, the characteristic values of the flame radicals are extracted. These characteristic values, together with the flame temperature, are then used to predict NOx emissions. Experimental results from a laboratory-scale gas-fired combustion rig have shown the effectiveness of the proposed method for the prediction of NOx emissions.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/IST.2012.6295594
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Tina Thompson
Date Deposited: 31 Oct 2013 10:36 UTC
Last Modified: 29 May 2019 11:13 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35862 (The current URI for this page, for reference purposes)
  • Depositors only (login required):