Full Content is available to subscribers

Subscribe/Learn More  >

Predictive Emissions Monitoring Using a Continuously Updating Neural Network

[+] Author Affiliations
Evert Vanderhaegen, Michaël Deneve, Hannes Laget, Nathalie Faniel, Jan Mertens

Laborelec, Linkebeek, Belgium

Paper No. GT2010-22899, pp. 769-775; 7 pages
  • ASME Turbo Expo 2010: Power for Land, Sea, and Air
  • Volume 2: Combustion, Fuels and Emissions, Parts A and B
  • Glasgow, UK, June 14–18, 2010
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4397-0 | eISBN: 978-0-7918-3872-3
  • Copyright © 2010 by ASME


In the European Union, power plants of more than 50 MW (thermal energy) need to comply with the Large Combustion Plant Directive (LCPD, 2001) implying that flue gas emissions need to be measured continuously. Traditionally, emissions from power plants are measured using Automated Measuring Systems (AMS). The LCPD states that no more than 10 days of emission data may be lost within one year including days needed for maintenance. This is the reason why more and more power plants are currently installing a second, back-up AMS since they have problems with the availability of their AMS. Since early 1990’s, Predictive Emissions Monitoring Systems (PEMS) are being developed and accepted by some local authorities within Europe and the United States. PEMS are in contrast to AMS based on the prediction of gaseous emissions (most commonly NOx and CO) using plant operational data (eg. fuel properties, pressure, temperature, excess air, [[ellipsis]]) rather than the actual measurement of these emissions. The goal of this study is to develop a robust PEMS that can accurately predict the NOx and CO emissions across the entire normal working range of a gas turbine. Furthermore, the PEMS should require as little maintenance as possible. The study does not intend to replace the AMS by a PEMS but rather to use the PEMS as a backup for the AMS. Operational data of a gas turbine, acquired over a long period, was used to identify inputs with a high influence on the NOx and CO formation. Consequently, simulations were done testing different model structures and calibration methodologies. The study shows that a static model failed to predict the emissions accurately over long time periods. In contrast, a dynamic or self-adapting algorithm proved to be most efficient in predicting the emissions over a long time period with a minimum of required intervention and maintenance. The self-adapting algorithm uses measured AMS data to continuously update the neural network. Since the PEMS is developed as a backup for the AMS, these data are readily available. The study shows that in case of a failing AMS, the developed model could accurately predict the NOx emissions for a duration of several weeks. Although not discussed in detail in this study, a quality assurance system of the PEMS is also developed since the PEMS needs to comply to the EN14181 (as does any AMS). The PEMS as a backup of the AMS instead of a second AMS is cost and time saving. Not only is the purchase of a second AMS avoided (between 40 and 100 k€) but equally important and of the same order of magnitude are the cost and time savings with respect to the Quality Assurance of the second AMS.

Copyright © 2010 by ASME



Interactive Graphics


Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

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 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