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A robust framework to predict mercury speciation in combustion flue gases.

Publication ,  Journal Article
Ticknor, JL; Hsu-Kim, H; Deshusses, MA
Published in: Journal of hazardous materials
January 2014

Mercury emissions from coal combustion have become a global concern as growing energy demands have increased the consumption of coal. The effective implementation of treatment technologies requires knowledge of mercury speciation in the flue gas, namely concentrations of elemental, oxidized and particulate mercury at the exit of the boiler. A model that can accurately predict mercury species in flue gas would be very useful in that context. Here, a Bayesian regularized artificial neural network (BRANN) that uses five coal properties and combustion temperature was developed to predict mercury speciation in flue gases before treatment technology implementation. The results of the model show that up to 97 percent of the variation in mercury species concentration is captured through the use of BRANNs. The BRANN model was used to conduct a parametric sensitivity which revealed that the coal chlorine content and coal calorific value were the most sensitive parameters, followed by the combustion temperature. The coal sulfur content was the least important parameter. The results demonstrate the applicability of BRANNs for predicting mercury concentration and speciation in combustion flue gas and provide a more efficient and effective technique when compared to other advanced non-mechanistic modeling strategies.

Duke Scholars

Published In

Journal of hazardous materials

DOI

EISSN

1873-3336

ISSN

0304-3894

Publication Date

January 2014

Volume

264

Start / End Page

380 / 385

Related Subject Headings

  • Strategic, Defence & Security Studies
  • Neural Networks, Computer
  • Models, Chemical
  • Mercury
  • Gases
  • Bayes Theorem
  • 41 Environmental sciences
  • 40 Engineering
  • 34 Chemical sciences
  • 09 Engineering
 

Citation

APA
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ICMJE
MLA
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Ticknor, J. L., Hsu-Kim, H., & Deshusses, M. A. (2014). A robust framework to predict mercury speciation in combustion flue gases. Journal of Hazardous Materials, 264, 380–385. https://doi.org/10.1016/j.jhazmat.2013.10.052
Ticknor, Jonathan L., Heileen Hsu-Kim, and Marc A. Deshusses. “A robust framework to predict mercury speciation in combustion flue gases.Journal of Hazardous Materials 264 (January 2014): 380–85. https://doi.org/10.1016/j.jhazmat.2013.10.052.
Ticknor JL, Hsu-Kim H, Deshusses MA. A robust framework to predict mercury speciation in combustion flue gases. Journal of hazardous materials. 2014 Jan;264:380–5.
Ticknor, Jonathan L., et al. “A robust framework to predict mercury speciation in combustion flue gases.Journal of Hazardous Materials, vol. 264, Jan. 2014, pp. 380–85. Epmc, doi:10.1016/j.jhazmat.2013.10.052.
Ticknor JL, Hsu-Kim H, Deshusses MA. A robust framework to predict mercury speciation in combustion flue gases. Journal of hazardous materials. 2014 Jan;264:380–385.
Journal cover image

Published In

Journal of hazardous materials

DOI

EISSN

1873-3336

ISSN

0304-3894

Publication Date

January 2014

Volume

264

Start / End Page

380 / 385

Related Subject Headings

  • Strategic, Defence & Security Studies
  • Neural Networks, Computer
  • Models, Chemical
  • Mercury
  • Gases
  • Bayes Theorem
  • 41 Environmental sciences
  • 40 Engineering
  • 34 Chemical sciences
  • 09 Engineering