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A Cost Effective Advanced Emissions Monitoring Solution for Gas Turbines: Statistical Hybrid Predictive System That Accurately Measures Nitrogen Oxides, Carbon Monoxide, Sulfur Dioxide, Hydrocarbon and Carbon Dioxide Mass Emission Rates

[+] Author Affiliations
Brian Swanson

CMC Solutions, L.L.C., Wixom, MI

Paper No. GT2008-50401, pp. 267-276; 10 pages
doi:10.1115/GT2008-50401
From:
  • ASME Turbo Expo 2008: Power for Land, Sea, and Air
  • Volume 3: Combustion, Fuels and Emissions, Parts A and B
  • Berlin, Germany, June 9–13, 2008
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4313-0 | eISBN: 0-7918-3824-2
  • Copyright © 2008 by CMC Solutions LLC (Exclusive of data analysis in Sections 3 and 4)

abstract

U.S. Federal regulations under Title IV of the Clean Air Act Amendments promulgated in 1990 require continuous monitoring of nitrogen oxides (NOx ) and carbon dioxide emissions from large gas turbines. Local, regional, or State authorities may mandate continuous monitoring for carbon monoxide, sulfur dioxide, volatile organic compounds, and other specific pollutant parameters. U.S. regulations that require continuous emissions monitoring systems (CEMS) also allow for the use of predictive approaches as an alternative providing the installed predictive emissions monitoring system (PEMS) meets rigorous performance specification criteria and the site performs ongoing quality assurance tasks such as periodic audits with portable analyzers and annual accuracy testing. A statistical hybrid predictive emission monitoring system (PEMS) has been deployed at numerous sites in the United States to meet EPA requirements for continuous monitoring of gas turbine pollutant emissions. This paper discusses specific implementations of a unique cost-effective statistical hybrid PEMS on various classes of gas turbines ranging in size from 60kW to 180 MW, both gas-fired and liquid-fired units, in simple cycle and combined cycle mode of operation. The turbines were equipped with a variety of NOx control strategies including dry low NOx , steam and water injection, solid post-combustion catalyst, SoLoNOx ™, and selective catalytic reduction. In each instance the predictive engine operated on training data of at least three days and up to ninety days as required to develop a robust empirical model of the emissions. Each model was subsequently evaluated using standard U.S. EPA performance specification test methods. The results of PEMS performance testing on these gas turbines are presented along with additional information regarding the quality assurance and quality control procedures put in place and the costs to support the ongoing operation of the deployed compliance statistical hybrid PEMS.

Copyright © 2008 by CMC Solutions LLC (Exclusive of data analysis in Sections 3 and 4)

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