Long-term carbon monoxide emission behavior of heavy-duty gas turbines: An approach for model-based monitoring and diagnostics. (July 2019)

• Record Type: Journal Article
• Authors: Lipperheide, Moritz; Gassner, Martin; Weidner, Frank; Bernero, Stefano; Wirsum, Manfred
• Is Part Of: International journal of spray and combustion dynamics. Volume 11(2019)
• Publication Date: 2019-07
• Subjects: Carbon monoxide emission -- combustion -- monitoring -- gas turbine; Combustion engineering -- Periodicals; Fluid dynamics -- Periodicals; Combustion -- Periodicals; Spraying -- Periodicals; Combustion; Combustion engineering; Fluid dynamics; Spraying; Periodicals; 541.361
• DOI: 10.1177/1756827718791921
• Languages: English
• ISSNs: 1756-8285

Abstract

Emission measurements are a valuable source of information regarding the condition of gas turbine combustors. Aging of the hot gas path components can lead to an emission increase, which may ultimately require a readjustment of operational settings and accordingly impacts plant availability and maintenance. While NOx emissions may become crucial in high flame temperatures at full load, carbon monoxide emissions typically restrict low-load operation, which electricity markets demand more frequently due to the increasing penetration of intermittent renewable power. This paper presents a semiempirical carbon monoxide model that allows for quantifying the evolution of carbon monoxide emissions for GT24/GT26 heavy-duty gas turbines in commercial long-term operation. Input parameters to the derived carbon monoxide model are either directly measured or reconstructed by virtual measurements based on a simplified engine model. The method is developed with commissioning and operation data of three different gas turbines of GE's GT24/GT26 fleet and validated over a total of 8.5 years of observation. Aging is accounted for by incorporating control sensor deviation and the formation of cold spots in the combustor into the semiempirical model. When these effects are taken into account, the carbon monoxide prediction is improved by up to 60% in terms of root mean square error of the log10(carbon monoxide) values compared to a benchmark case without consideration of aging.