Enhanced turbine monitoring using emissions measurements and data reconciliation

Abstract

Standard monitoring within a gas-turbine based cogeneration system includes key flow rates, temperatures, pressures and turbine vibration. These standard measurements can be enhanced with continuous emissions monitoring to help pinpoint system problems. A combination of these measurements, a fast prediction model and data reconciliation constitute an improved monitoring and diagnostic tool that can quantitatively predict the existence of turbine problems (for example, damaged combustor nozzles) even when standard turbine monitoring indicates no problems exist.

Gas turbine combined cycle start-up and stress evaluation: A simplified dynamic approach

“…In this regards, new ways to fasten the energy systems during startups [9] as well as in case of rapid transient load conditions [10], have been studied and tested [11][12] [13][14] [15]. Moreover, advanced monitoring technique were introduced to control the productive parameters of energy systems and life of their components [16][17] [18][19] [20]. Analysis on reliability and maintenance turned from a periodical approach to equivalent life-impact due to new high stressing working conditions.…”

Box-Behnken design based CO2 co-gasification of horticultural waste and sewage sludge with addition of ash from waste as catalyst

“…GRI-Mech 3.0 reaction mechanism, containing 53 species and 325 reactions, is an optimized mechanism for syngas oxidation simulation, comprised of a list of elementary chemical reactions and the associated rate constant expressions [33]. It has been experimentally confirmed and widely employed in the existing literature [34,35]. In the present study, it was used as an input file in the CHEMKIN code to solve the chemical kinetics of syngas combustion.…”

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

“…Such models are also suitable for monitoring, since root causes for engine deterioration can be linked online to a real component, as previously shown for NOx emissions. 19,20…”

“…Such models are also suitable for monitoring, since root causes for engine deterioration can be linked online to a real component, as previously shown for NOx emissions. 19,20 Similar to these existing approaches for NOx, this paper aims at creating a semiempirical CO model for online emission monitoring, diagnosis, and operational support for, e.g. occasional engine readjustment.…”