Effects of compositional uncertainties in cracked NH3/biosyngas fuel blends on the combustion characteristics and performance of a combined-cycle gas turbine: A numerical thermokinetic study

• Blending of partially cracked ammonia with biosyngas is an attractive strategy for improving NH3 combustion. In practice, products of biomass gasification and those of thermo-catalytic cracking of NH3 are subject to some compositional uncertainties. Despite their practical importance, so far, the effects of such uncertainties on combustion systems remained largely unexplored. Hence, this paper quantifies the effects of small compositional uncertainties of reactants upon combustion of partially cracked NH3/syngas/air mixtures. An uncertainty quantification method, based on polynomial chaos expansion and a data-driven model, is utilised to investigate the effects of uncertainty in fuel composition on the laminar flame speed (SL) and adiabatic flame temperature (Tad) at different inlet pressures (Pi). The analysis is then extended to the power output of a combined-cycle gas turbine fuelled by the reactants. It is found that 1.5% fuel compositional uncertainty can cause 12–21% of SL uncertainty depending on the inlet pressure. Furthermore, the effect of compositional uncertainty on Tad increases at higher ratios of H2 to NH3. Sensitivity analysis reveals that the uncertainty of CO contribution to SL uncertainty is higher than that of NH3, while the trend is reversed for the Tad uncertainty. In addition, the power output from the combined-cycle gas turbine system varies between 4 and 6% with 1.5% of fuel compositional uncertainty. This become more noticeable at elevated Pi [5–10 atm], particularly when the fuel mixture contains high H2 which is the main contributor to Tad variability.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Energiteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Energy Engineering (hsv//eng)

Nyckelord

Ammonia-syngas fuel blends

Combined-cycle gas turbine

Cracked ammonia

Data-driven models

Uncertainty quantification

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