Prediction and optimization of combustion performance and emissions for lean methane-hydrogen non-premixed flame using RSM-PSO methodology

To reduce carbon emissions, hydrogen is a clean energy as fuel to co-combustion with natural gas. This experimental work focuses on studying the effects of hydrogen addition to methane on combustion performance and emissions in non-premixed coaxial burners, establishing performance prediction models and optimizing operation conditions involving fuel velocity, hydrogen blending ratio and equivalence ratio. The global sensitivity and analysis of variance were adopted to quantify the contributions of independent variables on the output responses of temperature, O2, 2 , NO, CO and CO2 2 emissions. The hydrogen blending ratio is the most influential parameter, whose total effects of NO and CO2 2 emissions are 64.6% and 83.5%, respectively. The equivalence ratio is not significant to NO and CO2 2 emissions, and the adjusted determination coefficient of the reduced prediction model is 0.9707 and 0.9360, demonstrating a better goodness of fit. Based on the response surface model and particle swarm optimization algorithm, a Pareto was solved showing NO and CO2 2 emissions decrease by 42.0%-73.6% and 60.8%-89.5%, and peak temperature in the chamber centerline increases by 1.02%-3.61%. The research results provide a profound guide to predict and optimize combustion performance and emissions for methane- hydrogen non-premixed flame.