Laminar burning velocity of hydrogen and carbon-monoxide enriched natural gas (HyCONG): An experimental and artificial neural network study

Abstract

The laminar burning velocity of H2 and CO enriched natural gas (HyCONG) mixtures measured through spherically expanding flames method under three different fuel-air equivalence ratios (0.6, 0.8, and 1.0) at room temperature and atmospheric pressure. Experiments have been conducted into constant volume combustion chamber by using high-speed schlieren photography technique. The five different CO fraction (0%, 20%, 40%, 60%, and 80%) and three HCNG (0%, 20%, and 40%) blends combinations are selected for the experiments. The laminar burning velocity and Markstein length were obtained for 45 operational conditions. The results reveal that the laminar burning velocity increases drastically with an increase of hydrogen and carbon monoxide in HyCONG blends and increasing fuel-air equivalence ratio. The maximum laminar burning velocities are determined as 24.42 cm/s, 45.7 cm/s, and 62.78 cm/s for 20–80 HyCONG at ϕ = 0.6, 40–60 HyCONG at ϕ = 0.8, and 40–60 HyCONG at ϕ = 1.0 respectively. Additionally, the artificial neural network model (ANN) of combustion chamber has been constructed to predict the laminar burning velocity of HyCONG blends. The ANN’s well known backpropagation algorithm is applied in multilayered feedforward networks. The forecasting of laminar burning velocity has been evaluated using four-input and one-output network structure with two different learning algorithm namely: Levenberg-Marquardt (LM) algorithm and Scale Conjugate Gradient (SCG) algorithm. The four-input parameters considered as; fuel-air equivalence ratio, hydrogen fraction, methane fraction, and carbon monoxide fraction while output parameter is laminar burning velocity. The model training has been accomplished using hyperbolic tangent transfer activation function (tansig) and various number of neurons.The maximum and minimum value of correlation coefficient (R) and mean square error (MSE) for validation were obtained for laminar burning velocity. The values of R (max-min) and MSE (max-min) are 0.9987–0.9944 & 2.1305–1.0195 for Levenberg-Marquardt (LM) algorithm and 0.9990–0.9952 & 2.7965–0.9501 for Scale Conjugate Gradient (SCG) algorithm. Presented article not only provides the experimental study of LBV as well as explores the possibility of artificial neural network application in combustion research area.