A hybrid GA-ANN and correlation approach to developing a laminar burning velocity prediction model for isooctane/blends-air mixtures

Abstract

Surrogate fuels offer a cost-effective way to predict the combustion properties of transportation fuels like diesel, gasoline, kerosene, etc. Iso-octane (2,2,4-trimethylpentane) is a key gasoline reference and surrogate component. Researchers explore alternative fuels and their combustion characteristics to enhance efficiency and reduce emissions. The rising interest lies in the blending of isooctane with various alternative fuels, aiming for cleaner and more efficient combustion. In this current study, a machine learning method called Feed-Forward Artificial Neural Network (FFANN) with back-propagation (BP) was employed to forecast the laminar burning velocity (LBV) of isooctane/blends – air mixtures. A total of eleven blends including ammonia, hydrogen, methane, methanol, ethanol, butanol, n-heptane, 2-methyl furan (2-MF), 2,5-dimethylfuran (2,5-DMF), 2-methyl tetrahydrofuran (2-MTHF), and syngas were examined. The artificial neural network (ANN) model was created using a dataset consisting of 2234 data points gathered from the past experimental literature since 1983. To enhance the ANN's predictive capability, a combination of the random search CV technique and selective testing approach was utilized for optimizing ANN hyperparameters, while the genetic algorithm (GA) was deployed to optimize the ANN's weight values. The development of the ANN model was carried out within the Python software environment, utilizing the Keras application programming interface. The constructed GA-ANN model was compared to a variety of other machine learning (ML) models developed within this study, including generalized linear regression (GLR), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and XGBoost regression. When evaluated on the testing set, which constitutes 15% of the complete dataset, the GA-ANN model demonstrated superior performance compared to all other ML models utilized in this research, achieving an impressive prediction accuracy with the coefficient of determination (R2) of 0.9910, root mean square error (RMSE) of 0.8231, and a mean absolute error (MAE) of 0.643. Additionally, a common LBV correlation for all isooctane/blend-air mixtures was created using the extended Gulder’s LBV formulation with the input parameters including pressure, temperature, equivalence ratio, and molar fraction of blend. This correlation results showed very minimum deviation from the experimental with an RMSE value of less than 0.38541.