Prediction of Laminar Burning Speed of Propane/Hydrogen/Air Mixtures Using Power-Law Correlation and Two Machine Learning Models

Abstract

Abstract Propane (C3H8) and hydrogen (H2) are regarded as alternative fuels that are favorable to the environment. Hydrogen gas's low energy density, storage, and transportation are the main issues with using it as an alternative fuel. Addition of hydrogen gas in the combustion of propane will also improve flame stability, broaden lean flammability limits, and reduces pollutant emissions. Thus, utilizing propane and hydrogen mixtures as fuel is a good choice. Laminar burning speed is a fundamental property of a combustible mixture and can be used to provide information regarding the mixture’s reactivity, exothermicity, and diffusivity. In this study, power-law correlation and machine learning methods were used to create models that predict the laminar burning speed of propane/hydrogen/air mixtures at various states. Two machine learning models are artificial neural network (ANN) and support vector machine (SVM). The data were generated by using CANTRA code and a chemical kinetic mechanism. For a wide variety of input values, the models were able to determine the laminar burning speed with great accuracy. The ANN model yields the best performance. The main advantage of these models is the noticeably faster computing time when compared to chemical reaction mechanisms.