A machine learning framework for evaluating the biodiesel properties for accurate modeling of spray and combustion processes

Abstract

The environment-friendly nature of biodiesel coupled with its inherent compression ignition fuel characteristics like better ignition quality, and fuel bound oxygen may reduce sooting combustion, and attract wide attention. The variation of thermophysical properties with temperature is essential for accurately modeling the combustion and emission characteristics of biodiesel fuelled diesel engines. Hence, the current paper attempts to predict the thermo-physical properties such as density, viscosity, specific heat, thermal conductivity, latent heat of vaporization, vapor pressure, mass diffusivity etc., and their variation with respect to the temperature following a machine learning approach for biodiesel fuels. Twenty feedstock samples are chosen for predicting the biodiesel thermo-physical properties and are validated with the available experimental data within a maximum error of 14 %. To evaluate the thermo-physical properties of a variety of feedstock, the predicted data are used for training an artificial neural network (ANN) and the developed ANN function is used for generating the various thermophysical properties of biodiesel fuels based on their composition and temperature. A good agreement is observed between the predicted and ANN results with a maximum mean absolute percentage error of 10 %. Hence in the current work, a collective mathematical modeling approach is proposed to accurately determine the spray and combustion-related fuel properties. The proposed model provides the various liquid and vapor properties and their variation with temperature which could be effectively employed for detailed spray and biodiesel-combustion modeling. An exhaustive data generation of eleven major liquid and vapour properties of twenty biodiesel fuels within the actual temperature range from 300 K to their respective critical temperature is presented.