A simple model to predict the biodiesel blend density as simultaneous function of blend percent and temperature

Area of Science:

• Chemical Engineering
• Materials Science

Background:

• Biodiesel blends are crucial for sustainable energy.
• Accurate density prediction is vital for fuel quality control and process design.

Purpose of the Study:

• To propose a simple and accurate method for estimating biodiesel blend density.
• To investigate the density of vegetable oil biodiesel blends as a function of temperature and composition.

Main Methods:

• Development of a theoretical model based on Kay's mixing rule.
• Investigation of biodiesel blend density as a function of temperature and volume percent of biodiesel.
• Validation of the model using experimental data.

Main Results:

• The proposed model accurately estimates biodiesel blend density.
• Density decreases linearly with increasing temperature.
• Density increases with increasing biodiesel volume percentage.
• Low Standard Estimate of Error (SEE) and Absolute Average Deviation (AAD) values demonstrate high predictive capability.

Conclusions:

• The developed model offers a straightforward and effective approach for predicting biodiesel blend density.
• The model's accuracy is confirmed by good agreement with experimental data.
• This method simplifies density estimation for biodiesel blends, aiding in their practical application.