Estimating Ternary Blends Properties using ANNs Trained with Binary Blends

Abstract

The present study aimed to estimate the physical properties of the diesel-diesel-biodiesel ternary blends, using artificial neural networks, also known as ANNs. The input data used to estimate the properties was the percentage in which each component was used to obtain the blend. Using two hydrofined diesel fuels from a local refinery and three biodiesel samples synthesized in the university laboratory, a total of 114 blends, both binary and ternary, were obtained. The ANN training database was comprised of exclusively 96 binary blends, from the total of 114. The predictions were made on the remaining 18 ternary blends. All the predictions were within the error mentioned in the standard, concluding the fact that the created ANNs had a rate of 100% accuracy.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

“…Powerful modelling techniques are available that can be used to identify highly complex, nonlinear relationships between input and output data and neural networks. Artificial neural networks (ANNs) describe such relations by choosing network weights, using a trial-and-error-based calculation method and a training algorithm, such as the Levenberg-Marquardt algorithm [15][16][17].…”