STEFAN SANDRU, ION ONUTU ESTIMATING TERNARY BLENDS PROPERTIES USING ANNS TRAINED WITH BINARY BLENDS 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.