Isentropic compressibility is one of the significant properties of biofuel. On the other hand, the complexity related to theexperimental procedure makes the detection process of this parameter time-consuming and hard. Thus, we propose a newMachine Learning (ML) method based on Extreme Learning Machine (ELM) to model this important value. A real databasecontaining 483 actual datasets is compared with the outputs predicted by the ELM model. The results of this comparison showthat this ML method, with a mean relative error of 0.19 andR2values of 1, has a great performance in calculations related to thebiodieselfield. In addition, sensitivity analysis exhibits that the most efficient parameter of input variables is the normal meltingpoint to determine isentropic compressibility.
