Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes

Isentropic compressibility is one of the significant properties of biofuel. On the other hand, the complexity related to the experimental procedure makes the detection process of this parameter time-consuming and hard. Thus, we propose a new Machine Learning (ML) method based on Extreme Learning Machine (ELM) to model this important value. A real database containing 483 actual datasets is compared with the outputs predicted by the ELM model. The results of this comparison show that this ML method, with a mean relative error of 0.19 and R 2 values of 1, has a great performance in calculations related to the biodiesel field. In addition, sensitivity analysis exhibits that the most efficient parameter of input variables is the normal melting point to determine isentropic compressibility.

PHASE TRANSITIONS IN MULTIMOLECULAR LAYERS OF ADSORBED NITROGEN

The heat capacity of nitrogen adsorbed on titanium dioxide has been measured for amounts adsorbed corresponding to 2.2, 3.1, 4.0, and 4.8 molecular layers in the temperature range 14 ° to 75 °K. The occurrence in the adsorbed films of melting and of the transition in the solid phase of nitrogen is shown by maxima in the heat capacity. The melting process is completely reversible and takes place at temperatures below the normal melting point. The solid phase transition does not occur reversibly, the magnitude of the anomaly in the heat capacity depending upon the extent to which the system has been cooled prior to the measurement of the heat capacity. The experimental results are discussed in terms of current thermodynamic treatments of phase transitions in adsorbed films.