Predicting the standard enthalpy (triangle H-f(0)) and entropy (S-0) of alkanes by artificial neural networks

Artificial Neural Networks (ANNs) with Extended Delta-Bar-Delta (EDBD) back propagation learning algorithm have been developed to predict the standard enthalpy and entropy of 87 acyclic alkanes. Molecular weight, boiling poin t and density of the compounds were used as input parameters. The network's architecture and parameters were optimized to give maximum performances. T he best network was a 3-6-2 ANN, and the optimum learning epoch was about 1 320. The results show that the maximum relative errors of enthalpy and entr opy are less than 3%. They reveal that the performances of ANNs for predict ing the enthalpy and entropy of alkanes are satisfying.