Using ANFIS and MLP Neural Networks in Predicting the Extraction of Aromatic Compounds from Aliphatic Compounds by Ionic Liquids

One of the main processes in the refining industries of the oil industry is the extraction of aromatic hydrocarbons from aliphatic hydrocarbons. Accordingly, accurate prediction of the phase behavior of these systems can improve liquid-liquid extraction.  In this study, the phase thermodynamic behavior of the ternary system of aliphatic and aromatic hydrocarbons with ionic liquids is predicted by the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Multilayer Perceptron (MLP)neural network. The model inputs were considered in modeling the liquid-liquid extraction system, the molar ratio of aliphatic, aromatic, and ionic compounds in the feed, as well as the molecular mass of the ions and the temperature of the extraction system, and the model output was the molar ratio. Aliphatic and aromatic compounds in the alkane-rich phase and molar ratio of aromatic compounds and ionic liquids in the iron-rich phase were considered. The design parameters of these neural networks, including the number of neurons and the clustering radius of the MLP and ANFIS networks, were optimized by the genetic algorithm evolution method (GA) in order to improve their prediction accuracy. Comparison of prediction accuracy of ANFIS and MLP networks with experimental data based on statistical parameters R2, RMSD, and MAD for ANFIS model was calculated 0.9999, 0.0190, and 0.0129 respectively and for MLP neural network model was 0.996, 0.0204, and 0.0127 respectively. Also, a comparison was made between the prediction accuracy of ANFIS, MLP networks and the NRTL thermodynamic model for two different liquid-liquid extraction systems, their RMSD for the two extraction systems were 0.0093, 0.0110, and 0.0113, respectively. The results of statistical parameters show that these networks have relatively good accuracy in predicting the thermodynamic behavior of liquid-liquid equilibrium and are an effective method.