genetic algorithm (ga)

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.

Motor oils have different physicochemical properties, namely viscosity, viscosity index, flash point, pour point, etc. Viscosity is one of the important properties of motor oils since all the properties of industrial lubricants are referred to as their viscosities. The changes in viscosity with variation in temperature are regarded as the viscosity index. The greater the viscosity index, the lower the chances of the viscosity of motor oil with temperature and vice versa. According to the importance of viscosity index in lubricants and because the viscosity index of lubricants is dependent on the chemical composition of motor oils, thus in this study, a simple spectroscopic technique like Fourier Transform InfraRed (FT-IR) spectroscopy was used to analyze the Behran motor oils. The important wavenumbers that affect the viscosity indices were identified by using the Genetic Algorithm (GA) as a variable selection method. By using this method, some functional groups like Alkyl halides, Alkene, Nitro, Acid, Alkane, Alkyne, and Alcohol were recognized that affect the viscosity index of motor oils. Modeling the viscosity index of motor oils was done by Multivariate Linear Regression (MLR) method. Various data preprocessing techniques like Mean Centering and Auto-scaling were operated before the MLR and GA-MLR techniques. The results of modeling were evaluated by using different parameters like regression coefficients (R2) and Root Mean Square Error (RMSE). The values of R2 and RMSE, obtained by the GA-MLR were 0.998 and 0.954 respectively.