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.

The Karun River is the biggest river basin in Iran, which supplies water demands of about 16 cities, several villages, thousands of hectares of agricultural. This river polluted because of domestic and urban sewerage, industrial sources, and irrigation of agricultural land, Hospital sewage and high tide level of Persian Gulf.
Therefore, because of the importance of this river, the water salinity of this river is determined in this study. The traditional methods of determining water salinity are costly in comparison with remote sensing methods.
In the present study, Landsat 8 (OLI) data was used to calculate the water salinity map for Karun River since not only it is free, but it also has an acceptable resolution.

Landsat 8 (OLI) images were used to calculate reflectance for a pixel and were attained from (US Geological Survey (USGS) 2019). First, radiometric correction was applied to normalize satellite images. This process convert Digital Number into radiance. Second, in order to attain the surface reflectance values, the process of atmospheric correction was applied using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH).
Water salinity was calculate by Iran Water and Power Recourses Development Company. Eight stations are located in the crucial point for EC measuring ALIKALE, GOTVAND, SHOOSHTAR, SHOTEYT, GARGAR, DEZ, AHVAZ, and ABADAN.
Iran Water and Power Recourses Development Company obtained 102 observed EC samples from June 2013 to July 2018 along the Karun River.
The Support Vector Machine was classically used for classification, Support Vector Classification, but extended for using along with regression issue, namely Support Vector Regression.
The results related to the quality of the SVR depend on some factors: the loss function Ɛ, the error penalty factor C and the kernel function parameters.
Usually, radial basis kernel function (RBF), k(x, x΄) = k(x,x΄)=exp⁡〖( -||x-x΄〗 2/σ^2), has been used in remote sensing studies, so, it is implemented in this study. Finally, the Genetic Algorithm (GA) is employed to optimize some parameters including C, Ɛ and σ.
GA is an optimization technique create by Holland (1975) and discussed the mechanism of GA in solving nonlinear optimization problems.
Besides, the Genetic Algorithm (GA) was applied to determine the best performer bands combination. Furthermore, we employ GA to optimize SVR parameters and number of layers and neurons of MLP neural network in order to maximize model accuracy.

Salinity intrusion is a complex issue in coastal, hot, and dry areas. Currently, remote sensing techniques have been widely used to monitor water salinity changes, ranging from inland river networks to deep oceans. The Karun River basin, with a basin area of 67,000 km^2, is located in the southern part of Iran. The salinity of Karun River has been increasing due to some critical factors, e.g. severe climate condition and regional physiography, industrial sources, domestic and urban sewerage, irrigation of agricultural land, fish hatchery, hospital sewage, and high tide level of Persian Gulf .
This study aimed at building Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models to realize the salinity intrusion through the relationship between reflectance from the Landsat-8 Operational Land Imager images and salinity levels measured in situ. 102 observed samples were divided into 75% training and 25% test.
Besides, the Genetic Algorithm (GA) was applied to determine the best performer bands combination. Furthermore, we employ GA to optimize SVR parameters and number of layers and neurons of MLP neural network in order to maximize model accuracy.
The result showed that the MLP approach was the better model to estimate water salinity along the Karun River network in the study area, which coefficient of determination (R^2) and RMSE of test data is obtained as 0.73 and 390μs 〖cm〗^(-1).
GA analysis proved that bands 1, 2 and 3 are the best for modeling water salinity. In this study, the GA is used to determine the SVR meta-parameters including the loss function Ɛ, the error penalty factor C and σ parameters, which are obtained to be〖1×10〗^(-9), 1099 and 0.96, respectively, and number of layers and neurons of MLP neural network, which are obtained to be 5 and 35, respectively.
The result showed that the MLP approach was the better model to estimate water salinity along the Karun River network in the study area, which coefficient of determination (R^2) and RMSE of test data is obtained as 0.73 and 390μs 〖cm〗^(-1).

The present study calculated the relationship between reflectance retrieved from Landsat-8 OLI and water salinity in the Karun River. SVR and MLP models had acceptable operation by considering the large size, geographic complexity of the study domain and the wide range of field data that change between 385 and 4310μs cm^(-1). Augmentation field data is the critical priority work for future study to probe the relationship between water salinity and satellite images.In addition, the contribution of thermal bands can help to increase accuracy of models. Salinity intrusion is a complex issue in coastal and hot and dry areas. Currently, remote sensing techniques have been widely used to monitor water salinity changes, ranging from inland river networks to deep oceans. The Karun River basin, with a basin area of 67,000 km2, is located in the southern part of Iran. The salinity of Karun River has been increasing due to some critical factors, e.g. severe climate condition and regional physiography, industrial sources, domestic and urban sewerage, irrigation of agricultural land, fish hatchery, hospital sewage, and high tide level of Persian Gulf .This study aimed at building Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models to realize the salinity intrusion through the relationship between reflectance from the Landsat-8 Operational Land Imager images and salinity levels measured in situ. A total of 102 observed samples were divided into 75% training and 25% test. Besides, the Genetic Algorithm (GA) was applied to determine the best performer bands combination. Furthermore, we employ GA to optimize SVR parameters and number of layers and neurons of MLP neural network in order to maximize model accuracy. The result showed that the MLP approach was the better model to estimate water salinity along the Karun River network in the study area, which coefficient of determination (R2) and RMSE of test data is obtained as 0.73 and 390μscm-1