Experimental and modeling studies on adsorption of a nonionic surfactant on sandstone minerals in enhanced oil recovery process with surfactant flooding

Simulation of kinetic behavior of natural surfactants adsorption using a new robust approach

“…Models that are developed on the basis of a dataset that covers a wide range of conditions and is comprehensive are more reliable. In this study, nearly all experimental adsorption data of natural surfactants, which are applicable for chemical enhanced oil recovery, are gathered from literature . Natural surfactants including Trigoonella foenum‐graceum (TFG), Zizyphus Spina Christi (ZSC), and Glycyrrhiza Glabra (GG) and their adsorption density data for both sandstone and carbonate minerals were selected for developing the model.…”

“…Natural surfactants including Trigoonella foenum‐graceum (TFG), Zizyphus Spina Christi (ZSC), and Glycyrrhiza Glabra (GG) and their adsorption density data for both sandstone and carbonate minerals were selected for developing the model. As indicated in literature, these natural surfactants could be used for enhanced oil recovery; therefore, predicting their kinetic adsorption behavior is important. The properties of the used data are summarized in Table .…”

Prediction of solubility of solid compounds in supercritical CO2 using a connectionist smart technique

“…Intelligent methods including the artificial neural network (ANN) [41][42][43], Adaptive Neuro-Fuzzy Inference System (ANFIS) models [44,45], Least Square Support Vector Machine (LS-SVM) [46][47][48][49], Multilayer Perceptron Neural Network (MLP-NN) [37,[50][51][52], and Radial Basis Function Neural Networks (RBF-NN) [38,48,[53][54][55][56][57] are amazingly robust and reliable tools for data analysis and interpretation that can be employed to predict regression and classification problems. The Fuzzy Logic concept (FL), which was introduced by Zade [58] for the first time to handle the complex and nonlinear systems has gained much attention in modeling and solving different problems in various areas of science.…”

“…Hence, taking into account the growing application of SCFs in industrial processes, the need for global, accurate, and reliable techniques for solubility prediction of solid components in supercritical CO2 is of crucial importance. Nowadays, soft computing approaches are well recognized as beneficial robust tools, which take a significant part in analyzing and unraveling challenging problems in various scopes of science and engineering [33][34][35][36][37][38][39][40]. This study highlights the application of an intelligent approach namely adaptive neuro-fuzzy inference system for this purpose.…”

Prediction of H2S Solubility in Liquid Electrolytes by Multilayer Perceptron and Radial Basis Function Neural Networks

“…Beside these methods, there are intelligence techniques such as least square support vector machines (LSSVMs) 13–15, fuzzy logic (FL) 14, 16, genetic algorithms (GAs) 14, 16, and artificial neural networks (ANNs) 17–24, which have found wide application in many scopes of science and engineering, as a result of their capacity in analysis and modeling ambiguous and complex subjects, which were difficult and complicated to solve in the past. Eslamimanesh et al 25 applied the ANN method to predict the supercritical CO 2 solubility in 24 mostly used ILs.…”