Publications Scientifiques
Permanent URI for this communityhttps://dspace.univ-boumerdes.dz/handle/123456789/10
Browse
5 results
Search Results
Item Predicting solubility of nitrous oxide in ionic liquids using machine learning techniques and gene expression programming(Elsevier, 2021) Nait Amar, Menad; Ghriga, MMohammed Abdelfetah; Ben Seghier, Mohamed El Amine; Ouaer, HocineBackground: - Nitrous oxide (N2O), as a potent greenhouse gas, is increasingly becoming a major multidisciplinary concern in recent years. Therefore, the removal of N2O using powerful green solvents such as ionic liquids (ILs) has turned into an attractive way to reduce the amount of N2O in the atmosphere. Methods: -The aim of this study was to establish rigorous models that can predict the solubility of N2O in various ILs. To achieve this, three advanced soft-computing methods, viz. cascaded forward neural network (CFNN), radial basis function neural network (RBFNN), and gene expression programming (GEP) were trained and tested using comprehensive experimental measurements. Significant Findings: - The obtained results demonstrated that the newly implemented models can predict the solubility of N2O in ILs with high accuracy. Besides, it was found that the CFNN model optimized using Levenberg-Marquardt (LM) algorithm was the best predictive paradigm (R2=0.9994 and RMSE=0.0047). Lastly, the Leverage technique was carried out, and the statistical validity of the newly implemented model was documented as more than 96% of data were located in the applicability realm of this paradigm. © 2021 Taiwan Institute of Chemical EngineersItem On the evaluation of solubility of hydrogen sulfide in ionic liquids using advanced committee machine intelligent systems(Elsevier, 2021) Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Ouaer, HocineIonic Liquids (ILs) are increasingly emerging as new innovating green solvents with great importance from academic, industrial, and environmental perspectives. This surge of interest in considering ILs in various applications is owed to their attractive properties. Involvements in the gas sweetening and the reduction of the amounts of sour and acid gasses are among the most promising applications of ILs. In this study, new advanced committee machine intelligent systems (CMIS) were introduced for predicting the solubility of hydrogen sulfide (H2S) in various ILs. The implemented CMIS models were gained by linking robust data-driven techniques, namely multilayer perceptron (MLP) and cascaded forward neural network (CFNN) beneath rigorous schemes using group method of data handling (GMDH) and genetic programming (GP). The proposed paradigms were developed using an extensive database encompassing 1243 measurements of H2S solubility in 33 ILs. The performed comprehensive error investigation revealed that the newly implemented paradigms yielded very satisfactory prediction performance. Besides, it was found that CMIS-GP provided more accurate estimations of H2S solubility in ILs compared with both the other intelligent models and the best-prior paradigms. In this regard, the developed CMIS-GP exhibited overall average absolute relative deviation (AARD) and coefficient of determination (R2) values of 2.3767% and 0.9990, respectively. Lastly, the trend analyses demonstrated that the tendencies of CMIS-GP predictions were in excellent accordance with the real variations of H2S solubility in ILs with respect to pressure and temperatureItem Modeling viscosity of CO 2 at high temperature and pressure conditions(Elsevier, 2020) Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Ouaer, Hocine; Ben Seghier, Mohamed El Amine; Thai Pham, BinhThe present work aims at applying Machine Learning approaches to predict CO2 viscosity at different thermodynamical conditions. Various data-driven techniques including multilayer perceptron (MLP), gene expression programming (GEP) and group method of data handling (GMDH) were implemented using 1124 experimental points covering temperature from 220 to 673 K and pressure from 0.1 to 7960 MPa. Viscosity was modelled as function of temperature and density measured at the stated conditions. Four backpropagation-based techniques were considered in the MLP training phase; Levenberg-Marquardt (LM), bayesian regularization (BR), scaled conjugate gradient (SCG) and resilient backpropagation (RB). MLP-LM was the most fit of the proposed models with an overall root mean square error (RMSE) of 0.0012 mPa s and coefficient of determination (R2) of 0.9999. A comparison showed that our MLP-LM model outperformed the best preexisting Machine Learning CO2 viscosity models, and that our GEP correlation was superior to preexisting explicit correlations.Item Predicting thermal conductivity of carbon dioxide using group of data-driven models(Elsevier, 2020) Nait Amar, Menad; AshkanJahanbani, Ghahfarokhi; Zeraibi, NoureddineThermal conductivity of carbon dioxide (CO2) is a vital thermophysical parameter that significantly affects the heat transfer modeling related to CO2 transportation, pipelines design and associated process industries. The current study lays emphasis on implementing powerful soft computing approaches to develop novel paradigms for estimation of CO2 thermal conductivity. To achieve this, a massive database including 5893 experimental datapoints was acquired from the experimental investigations. The collected data, covering pressure values from 0.097 to 209.763 MPa and temperature between 217.931 and 961.05 K, were employed for establishing various models based on multilayer perceptron (MLP) optimized by different back-propagation algorithms, and radial basis function neural network (RBFNN) coupled with particle swarm optimization (PSO). Then, the two best found models were linked under two committee machine intelligent systems (CMIS) using weighted averaging and group method of data handling (GMDH). The obtained results showed that CMIS-GMDH is the most accurate paradigm with an overall AARD% and R2 values of 0.8379% and 0.9997, respectively. In addition, CMIS-GMDH outperforms the best prior explicit models. Finally, the leverage technique confirmed the validity of the model and more than 96% of the data are within its applicability realmItem Application of gene expression programming for predicting density of binary and ternary mixtures of ionic liquids and molecular solvents(ELSEVIER, 2020) Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Hemmati-Sarapardeh, AbdolhosseinIonic Liquids (ILs) have received increased attention across a number of disciplines in recent years. This noticeable importance of ILs is attributed to their attractive proprieties. Precise evaluation of the thermophysical properties of ionic liquids and their mixtures with molecular solvents is essential for distinct multidisciplinary applications. In this study, a rigorous white-box intelligent technique, viz. gene expression programming (GEP) was implemented for establishing new correlations for accurate prediction of density of binary and ternary mixtures of ILs and molecular solvents. The newly suggested correlations were developed using a comprehensive experimental database with 1985 real measurements under a variety of operational conditions. The obtained results revealed that the newly established GEP-based correlations can predict the density of binary and ternary mixtures of ILs and molecular solvents with a high degree of integrity. The GEP-based correlations exhibited overall average absolute relative error (AARE) values of 0.5621% and 0.2128% for binary and ternary cases, respectively. Besides, it was found that our proposed explicit correlations followed the expected tendency with respect to the considered variables. Furthermore, the superiority and the reliability of the GEP-based correlations was testified against the best-existing approaches in the literature. Finally, the leverage approach was performed and the statistical validity of the correlations and the experimental data was testified.