Publications Scientifiques
Permanent URI for this communityhttps://dspace.univ-boumerdes.dz/handle/123456789/10
Browse
9 results
Search Results
Item Thermal gelation of partially hydrolysed polyacrylamide/polyethylenimine mixtures using design of experiments approach(Elsevier, 2019) Ghriga, Mohammed Abdelfetah; Hasanzadeh, Mahdi; Gareche, Mourad; Lebouachera, Seif El Islam; Drouiche, Nadjib; Grassl, BrunoPolyethylenimine crosslinked polymer gels are gaining a huge interest in conformance control applications in oilfields. They are used to reduce the production of undesirable fluids (water & gas) by blocking the fractures that connect injection and production wells. In this paper, a statistical analysis on the thermal gelation of well characterized reactants namely partially hydrolysed polyacrylamide (PHPA) (Mw = 5.1 million Daltons and hydrolysis degree = 6%) and polyethylenimine (PEI) (Mw = 19.2 kilo Daltons and branching degree = 59%), was conducted using response surface methodology (RSM). A four factor doehlert matrix was employed in designing the experiments and evaluating the gelation time as function of salinity (0–8 g/L NaCl), polymer (PHPA) and crosslinker (PEI) concentrations, temperature (70 °C–90 °C) and their corresponding combinations. As a result, the gelation time was found to strongly vary with salinity, temperature and PHPA concentration following a nonlinear mathematical model. The analysis of variance (ANOVA) of this model revealed its significance in a 95% confidence level against experimental data. In a second part, an experimental investigation was carried out to understand the interaction between PHPA and PEI. To do so, the viscosity variations of analogue mixtures prepared with low molecular weight (Mw) polymers, such as polyacrylamide (PAM) and polyacrylic acid (PAA), were monitored using capillary viscometry at different conditions of temperature, pH and reaction time. The PAM/PEI mixtures showed a remarkable viscosity increase at typical pH of around 10 when cured at 80 °C. While, the PAA/PEI mixtures underwent precipitation at pH of around 6 revealing the strong interaction between PAA and PEI at this conditionItem 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 Rigorous connectionist models to predict carbon dioxide solubility in various ionic liquids(MDPI AG, 2020) Ouaer, Hocine; hossein hosseini, amir; Nait Amar, Menad; Ben Seghier, Mohamed El Amine; Ghriga, Mohammed Abdelfetah; Nabipour, Narjes; Pål Østebø, Andersen; Mosavi, Amir; Shamshirband, ShahaboddinEstimating the solubility of carbon dioxide in ionic liquids, using reliable models, is ofparamount importance from both environmental and economic points of view. In this regard,the current research aims at evaluating the performance of two data-driven techniques, namelymultilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubilityof carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and fourthermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimentaldata points derived from the literature including 13 ILs were used (80% of the points for training and20% for validation). Two backpropagation-based methods, namely Levenberg–Marquardt (LM) andBayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical andgraphical assessments were applied to check the credibility of the developed techniques. The resultswere then compared with those calculated using Peng–Robinson (PR) or Soave–Redlich–Kwong(SRK) equations of state (EoS). The highest coefficient of determination (R2=0.9965) and the lowestroot mean square error (RMSE=0.0116) were recorded for the MLP-LMA model on the full dataset(with a negligible difference to the MLP-BR model). The comparison of results from this model withItem 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.Item Evolving support vector regression using Grey Wolf optimization; forecasting the geomechanical properties of rock(Springer link, 2020) Xu, Chuanhua; Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Ouaer, Hocine; Zhang, Xiliang; Hasanipanah, MahdiThe geomechanical properties of rock, including shear strength (SS) and uniaxial compressive strength (UCS), are very important parameters in designing rock structures. To improve the accuracy of SS and UCS prediction, this study presented an evolving support vector regression (SVR) using Grey Wolf optimization (GWO). To examine the feasibility and applicability of the SVR-GWO model, the differential evolution (DE) and artificial bee colony (ABC) algorithms were also used. In other words, the SVR hyperparameters were tuned using the GWO, DE, and ABC algorithms. To implement the proposed models, a comprehensive database accessible in an open-source was used in this study. Finally, the comparative experiments such as root mean square error (RMSE) were conducted to show the superiority of the proposed models. The SVR-GWO model predicted the SS and UCS with RMSE of 0.460 and 3.208, respectively, while, the SVR-DE model predicted the SS and UCS with RMSE of 0.542 and 5.4, respectively. Furthermore, the SVR-ABC model predicted the SS and UCS with RMSE of 0.855 and 5.033, respectively. The aforementioned results clearly exhibited the applicability as well as the usability of the proposed SVR-GWO model in the prediction of both SS and UCS parameters. Accordingly, the SVR-GWO model can be also applied to solving various complex systems, especially in geotechnical and mining fieldsItem Prediction of Lattice Constant of A 2 XY 6 Cubic Crystals Using Gene Expression Programming(2020) Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Ben Seghier, Mohamed El Amine; Ouaer, HocineLattice constant is one of the paramount parameters that mark the quality of thin film fabrication. Numerous research efforts have been made to calculate and measure lattice constant, including experimental and empirical approaches. Not withstanding these efforts, a reliable and simple-to-use model is still needed to predict accurately this vital parameter. In this study, gene expression programming (GEP) approach was implemented to establish trustworthy model for prediction of the lattice constant of A2XY6 (A = K, Cs, Rb, TI; X = tetravalent cation; and Y = F, Cl, Br, I) cubic crystals based on a comprehensive experimental database. The obtained results showed that the proposed GEP correlation provides excellent prediction performance with an overall average absolute relative deviation (AARD%) of 0.3596% and a coefficient of determination (R2) of 0.9965. Moreover, the comparison of the performance between the newly proposed correlation and the best pre-existing paradigms demonstrated that the established GEP correlation is more robust, reliable, and efficient than the prior models for prediction of lattice constant of A2XY6 cubic crystalsItem Hybrid soft computational approaches for modeling the maximum ultimate bond strength between the corroded steel reinforcement and surrounding concrete(Springer, 2020) Ben Seghier, Mohamed El Amine; Ouaer, Hocine; Ghriga, Mohammed Abdelfetah; Nait Amar, Menad; Duc-Kien, ThaiThe capacity efficiency of load carrying with the accurate serviceability performances of reinforced concrete (RC) structure is an important aspect, which is mainly dependent on the values of the ultimate bond strength between the corroded steel reinforcements and the surrounding concrete. Therefore, the precise determination of the ultimate bond strength degradation is of paramount importance for maintaining the safety levels of RC structures affected by corrosion. In this regard, hybrid intelligence and machine learning techniques are proposed to build a new framework to predict the ultimate bond strength in between the corroded steel reinforcements and the surrounding concrete. The proposed computational techniques include the multilayer perceptron (MLP), the radial basis function neural network and the genetic expression programming methods. In addition to that, the Levenberg–Marquardt (LM) deterministic approach and two meta-heuristic optimization approaches, namely the artificial bee colony algorithm and the particle swarm optimization algorithm, are employed in order to guarantee an optimum selection of the hyper-parameters of the proposed techniques. The latter were implemented based on an experimental published database consisted of 218 experimental tests, which cover various factors related to the ultimate bond strength, such as compressive strength of the concrete, concrete cover, the type steel, steel bar diameter, length of the bond and the level of corrosion. Based on their performance evaluation through several statistical assessment tools, the proposed models were shown to predict the ultimate bond strength accurately; outperforming the existing hybrid artificial intelligence models developed based on the same collected database. More precisely, the MLP-LM model was, by far, the best model with a determination coefficient (R2) as high as 0.97 and 0.96 for the training and the overall data, respectively.Item Review of recent advances in polyethylenimine crosslinked polymer gels used for conformance control applications(Springer, 2019) Ghriga, Mohammed Abdelfetah; Grassl, Bruno; Gareche, Mourad; Khodja, Mohamed; Lebouachera, Seif El Islam; Andreu, Nathalie; Drouiche, Nadjib