Publications Scientifiques

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Author: search.filters.author.Ghriga, Mohammed AbdelfetahHas files: No

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    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, Bruno
    Polyethylenimine 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 condition
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    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, Hocine
    Ionic 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 temperature
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    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, Abdolhossein
    Ionic 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.