Publications Internationales
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Item Predicting the viscosity of hydrogen – methane blends at high pressure for hydrogen transportation and geo-storage: Integration of robust white-box machine learning frameworks(Elsevier, 2025) Alatefi, Saad; Youcefi, Mohamed Riad; Amar, Menad Nait; Djema, HakimThe integration of hydrogen into underground storage systems is pivotal for large-scale energy management, often involving blends with methane to leverage existing infrastructure. Accurate viscosity prediction of hydrogen – methane blends under subsurface conditions is essential for optimizing flow assurance and operational safety. Accordingly, this study employs three data-driven models, namely Genetic Expression Programming (GEP), Group Method of Data Handling (GMDH), and Multi-Gene Genetic Programming (MGGP), to predict the viscosity of hydrogen – methane mixtures for transportation and underground storage applications. A comprehensive dataset of 313 experimentally measured values from the literature were utilized to develop and validate the established correlations. The MGGP paradigm emerged as the top performer, achieving a root mean square error (RMSE) of 0.4054 and an R2 value of 0.9940, outperforming both GEP and GMDH, as well as prior predictive models. The consistency of the dataset was confirmed using the Leverage approach, ensuring robust predictions. In addition, the Shapley Additive Explanations technique revealed key factors influencing the viscosity predictions, enhancing the interpretability of the best-performing correlation. Furthermore, comparative trend analysis demonstrated the MGGP correlation's superior accuracy and robustness across varying blend compositions and operational conditions. These findings offer a reliable and simple-to-use predictive correlation for engineers and researchers designing hydrogen transport and storage systems, supporting efficient energy storage and the transition to a low-carbon economyItem Using Machine Learning Algorithms for the Analysis and Modeling of the Rheological Properties of Algerian Crude Oils(Taylor and Francis Ltd., 2024) Souas, Farid; Oulebsir, RafikOur research described in this report investigated the rheological behavior of crude oils from the Tin Fouye Tabankort oil field in Southern Algeria, focusing on their viscosity under varying temperatures (10 °C–50 °C). The results show that the oils exhibited non-Newtonian shear-thinning behavior at low shear rates, with the viscosity decreasing as the temperature was increased. At higher shear rates, the Herschel–Bulkley model accurately described the oils’ transition to Newtonian behavior. Machine learning models, including CatBoost, LightGBM, and XGBoost, were trained on the experimental data to predict the viscosity, with CatBoost and XGBoost showing superior performance. We suggest these findings are valuable for improving the efficiency of oil transportation and processing.Item Viscosity-boosting effects of polymer additives in automotive lubricants(Springer Nature, 2024) Boussaid, Mohamed; Haddadine, Nabila; Benmounah, Abdelbaki; Dahal, Jiba; Bouslah, Naima; Benaboura, Ahmed; El-Shall, SamyThis study investigated polyethylene glycol (PEG), as a polymer improver of the paraffinic oil viscosity index (VI). The characterization of PEG/paraffinic oil blends at different concentrations (0%, 1%, 2%, 3%, 5%, and 10%), was performed using Raman spectroscopy and optical microscopy. The rheological parameters as the viscosity index and activation energy were determined using the kinematic viscosity measurements. Results showed that the VI improvement reached an optimal value for the blend containing 3% PEG, with greater value for blends containing 2% PEG than 5 and 10% PEG. The presence of polymer particles was observed by optical microscopy, which confirmed the lack of PEG distribution in the blend containing 5%, and more, whereas mixtures with 3 and 2% PEG exhibited good particle distribution, evidenced by smaller polymer particle sizes. This finding was corroborated by Raman spectroscopy, which revealed the absence of polymer–oil intermolecular interactions in the PEG/paraffinic oil blends. The rheological tests showed that increasing the blend temperature from 40 to 80 ℃, improved the PEG chains dispersion in the paraffin oil, for the blends containing up to 3% PEG. The difference of the activation energy of the pure paraffinic oil and the PEG/paraffinic oil blends, (ΔEa) was calculated, and the correlation between the ΔEa and the viscosity index values was established. Therefore, adding PEG to paraffinic oil appeared to be promising for the viscosity index improvement and promote industrial applications of paraffinic oil.Item Comparative studies of the rheological behavior and microstructural properties of emulsions (oil/distilled water phase) and (oil/Lias water phase)(Taylor and Francis, 2018) Djemiat, Djamal Eddine; Safri, Abdelhamid; Benmounah, AbdelbakiThe rheological behavior of crude oil and their emulsions were investigated as a function of two water types (distilled water and the LIAS water). The focus of this work is to obtain more knowledge about the effect of LIAS water concentration, which used to maintain pressure and produced from production of crude oil in the oil fields Tin Fouye Tabankort-south Algeria, on the rheological properties of crude oil. The rheological parameters were measured by using AR-2000 rheometer at 15 °C under dynamic and shear testing conditions. The measured data were first classified into two groups for Newtonian and non-Newtonian fluids. Depends on the type and concentration of water, the non-Newtonian behavior was described in better way by the Casson, Power law and the Herschel–Bulkley models. The results indicated that the viscosity, the yield stress, the elastic modulus, (G′), the loss modulus, (G″), and the microstructure of the prepared emulsions not only varied with water concentration but also by water types.