Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Author: search.filters.author.Benamara, ChahrazedHas files: No

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    Predicting Methane Hydrate Formation Temperature in the Presence of Diverse Brines Using Explainable Artificial Intelligence
    (American Chemical Society, 2025) Nait Amar, Menad; Zeraibi, Noureddine; Alqahtani, Fahd Mohamad; Djema, Hakim; Benamara, Chahrazed; Saifi, Redha; Gareche, Mourad; Ghasemi, Mohammad; Merzoug, Ahmed
    Thisstudy presents three advanced techniques, includingthe leastsquares support vector machine (LSSVM), categorical boosting (CatBoost),and cascaded forward neural network (CFNN), to model methane hydrateformation temperature (MHFT) across various brines under a wide pressurerange. Utilizing a comprehensive data set of nearly 1000 samples,the models underwent rigorous training and testing phases. Graphicalanalyses and statistical assessment confirmed the high accuracy ofthe implemented models, with the CFNN scheme outperforming the others,achieving a total root-mean-square error (RMSE) of 0.3569 and an R2 of 0.9977. Comparison with existing modelsfurther highlighted the CFNN model’s superior performance.Additionally, the Shapley Additive exPlanning (SHAP) method was employedto enhance the aspects related to predictions’ explainabilityby assessing the impact of different inputs on the outcomes. Lastly,the proposed model holds significant potential for advancing industrialand academic applications related to hydrate phenomena
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    Modeling wax disappearance temperature using advanced intelligent frameworks
    (American Chemical Society, 2019) Benamara, Chahrazed; Nait Amar, Menad; Gharbi, Kheira; Hamada, Boudjema
    The deposition of wax is one of the most potential problems that disturbs the flow assurance during production processes of hydrocarbon fluids. In this study, wax disappearance temperature (WDT) that is recognized as a vital parameter in such circumstances is modeled using advanced machine learning techniques, namely, radial basis function neural network (RBFNN) coupled with genetic algorithm (GA) and artificial bee colony (ABC). Besides, an accurate and user-friendly correlation was established by implementing the group method of data handling. Results revealed the high reliability of the proposed hybrid models and the established correlation. Moreover, RBFNN coupled with ABC (RBFNN-ABC) was found to be the best paradigm with an overall average absolute relative error value of 0.5402% and a total coefficient of determination (R2) of 0.9706. Furthermore, the performance comparison showed that RBFNN-ABC and the established explicit correlation outperform the prior intelligent and thermodynamic models. Finally, by performing the outlier detection, the quality of the utilized database was assessed, the applicability realm of the best model was delineated, and only one point was found as doubtful