Publications Scientifiques

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Author: search.filters.author.Ben Seghier, Mohamed El AmineHas files: No

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    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, Hocine
    Background: - 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 Engineers
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    Modified response surface method basis harmony search to predict the burst pressure of corroded pipelines
    (Elsevier Ltd, 2018) Ben Seghier, Mohamed El Amine; Behrooz, Keshtegar
    The accurate burst pressure prediction of pipelines with corrosion defects is important to provide a suitable design of water, oil, and gas pipes networks. Generally, the empirical burst pressure models for corroded pipelines have the narrow limitation for large-verity of steel grades. In this paper, a modified response surface model is proposed based on the novel learning procedure using harmony search algorithm to predict the burst pressure of corroded pipelines with different steel grades named as HS-MRSM. The nonlinear relation as a power and high-order polynomial functions is calibrated using improved harmony search for large experimental corroded pipes >572 in HS-MRSM model. The performances for both accuracy and agreement predictions of the HS-MRSM are compared with modified response surface method (MRSM) and existing empirical models using comparative statistics as root mean square error (RMSE), mean absolute error (MAE), the Nash-Sutcliffe Efficiency (NSE), and the Willmott index of agreement (d). The results demonstrated that the proposed HS-MRSM is significantly improved The burst pressure predictions of corroded pipelines compared to best empirical model and MRSM. Generally, the empirical models – based PCORRC format are performed the best predictions among other empirical models
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    Structural reliability of corroded pipeline using the so-called Separable Monte Carlo method
    (Sage journals, 2018) Ben Seghier, Mohamed El Amine; Bettayeb, Mourad; Correia, José; De Jesus, Abílio
    The evaluation of the failure probability of corroded pipelines is an important calculation to quantify the risk assessment and integrity of pipelines. Traditional Monte Carlo simulation method has been widely used to solve this type of problems, where it generates a very large number of simulations and takes longer time in computing. In this study, enhanced computational method called Separable Monte Carlo is employed to evaluate the time-dependent reliability of pipeline segments containing active corrosion defects, where a practical example was used. The results show that the Separable Monte Carlo simulation method not only minimizes the computational cost strongly but also improves the calculation precision.