Publications Internationales

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

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Subject: search.filters.subject.Gene expression programming

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    Modeling wax disappearance temperature using robust white-box machine learning
    (Elsevier Ltd, 2024) Nait Amar, Menad; Zeraibi, Noureddine; Benamara, Chahrazed; Djema, Hakim; Saifi, Redha; Gareche, Mourad
    Wax deposition is one of the major operational problems encountered in the upstream petroleum production system. The deposition of this undesirable scale can cause a variety of challenging problems. In order to avoid the latter, numerous parameters associated with the mechanism of wax deposition should be determined precisely. In this study, a new smart correlation was proposed for the accurate prediction of Wax disappearance temperature (WDT) using a robust explicit-based machine learning (ML) approach, namely gene expression programming (GEP). The correlation was developed using comprehensive experimental measurements. The obtained results revealed the promising degree of accuracy of the suggested GEP-based correlations. In this context, the newly-introduced correlations provided excellent statistical metrics (R2 = 0.9647 and AARD = 0.5963 %). Furthermore, performance of the developed correlation outperformed that of many existing approaches for predicting WDT. In addition, the trend analysis performed on the outcomes of the proposed GEP-based correlations divulged their physical validity and consistency. Lastly, the findings of this study provide a promising benefit, as the newly developed correlations can notably improve the adequate estimation of WDT, thus facilitating the simulation of wax deposition-related phenomena. In this context, the proposed correlations can supply the effective management of the production facilities and improvement of project economics since the provided correlation is a simple-to-use decision-making tool for production and chemical engineers engaged in the management of organic deposit-related issues.
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    Turbulent forced convective flow in a conical diffuser : hybrid and single nanofluids
    (Elsevier, 2023) Iachachene, Farida; Haddad, Zoubida; Arıcı, Müslüm; Jamei, Mehdi; Mataoui, Amina
    Turbulent forced convective flow of hybrid and single nanofluids in a conical diffuser is investigated numerically. Simulations are conducted for various Reynolds (Re=10000-70000) and different concentrations (ϕ=0-1.5 vol%) at equal ratio of TiO2:SiO2. The impact of using theoretical and experimental correlations for dynamic viscosity and thermal conductivity on turbulent forced convection of TiO2 showed that the mean Nusselt (Nu) number is considerably reduced with the use of the experimental model. However, when the theoretical model is used, Nu varies insignificantly. Addition of TiO2 nanoparticles decreases the heat transfer inside the diffuser, whereas addition of TiO2–SiO2 nanoparticles either enhances or decreases the heat transfer rate. Compared to the pure fluid, hybrid nanofluids show a maximum enhancement of 5% and a maximum decrease of 9.7% at ϕ= 0.5 vol% and ϕ= 0.5 vol% at Re=10000, respectively. However, TiO2 nanofluids show a maximum decrease of 19% at ϕ=1.5 vol% and Re= 30000. As Re increases, the deviations between TiO2 and SiO2-TiO2 nanofluids diminish. Moreover, the Gene Expression Programming model can accurately evaluate Nu versus Re number and nanoparticle concentration