Sonatrach

About: Sonatrach is a company organization based out in Algiers, Algeria. It is known for research contribution in the topics: Hydraulic fracturing & Structural basin. The organization has 460 authors who have published 494 publications receiving 6339 citations. The organization is also known as: Sonatrach SPA.

Synthesis of PP‐g‐MAH and evaluation of its effect on the properties of glass fibre reinforced nylon 6/polypropylene blends

Abstract: Polymer blends based on polyolefins constitute materials of a great interest owing to their broad spectrum of properties and practical applications. However, due to the poor compatibility of the components, most of these systems are generally characterized by high interfacial tension, low degree of dispersion and poor mechanical properties. It is generally accepted that PP and Nylon 6 are not compatible and that blending of these materials results in poor properties. This compatibility can be improved by the addition of a compatibilizer. In this study, the PP is first functionalised by Maleic Anhydride (MAH) in the presence of an optimized amount of Dicumyl peroxide (DCP). The reaction was carried out in the molten state using an internal mixer. Then, once the compatibilizer (PP-g-MAH) was prepared, it was added at a various concentration (2.5 - 10 wt.%) to 30/70 Glass Fiber Reinforced Nylon 6 (GFRN6)/PP and their mechanical properties are evaluated. It has been found that the incorporation of the compatibilizer enhances the tensile properties (tensile strength and the modulus) as well as the izod impact properties of the notched samples. This was attributed to better interfacial adhesion as evidenced by SEM. The optimum in these properties is reached at a critical PP-g-MAH concentration (5 wt.%).

L'hydrodésazotation des distillats issus de la conversion des hydrocarbures lourds

Abstract: L'hydrodesazotation catalytique (HDN) est un des points cles de la valorisation des hydrocarbures lourds par leur conversion en carburants. Les coupes lourdes sont riches en azote, et leur conversion produit des distillats eux-memes tres riches en azote, qui ne pourront etre absorbes par une raffinerie classique sans un severe pretraitement desazotant. Ce probleme se pose quel que soit le procede de conversion mis en oeuvre. On propose donc une analyse de l'HDN comprenant la description des structures azotees rencontrees dans les coupes a traiter, la mise en evidence des reactions a promouvoir, et les regles de choix de catalyseurs et de conditions operatoires en decoulant compte tenu des principales determinantes thermodynamiques et cinetiques. Cette analyse s'appuie sur des resultats de recherche tres recents. Elle conduit a la mise en evidence de solutions actuelles au probleme pose, et ouvre des perspectives pour des ameliorations futures.

A novel solution for simulating air overpressure resulting from blasting using an efficient cascaded forward neural network

Abstract: Air overpressure (AOp) is a hazardous effect induced by the blasting method in surface mines. Therefore, it needs to be predicted to reduce the potential risk of damage. The aim of this study is to offer an efficient method to predict AOp using a cascaded forward neural network (CFNN) trained by Levenberg–Marquardt (LM) algorithm, called the CFNN-LM model. Additionally, a generalized regression neural network (GRNN) and extreme learning machine (ELM) were employed to demonstrate the accuracy level of the proposed CFNN-LM model. To conduct the CFNN-LM, GRNN, and ELM models, an extensive database, related to four quarry sites in Malaysia, was used including 62 sets of dependent and independent parameters. Next, the performances of the aforementioned models were checked and discussed through statistical criteria and efficient graphical tools. Finally, the results showed the superiority of CFNN-LM (R2 = 0.9263 and RMSE = 3.0444) over GRNN (R2 = 0.7787 and RMSE = 5.1211) and ELM (R2 = 0.6984 and RMSE = 6.2537) models in terms of prediction accuracy. Furthermore, three different regression analysis metrics were used to perform the sensitivity analysis, and according to the obtained results, the maximum charge per delay ( $$\beta$$ = 0.475, SE = 0.115, t-test = 4.125) was considered as the most influential feature in modeling the AOp.