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.

A hybrid approach to faults detection and diagnosis in batch and semi-batch reactors by using EKF and neural network classifier

Abstract: This work deals with a new hybrid approach for the detection and diagnosis of faults in different parts of fed-batch and batch reactors. In this paper, the fault detection method is based on the using of Extended Kalman Filter (EKF) and statistical test. The EKF is used to estimate on-line in added to the state of reactor the overall heat transfer coefficient (U). The diagnosis method is based on a probabilistic neural network classifier. The Inputs of the probabilistic classifier are the input–output measurements of reactor and the parameter U estimated by EKF, while the outputs of the classifier are fault types in reactor. This new approach is illustrated for simulated as well as experimental data sets using two cases of reactions: the first is the oxidation of sodium thiosulfate by hydrogen peroxide and the second is alkaline hydrolyse of ethyl benzoate in homogeneous hydro-alcoholic. Finally, the combination of the estimated parameter U using EKF and probabilistic neural network classifier provided the best results. These results show the performance of the proposed approach to monitoring the semi-batch and batch reactors.

Modeling interfacial tension of methane-brine systems at high pressure and high salinity conditions

Abstract: Natural gas which consists mainly of methane (usually more than 90% in volume), is becoming increasingly an important and efficient source of energy because of the lower greenhouse gas emissions and air pollution. Achieving satisfactory recovery factors in gas reservoirs is sensitive to the methane-brine/water interfaces induced by the interfacial tension (IFT) between these systems. Accordingly, accurate determination of IFT of the systems methane-brine/water is extremely important for natural gas production. In this paper, several intelligent models were implemented to accurately estimate interfacial tension (IFT) of the systems brine/pure water-methane under wide temperature, pressure and salinity ranges of (278.1–477.59 K), (0.01–260 MPa) and (0–200,000 ppm), respectively. The established models were based on an extensive databank including 879 experimental measurements. The implemented intelligent models in this study were Extreme Learning Machine (ELM), Radial Basis Function (RBF) neural network, Multilayer Perceptron (MLP), Least Square Vector Machine (LSSVM), and Generalized Regression Neural Network (GRNN). Various optimization algorithms were applied for improving the learning phase of these models. Furthermore, a Committee Machine Intelligent System (CMIS) scheme was proposed by linking the best-found paradigm under a linear single model. The results showed that all the developed intelligent-based paradigms exhibit reliable prediction abilities. In addition, it was found that CMIS and GRNN are the fittest paradigms with overall absolute average percent relative error (AAPRE) values of 1.117% and 1.003%, respectively. Besides, the performance assessment revealed that our best paradigms outperform the existing approaches. Finally, the sensitivity analysis revealed that salinity has a slight impact on IFT.