Publications Scientifiques
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Item Prediction of Flash Points of Petroleum Middle Distillates Using an Artificial Neural Network Model(Pleiades Publishing, 2024) Bedda, KahinaAn artificial neural network (ANN) model of a multilayer perceptron-type was developed to predict flash points of petroleum middle distillates. The ANN model was designed using 252 experimental data points taken from the literature. The properties of the distillates, namely, specific gravity and distillation temperatures, were the input parameters of the model. The training of the network was carried out using the Levenberg– Marquardt backpropagation algorithm and the early stopping technique. A comparison of the statistical parameters of different networks made it possible to determine the optimal number of neurons in the hidden layer with the best weight and bias values. The network containing nine hidden neurons was selected as the best predictive model. The ANN model as well as the Alqaheem–Riazi’s model was evaluated for the prediction of flash points by a statistical analysis based on the calculation of the mean square error, Pearson correlation coefficient, coefficient of determination, absolute percentage errors, and the mean absolute percentage error. The ANN model provided higher prediction accuracy over a wide distillation range than the Alqaheem–Riazi’s model. The developed ANN model is a reliable and fast tool for the low-cost estimation of flash points of petroleum middle distillates.Item Prediction of smoke points of kerosene distillates using simple laboratory tests: artificial neural network versus conventional correlations(Pleiades Publishing, 2023) Bedda, KahinaIn the present study, an artificial neural network (ANN) model and three well-known correlations were used to predict the smoke points of 430 kerosene distillates from their specific gravities and distillation temperatures. The ANN model was developed in MATLAB software, it is a feedforward multilayer perceptron with a single hidden layer. The optimal number of neurons in the hidden layer as well as the best training algorithm and the best values of connection weights and biases were determined by trial and error using the nftool command. The early stopping technique by cross-validation was employed to avoid overfitting of the model. The developed model composed of 17 sigmoid hidden neurons and one linear output neuron was trained with the Levenberg-Marquardt backpropagation algorithm. This model allowed the prediction of smoke points with a coefficient of determination of 0.852, an average absolute deviation of 1.4 mm and an average absolute relative deviation of 6%. Statistical analysis of the results indicated that the prediction accuracy of the ANN model is higher than that of the conventional correlations. Indeed, in addition to its effectiveness, the proposed ANN method for the estimation of smoke points has the advantages of low-cost and easy implementation, as it relies on simple laboratory tests. Thus, the developed ANN model is a reliable tool that can be used in petroleum refineries for fast quality control of kerosene distillates.Item Sensitivity analysis of the gtn damage parameters at different temperature for dynamic fracture propagation in x70 pipeline steel using neural network(Gruppo Italiano Frattura, 2021) Abdelmoumin Ouladbrahim, Abdelmoumin; Belaidi, Idir; Khatir, Samir; Magagnini, Erica; Capozucca, Roberto; Wahab, Magd AbdelIn this paper, the initial and maximum load was studied using the Finite Element Modeling (FEM) analysis during impact testing (CVN) of pipeline X70 steel. The Gurson-Tvergaard-Needleman (GTN) constitutive model has been used to simulate the growth of voids during deformation of pipeline steel at different temperatures. FEM simulations results used to study the sensitivity of the initial and maximum load with GTN parameters values proposed and the variation of temperatures. Finally, the applied artificial neural network (ANN) is used to predict the initial and maximum load for a given set of damage parameters X70 steel at different temperatures, based on the results obtained, the neural network is able to provide a satisfactory approximation of the load initiation and load maximum in impact testing of X70 SteelItem Artificial Neuron Network Based Faults Detection and Localization in the High Voltage Transmission Lines with Mho Distance Relay(IETA, 2020) Boumedine, Mohamed Said; Khodja, Djalal Eddine; Chakroune, SalimThis study offers the opportunity to extend the functioning of the most advanced protection systems. The faults which can arise on the power transmission lines are numerous and varied: Short-circuit; Overvoltage; Overloads, etc. In the context of short circuits, the conventional sensor as the Mho distance relay also known as the admittance relay is generally used. This relay will be discussed later in this study. By taking into account the preventive risks of the Mho relay and discover the new techniques of artificial intelligence, namely the neural network which can contribute to the precise and rapid detection of all types of short-circuit faults. The results of the simulation tests demonstrate the effectiveness of the methods proposed for the automatic diagnosis of faults.Item Keratoconus prognosis study for patients with corneal external mechanical stress mode(Springer Nature, 2020) Bettahar, Toufik; Rahmoune, Chemseddine; Benazzouz, DjamelPurpose To demonstrate the correlation between excessive eye rubbing and corneal degeneration for Keratoconus patients. Materials and methods Keratoconus (KC) patients who regularly rub their eyes had shown a rapid degeneration rate of their affected corneas. This observation is experimentally and numerical discussed and developed based on clinical data of 8 of KC Patients with a mean age of 26.5 ± 9.4 years old, and four healthy individuals with a mean age of 24.33 ± 5 years old at the baseline. Corneal topography was used to measure both central corneal thickness (CCT) and its total refractive power. The registered data had been exploited to assess the progression of the disease, and the final results were embedded in a finite element model of human corneas to simulate their response to eye rubbing at different stages of the pathology. Corneal lifetime prognosis using multi-layer perceptron was then established to estimate the number of eye rubbing cycles for each stage of KC. Results The survey of KC patients who declared stopping eye rubbing had shown a decrease in CCT loss rate, followed by a durable stability. Mechanical stresses numerical simulations had shown different corneal behaviours in term of shape deformity, apical raise and corneal applanation between healthy and KC stages models. Apical rise ranged from 0.122 to 0.389 mm for an applied intraocular pressure that equals to 15 mmHg. A normal stress of 5 kPa provoked a corneal applanation that ranged from 0.27 mm in healthy cases to 1.173 mm in severe stages of the disease. The application of 2.5 kPa biaxial stress had resulted normal and tangential applanations that successively ranged from 0.152 and 0.173 mm in healthy corneas to 0.446 mm and 0.458 mm in severe KC stages. An adopted prognosis algorithm was able to predict the current stage of the disease and to estimate the remaining number of eye rubbing cycles before failure. Conclusion Eye rubbing was proven to be a considerable contributing factor in KC patient’s corneal degeneration. The progression of this pathology could be decreased or halted by stopping eye rubbing at early stages.Item Application of artificial neural network and kinetic modeling for the prediction of biogas and methane production in anaerobic digestion of several organic wastes(Taylor & Francis, 2021) Mougari, Nour El Islam; Largeau, J. F.; Himrane, N.; Hachemi, M.; Tazerout, M.In the current study, artificial neural network (ANN) and modified Gompertz equation (MG) were applied to develop integrated based models for the prediction of cumulative biogas and methane yield (CBY and CMY) from anaerobic digestion (AD) of several organic wastes. Volatile solid to total solid ratio (VS/TS), carbon content (C), carbon-to-nitrogen ratio (C/N) and digestion time (DT) were selected as input features for the implementation of ANN approach. Genetic algorithm (GA) was employed in order to optimize the ANN architecture as well as the kinetic parameters of the MG to provide reliable and fast learning for better prediction performance. To evaluate model performances, determination coefficient (R2) and root mean square error (RMSE) were used. Both the approaches performed well in predicting CBY and CMY and showed a good agreement with the experimental data. However, GA-ANN models exhibit smaller deviation and higher predictive accuracy with satisfactory RMSE and R2 of about 0.0045 and 0.9996 for CBY, and 0.0046 and 0.9998 for CMY, compared with GA-MG models. This evinces the effectiveness of the developed approach to forecast CBY and CMY and can be an effective tool for the scale up of anaerobic digestion units and technico-economic studiesItem Predictionofnaturalgashydratesformationusingacombinationofthermodynamicandneuralnetworkmodeling(Elsevier, 2019) Rebai, Noura; Hadjadj, Ahmed; Benmounah, Abdelbaki; Abdallah, S.Berrouk; M.Boualleg, SalimDuring the treatment or transport of natural gas, the presence of water, even in very small quantities, can trigger hydrates formation that causes plugging of gas lines and cryogenic exchangers and even irreversible damages to expansion valves, turbo expanders and other key equipment. Hence, the need for a timely control and monitoring of gas hydrate formation conditions is crucial. This work presents a two-legged approach that combines thermodynamics and artificial neural network modeling to enhance the accuracy with which hydrates formation conditions are predicted particularly for gas mixture systems. For the latter, Van der Waals-Platteeuw thermodynamic model proves very inaccurate. To improve the accuracy of its predictions, an additional corrective term has been approximated using a trained network of artificial neurons. The validation of this approach using a database of 4660 data points shows a significant decrease in the overall relative error on the pressure from around 23.75%–3.15%. The approach can be extended for more complicated systems and for the prediction of other thermodynamics properties related to the formation of hydratesItem Artificial neuron network based faults detection and localization in the high voltage transmission lines with MHO distance relay(International Information and Engineering Technology Association, 2020) B.M, Said; K.D., Eddine; C., SalimThis study offers the opportunity to extend the functioning of the most advanced protection systems. The faults which can arise on the power transmission lines are numerous and varied: Short-circuit; Overvoltage; Overloads, etc. In the context of short circuits, the conventional sensor as the Mho distance relay also known as the admittance relay is generally used. This relay will be discussed later in this study. By taking into account the preventive risks of the Mho relay and discover the new techniques of artificial intelligence, namely the neural network which can contribute to the precise and rapid detection of all types of short-circuit faults. The results of the simulation tests demonstrate the effectiveness of the methods proposed for the automatic diagnosis of faultsItem New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network(SAGE Publications Inc., 2020) Afia, Adel; Rahmoune, C.; Benazzouz, D.; Merainani, B.; Fedala, S.Nowadays, fault detection, identification, and classification seem to be the most difficult challenge for gear systems. It is a complex procedure because the defects affecting gears have the same frequency signature. Thus, the variation in load and speed of the rotating machine will, inevitably, lead to erroneous detection results. Moreover, it is important to discern the nature of the anomaly because each gear defect has several consequences on the mechanism’s performance. In this article, a new intelligent fault diagnosis approach consisting of Autogram combined with radial basis function neural network is proposed. Autogram is a new sophisticated enhancement of the conventional Kurtogram, while radial basis function is used for classification purposes of the gear state. According to this approach, the data signal is decomposed by maximal overlap discrete wavelet packet transform into frequency bands and central frequencies called nodes. Thereafter, the unbiased autocorrelation of the squared envelope for each node is computed in order to calculate the kurtosis for each one at every decomposition level. Finally, the feature matrix obtained from the previous step will be the input of the radial basis function neural network to provide a new automatic gear fault diagnosis technique. Experimental results from the gearbox with healthy state and five different types of gear defects under variable speeds and loads indicate that the proposed method can successfully detect, identify, and classify the gear faults in all casesItem Neural network ARX model for gas conditioning tower(Taylor and Francis Online, 2019) Haddouche, Rezki; Boukhemis, Chetate; Mohand Said, BoumedineThis work focuses on the identification of the gas conditioning tower (GCT) operating in a cement plant. It is an important element in the cement production line. Mathematical modeling of such a process proves to be very complex. This is due to the phenomena that occur during the operation of the system. An artificial neural network-based auto-regressive with exogenous inputs (NNARX) model is constructed with the aim to study the system as well as used to control the process. Resulted models are tested and validated using data extracted on a GCT operating at Chlef cement plant in Algeria.