Publications Scientifiques
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Item Design of an Advanced Optimal Fuzzy Controller For a Binary Distillation Column(Université M'hamed Bougara de Boumerdès, 2024) Bendib, Riad; Mechhoud, El-arkam; Bentarzi, Hamid; Batout, NaoualThe most common control philosophy followed in the chemical process industries is the SISO system using the conventional PID controller algorithms. One drawback is relying on models for both control and design work in Chemical process industries (CPI) is that many problems are very complex and accurate models are difficult, if not impossible to obtain. To overcome these problems, it will be helpful to apply techniques that use human judgment and experience rather than precise mathematical models, which in the major cases deduced from the linearization of the system and simplification hypothesis. The fuzzy logic systems are capable of handling complex, nonlinear systems using simple solutions. However, obtaining an optimal set of fuzzy membership functions is not an easy task. In this chapter a solution based on artificial intelligence is proposed to improve the control of a binary distillation column. The solution is based on the use of the advantages of both fuzzy logic and genetic algorithms. The fuzzy logic is used as a supervisory PI controller that is a simple PI controller that generally used in controlling distillation columns with parameters deduced from the fuzzy supervisor. The membership functions shape is deduced by using research algorithms based on hierarchical genetic algorithms. The results show that the Fuzzy supervisory PI controller provide an excellent tracking toward set point changeItem Application of supervised machine learning methods on the multidimensional knapsack problem(Springer, 2021) Rezoug, Abdellah; Bader-el-den, Mohamed; Boughaci, DalilaMachine Learning (ML) has gained much importance in recent years as many of its effective applications are involved in different fields, healthcare, banking, trading, gaming, etc. Similarly, Combinatorial Optimisation (CO) keeps challenging researchers by new problems with more complex constraints. Merging both fields opens new horizons for development in many areas. This study investigates how effective is to solve CO problems by ML methods. The work considers the Multidimensional Knapsack Problem (MKP) as a study case, which is an np-hard CO problem well-known for its multiple applications. The proposed approach suggests to use solutions of small-size MKP to build models with different ML methods; then, to apply the obtained models on large-size MKP to predict their solutions. The features consist of scores calculated based on information about items while the labels consist of decision variables of optimal solutions calculated from applying CPLEX Solver on small-size MKP. Supervised ML methods build models that help to predict structures of large-size MKP solutions and build them accordingly. A comparison of five ML methods is conducted on standard data set. The experiments showed that the tested methods were able to reach encouraging results. In addition, the study proposes a Genetic Algorithm (GA) that exploits ML outputs essentially in initialisation operator and to repair unfeasible solutions. The algorithm denoted GaPR explores the ML solution neighbourhood as a way of intensification to approach optimal solutions. The carried out experiments indicated that the approach was effective and competitiveItem An enhanced evolutionary approach for solving the community detection problem(Taylor and Francis Online, 2021) Cheikh, Salmi; Bouchema, Sara; Zaoui, SaraCommunity detection concepts can be encountered in many disciplines such as sociology, biology, and computer science, etc. Nowadays, a huge amount of data is produced by digital social networks and needs to be processed. In fact, the analysis of this data makes it possible to extract new knowledge about groups of individuals, their communication modes, and orientations. This knowledge can be exploited in marketing, security, Web usage, and many other decisional purposes. Community detection problem (CDP) is NP-hard and many algorithms have been designed to solve it but not to a satisfactory level. In this paper, we propose a hybrid heuristic approach based on a combination of genetic algorithms and tabu search that does not need any prior knowledge about the number or the size of each community to tackle the CDP. The method is efficient because it uses an enhanced encoding, which excludes redundant chromosomes while performing genetic operations. This approach is evaluated on a wide range of real-world networks. The result of experiments shows that the proposed algorithm outperforms many other algorithms according to the modularity (Q) measure.Item Solving the unsupervised graph partitioning problem with genetic algorithms: Classical and new encoding representations(Elsevier, 2019) Chaouche, Ali; Boulif, MenouarThe Graph Partitioning Problem (GPP) is one of the most ubiquitous models that operations research practitioners encounter. Therefore, several methods have been proposed to solve it. Among these methods, Genetic Algorithm (GA) appears to carry very promising performances. However, despite the huge number of papers being published with this approach, only few of them deal with the encoding representation and its role in the reported performances. In this paper, we present classical and new encoding representations for the unsupervised graph partitioning problem. That is, we suppose that the number of partition subsets (clusters) is not known apriori. Next, we conduct an empiric comparison to identify the most promising encodings.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 Adaptive surrogate modeling with evolutionary algorithm for well placement optimization in fractured reservoirs(Elsevier, 2019) Redouane, Kheireddine; Zeraibi, Noureddine; Nait Amar, MenadWell placement optimization is a decisive task for the reliable design of field development plans. The use of optimization routines coupled to reservoir simulation models as an automatic tool is a popular practice, which could improve the decision-making process on well placement problems. However, despite the various automatic techniques developed, there is still a lack of robust computer-added optimization tool, which can solve the well placement problem with high accuracy in reasonable time while handling the technical constraints properly. In this paper, a hybrid intelligent system is proposed to deal with a real well placement problem with arbitrary well trajectories, complex model grids, and linear and nonlinear constraints. In this intelligent approach, a Genetic Algorithm (GA) combined with a hybrid constraint-handling strategy is applied in conjunction with a constrained space-filling sampling design, Gaussian Process (GP) surrogate model, and one proposed adaptive sampling routine. This self-adaptive framework allows to consecutively augment the quality of surrogate, enhance the accuracy of the process, and thus guide the optimization rapidly into the optimal solution. To demonstrate the efficiency of the developed method, a full-field reservoir case is considered. This case covers a real well placement project in a fractured unconventional reservoir of El Gassi, which is a mature field located in Hassi-Massoud, Algeria. The obtained results highlighted the effectiveness of the proposed approach for solving the real well placement problem with high accuracy in reasonable CPU-time. These auspicious features make it a reliable tool to be used on other real optimization projectsItem Rheological studies and optimization of Herschel–Bulkley parameters of an environmentally friendly drilling fluid using genetic algorithm(Springer, 2018) Ouaer, Hocine; Gareche, Mourad; Rooki, RezaThe Herschel–Bulkley rheological parameters of an environmentally friendly drilling fluid formulated based on an Algerianbentonite and two polymers—hydroxyethyl cellulose and polyethylene glycol—have been optimized using a genetic algorithm.The effect of hydroxyethyl cellulose, temperature, pH and sodium chloride (NaCl) on the three-parameter Herschel-Bulkleymodel was also studied. The genetic algorithm technique provided improved rheological parameter characterization compared tothe nonlinear regression, especially in the case of drilling fluids formulated with sodium chloride making it a better choice.Furthermore, the oscillatory test offered more reliable yield stress values. The rheological parameters were found to be verysensitive to different conditions. Yield stress and consistency index increased with increasing the hydroxyethyl cellulose con-centration, reaching maximum at a temperature of 65 °C and decreased with decreasing pH and also when adding sodiumchloride to the drilling fluid. The flow index changed inversely to yield stress and consistency index. The physical origins of thesechanges in rheological parameters were discussed and correlation between variation in rheological parameters and bentonitesuspension properties were concluded. Based on these results, it is recommended to use the proposed formulation of drilling fluidat high temperature and when the formation of alkaline pH is encountered due to the gelation mechanism and to select theoptimum concentration of NaCl to avoid degradation of the rheological parametersItem Optimization of WAG process using dynamic proxy, genetic algorithm and ant colony optimization(Springer, 2018) Nait Amar, Menad; Zeraibi, Noureddine; Redouane, KheireddineItem Obsolescence optimization of electronic and mechatronic components by considering dependability and energy consumption(Springer, 2013) Mellal, Mohamed Arezki; Adjerid, Smail; Benazzouz, Djamel; Berrazouane, Sofiane; Williams, EdwardItem Guided genetic algorithm for the multidimensional knapsack problem(Springer, 2017) Rezoug, Abdellah; Bader-El-Den, Mohamed; Boughaci, Dalila