Publications Internationales
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Item Optimum sizing of hybrid sustainable and renewable energy systems using a modified harris hawks optimizer(Elsevier, 2025) Sifou, Djamel Eddine; Kheldoun, Aissa; Chaib, Ahmed; Belmadani, Hamza; Alharbi, Hisham; Alharbi, Saleh S.; Agajie, Takele Ferede; Ghoneim, Sherif S.M.To boost the use of renewable energy sources while maintaining reliability and affordability, Multi-source renewable and sustainable energy systems must be optimally sized. This research introduces a stand-alone metaheuristic algorithm for designing a hybrid sustainable and renewable energy system combining Wind turbine, PV and battery system. The main goal is to lower the overall present-day system's cost at the same time considering the indicator of reliability, which is the loss of power supply probability (LPSP), as a constraint. The developed algorithm resulted from enhancing the recent Harris Hawks Optimizer (HHO). The modified version incorporates a vector that saves the best three solutions and opposition learning to enhance the population diversity and assist the algorithm in jumping out of local optima regions. Three scenarios are presented, the first is modeled by PV/Bat the second one is modeled by WT/Bat while the third one consists of PV/WT/Bat. The studied project is located in Sidi Khattab, Relizane province, Algeria. The results demonstrate that the MHHO outperforms a range of well-known algorithms, among which one can cite the original HHO, Krill Optimization Algorithm (KOA), Red Squirrel Algorithm (RSA), Modified Coati Optimization Algorithm (MCOA), and Generalized Oppositional-based Social Spider Algorithm (GOOSE). Compared to the other algorithms, MHHO demonstrated superior performance in all proposed configuration settingsItem Geothermal Energy in Algeria and the Contribution of Geophysics(MDPI, 2023) Aliouane, Leila; Ouadfeul, Sid-AliGeothermal energy is one of the cleanest, most accessible and cheapest alternative energies in the whole world. It is a renewable energy designating an inexhaustible source at a human scale that can be renewed (energy culture). Geothermal energy comes from the disintegration of radioactive elements present in rocks and the Earth’s core. These generate heat flow to the surface. This heat increases with depth on average by 30 ◦C/km [1]. In Algeria, this gradient varies from 25 ◦C/km in the north to 60 ◦C/km in the south [2]. In 2006, Madlnés published a world map showing the geothermal potential on all continental plates. North Africa has geothermal potential, which explains why many geothermal studies have been carried out in the north of Algeria (Figure 1). Figure 2 shows the geothermal areas in Algeria where the reservoir rocks are the Jurassic limestone in the north and Albian sandstone in the south. This renewable energy is used in multiple areas: fish farming, greenhouse heating or district heat networks, balneotherapy, and electricity production. Currently, only a tiny fraction of the world’s geothermal resources are used. Certain technological improvements and a better recognition of the true value of geothermal energy could lead to a strong development of this clean and reliable energy for the majority of the countries of the world. Algeria, which has about 200 thermal springs, has the possibility of being among the leaders in this field. In this presentation, we cite the characteristics of geothermal energy, the Algerian thermal springs and the possibilities of their uses according to the temperatures using the Lindal diagram, as well as the role of geophysics or the Earth’s physics in the exploration of geothermal sources before drilling where most of the techniques are the same as those used in petroleum exploration and reservoir characterization exploiting new technological development such as artificial intelligence from seismic and well-logs data [3].Item Characterization of cardinal vine shoot waste as new resource of lignocellulosic biomass and valorization into value-added chemical using Plackett–Burman and Box Behnken(Springer, 2021) Didaoui, Amine; Amrane, Abdeltif; Aksil, Tounsia; Boubieb, NaimaThe objective of this work was to valorize a waste from cardinal vine shoot into a hydrolysate rich in reducing sugars. Plack- ett–Burman design was considered to identify the significant factors, while a Box Behnken design was considered to optimize the extraction in the following experimental conditions: 100 °C, 750 rpm, trifluoracetic acid (CF 3 O 2 H) concentration (TFA) in the range (1–10%), for 20 to 180 min and considering the following solid–liquid (S/V) ratios (1:1, 3:1, 5:1). The optimal result was 2.53% in sugars equivalent to a yield of 50.64% per gram of dry matter. Shoot vine waste was characterized by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), X-ray diffraction (XRD), simultaneous thermal analysis (STA), and X-ray fluorescence (XRF). The chemical composition was 43.38% cellulose, 23.58% hemicel- lulose, 21.22% lignin, 2.53% ash, 5.82% crude protein, 11.7% moisture, and extractives (0.81% fat, 0.56% total sugars, 2.3% extractive (hexane-ethanol)). The promising potential of shoot vine waste to produce sugar and other added-value compounds was demonstrated.Item Solar air heater with underground latent heat storage system for greenhouse heating: Performance analysis and machine learning prediction(Elsevier, 2023) Badji, Ahmed; Benseddik, Abdellouahab; Akkila, Boukhelifa; Bensaha, Hocine; Erregani, Reggani Moulay; Bendriss, Abdelbasset; Bouhoun, Salah; Nettari, Chihabeddine; Kouane, Mohamed; Lalmi, DjemouiThe increasing demand for renewable energy sources in greenhouse heating, driven by the high cost of fossil fuels, has prompted the exploration of various alternatives, such as solar collectors, heat pumps, biomass, and cogeneration systems. This study aimed to establish an optimal environment for plant growth by employing a unique solar air heater and an underground latent heat storage system with a packed bed of phase change material unit (CaCl2-6H2O). Conducted in a double-span greenhouse in Ghardaia, Algeria, characterized by a semi-arid climate, the research utilized two distinct machine learning algorithms to predict the heating system's thermal behavior accurately. An experimental assessment of climatic parameters revealed that the greenhouse equipped with the heating system maintained an air temperature 57 % higher than that of a conventional greenhouse during the nighttime. The use of phase change materials resulted in the release of only 20 kJ of energy at night, indicating the potential to meet 30 % of the greenhouse's energy requirements during nighttime. Utilizing artificial neural networks, this study accurately predicted internal greenhouse parameters with and without LTES. The Nonlinear Autoregressive Exogenous (NARX) model exhibited high accuracy in prediction, with an R2 value of 0.9986 in both cases, while the Recurrent Neural Network (RNN) model showed acceptable performance, achieving an R2 value of 0.9893. These results underscore the potential of ANN models in advancing thermal energy storage technologies and their applicability in sustainable agriculture. This research significantly contributes to thermal energy storage systems and their benefits for sustainable agriculture.Item Correction to: characterization of cardinal vine shoot waste as new resource of lignocellulosic biomass and valorization into value-added chemical using Plackett–Burman and Box Behnken(Springer Nature, 2023) Didaoui, Amine; Amrane, Abdeltif; Aksil, Tounsia; Boubieb, NaimaThe objective of this work was to valorize a waste from cardinal vine shoot into a hydrolysate rich in reducing sugars. Plackett–Burman design was considered to identify the significant factors, while a Box Behnken design was considered to optimize the extraction in the following experimental conditions: 100 °C, 750 rpm, trifluoracetic acid (CF3O2H) concentration (TFA) in the range (1–10%), for 20 to 180 min and considering the following solid–liquid (S/V) ratios (1:1, 3:1, 5:1). The optimal result was 2.53% in sugars equivalent to a yield of 50.64% per gram of dry matter. Shoot vine waste was characterized by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), X-ray diffraction (XRD), simultaneous thermal analysis (STA), and X-ray fluorescence (XRF). The chemical composition was 43.38% cellulose, 23.58% hemicellulose, 21.22% lignin, 2.53% ash, 5.82% crude protein, 11.7% moisture, and extractives (0.81% fat, 0.56% total sugars, 2.3% extractive (hexane-ethanol)). The promising potential of shoot vine waste to produce sugar and other added-value compounds was demonstrated.Item APF Applied on PV Conversion Chain Network Using FLC †(MDPI, 2023) Bourourou, Fares; Tadjer, Sid Ahmed; Habi, IdirThis paper focuses on regulation of the parallel active power filter (APF) Dc Voltage bus by judicious choice of rule bases and intervals for each selected fuzzy variable of suitable fuzzy logic controller. In addition, an algorithm describes the main steps for designing an FLC that has any number of rules with direct application to the APF capacitor voltage regulation. Where their simu- lation, by MATLAB, applied to PV conversion chain network will be represented in the booths cases, constant and variable non-linear loads after modeling, to show the effectiveness of this kind of reg- ulators on electrical power quality and improve the reliability of the APF on PV system. The deliv- ered voltage of PV plant has been regulated and controlled with MPPT using P&O technique and FLC regulator after modeling of each part of the conversion chain. PV plant supplies a nonlinear load from the rectifier installed on the output of the conversion chain via a controlled power inverter. A 3 × 3 rules fuzzy regulator is implanted in the control part of the APF to examine the influence of the FLC on the produced electrical power quality. Simulation results are represented and analyzeItem A novel method to forecast 24 h of global solar irradiation(Springer, 2017) Saoud, L. Saad; Rahmoune, F.; Tourtchine, V.; Baddari, KamelItem Cognitive Quaternion Valued Neural Network and some applications(Elsevier, 2016) Saad Saoud, Lyes; Ghorbanib, Reza; Rahmounea, FayçalA Meta-cognitive Quaternion Valued Neural Network (Mc-QVNN) learning algorithm and its forecasting applications is proposed in this paper. The Mc-QVNN has two parts, the cognitive part that contains the QVNN and a meta-cognitive part, which self-regulates the learning algorithm. At each epoch, when the Mc-QVNN receives a new sample, the meta-cognitive part makes a decision about the manner, the time and the need to learn this sample or not. In this case, the algorithm deletes the unneeded samples and keeps just the necessary ones for learning. The meta-cognitive component makes the decision according to the quaternion magnitude and phases. Three forecasting problems, which are Mackey–Glass time series, Lorenz attractor and the real home's power in the city of Honolulu in Hawaii, USA, are taken to test the performance of the proposed algorithm. Comparison with other existing methods shows that the Mc-QVNN is promising for forecasting chaotic systems