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var str = 'Structural Characterization of ZnO Thin Films Deposited onto Silicon Substrates using Cathodic Magnetron Sputtering';
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]]>
Zinc oxide ZnO]]>
Thin films]]>
Magnetron sputtering]]>
X-ray diffraction (XRD)]]>
Scanning electron microscopy (SEM)]]>
In this study, we analyzed Zinc Oxide (ZnO) thin films deposited on silicon substrates using magnetron sputtering. These films have numerous applications in photovoltaic and optoelectronic devices due to their excellent physique properties. We used two structural characterization techniques: X-ray diffraction and scanning electron microscopy. We found that the ZnO films had good crystallinity and a columnar structure on the substrate surface, as indicated by the (002) orientation. These findings could potentially be useful for the development of ZnO-based devices such as solar cells and piezoelectric sensors. our results are consistent with those found by other researchers.]]>
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var str = 'Ghalmi, Leila';
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]]>
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var str = 'Bensmaine, Souhila';
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]]>
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var str = 'Merzouk, Chems El Hayat';
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]]>
Journal of Renewable Energies; Vol. 26 No. 1 (2023): Volume 26 (Regular); 41 – 56]]>
Journal of Renewable Energies; Vol. 26 No 1 (2023): Volume 26 (Regular); 41 – 56]]>
2716-8247]]>
1112-2242]]>
10.54966/jreen.v26i1]]>
Renewable Energy Development Center]]>
2023-10-04]]>
Copyright (c) 2023 Journal of Renewable Energies]]>
https://creativecommons.org/licenses/by-sa/4.0]]>
https://revue.cder.dz/index.php/rer/article/view/1116/1033]]>
application/pdf]]>
eng]]>
info:eu-repo/semantics/article]]>
info:eu-repo/semantics/publishedVersion]]>
https://revue.cder.dz/index.php/rer/article/view/1116]]>
10.54966/jreen.v26i1.1116]]>
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var str = 'Clear Sky Global Surface Solar Irradiance Estimation from Bird & Hulstrom Radiometric Model/MODIS Atmospheric Data Combination';
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]]>
Global Surface Solar irradiance]]>
MODIS]]>
Bird & Hulstrom Radiometric model]]>
Transmittance]]>
Atmospheric Parameters]]>
Algeria]]>
Estimation of solar irradiance received on the ground is essential in many solar energy applications, particularly those relying on concentrating solar technologies. In this work, we propose a model to estimate the spatial distribution of global Surface Solar Irradiance (SSI) from a Bird & Hulstrom radiometric model/MODIS atmospheric data combination. The theoretical model selected is the radiometric model of Bird & Hulstrom, and it has been modified to take into account the effect of the atmosphere by adding a transmittance function that obeys the Beer-Lambert law, implemented with atmospheric parameters (water vapor, ozone, aerosols, etc.) from the Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products. The effectiveness of the proposed methodology is tested on the Algerian territory. The obtained results show that the proposed approach concurs with results provided by the tested Meteosat data-based method while providing SSI maps of better spatial resolution. Moreover, the obtained solar irradiances show a root mean square error of about 140 Wh.m-2 in comparison with the Copernicus Atmosphere Monitoring Service (CAMS) radiation extracted from the Solar Radiation Database for the environment (SoDa) server.]]>
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var str = 'Benharrats, Farah';
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]]>
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var str = 'Mahi, Habib';
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]]>
Journal of Renewable Energies; Vol. 26 No. 1 (2023): Volume 26 (Regular); 31 – 39]]>
Journal of Renewable Energies; Vol. 26 No 1 (2023): Volume 26 (Regular); 31 – 39]]>
2716-8247]]>
1112-2242]]>
10.54966/jreen.v26i1]]>
Renewable Energy Development Center]]>
2023-10-04]]>
Copyright (c) 2023 Journal of Renewable Energies]]>
https://creativecommons.org/licenses/by-sa/4.0]]>
https://revue.cder.dz/index.php/rer/article/view/1115/1032]]>
application/pdf]]>
eng]]>
info:eu-repo/semantics/article]]>
info:eu-repo/semantics/publishedVersion]]>
https://revue.cder.dz/index.php/rer/article/view/1115]]>
10.54966/jreen.v26i1.1115]]>
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var str = 'Physicochemical and Organoleptic Analysis of Tomato Irrigated with fish farms water; at URERMS-ADRAR. Algeria';
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]]>
Irrigation]]>
Fish farm]]>
Nutritional value]]>
Caloric values]]>
Microbiology analysis]]>
The objective of the developed system is to encourage efficient water management practices that optimize the usage of water by keeping crop health and yield intact through the implementation of an automated irrigation system. The study aimed to determine the impact of irrigation by fish farming on growth rates and concentrations of macro- and micronutrients in tomatoes. Results indicated that irrigation with fish farm effluent significantly increased and effluent water, depending on its composition to feed the fish, can supply nutrients and increase the concentrations of these elements in tomatoes irrigated with this water with a refractive index = 1.3391 and a calorie value of 19.927 Calorie in the first sampling and 16.696 calories in the second sampling for 100 g for the year 2021 and for the year 2022 we have a refractive index = 1.3495 and a calorie value of 20.66 Calorie for 100 g. Here we show for the first time that is more than the standard norm (Cotte.2000) which is 18 Calories. Also, after carrying out microbiological analyzes, there is a total absence of pathogenic bacteria, as well as the organoleptic parameters are heading in the right direction where tomatoes irrigated by fish farming water is concentrated compared to the tomatoes irrigated by normal fresh water with Light red of 53 %, Cooked acid of 47 %, spherical form of 94 % and a pleasant smell of 88 %. Therefore, using fish farm effluent in irrigation provides water requirements for plants and also can improve the availability of nutrient elements for tomatoes.]]>
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var str = 'Allali, Malika';
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]]>
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var str = 'Tigani, Cherif';
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]]>
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var str = 'Djaber, Abdelkrim';
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]]>
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var str = 'Mediani, Ahmed';
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]]>
Journal of Renewable Energies; Vol. 26 No. 1 (2023): Volume 26 (Regular); 5 – 29]]>
Journal of Renewable Energies; Vol. 26 No 1 (2023): Volume 26 (Regular); 5 – 29]]>
2716-8247]]>
1112-2242]]>
10.54966/jreen.v26i1]]>
Renewable Energy Development Center]]>
2023-10-04]]>
Copyright (c) 2023 Journal of Renewable Energies]]>
https://creativecommons.org/licenses/by-sa/4.0]]>
https://revue.cder.dz/index.php/rer/article/view/1114/1031]]>
application/pdf]]>
eng]]>
info:eu-repo/semantics/article]]>
info:eu-repo/semantics/publishedVersion]]>
https://revue.cder.dz/index.php/rer/article/view/1114]]>
10.54966/jreen.v26i1.1114]]>
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var str = 'Etude numérique de l’effet des générateurs de vortex longitudinaux sur le transfert thermique d’un écoulement laminaire traversant un micro-canal';
document.write(linkifyHtml(str, options));
]]>
longitudinal vortex generator (LVG)]]>
Rectangular micro-channel]]>
Heat transfer]]>
Forced convection]]>
Laminar flow]]>
Nusselt number]]>
Reynolds number]]>
Friction factor]]>
We conducted, in this work, a three-dimensional numerical study of forced convection heat transfer of a laminar flow of water passing through micro-channels with and without longitudinal vortex generator, using the CFD code “Ansys- Fluent”. This work aimed to elucidate the effect of vortex generators on the dynamic and thermal behavior of the micro-fluidic flow. The results obtained show that the increase in the Reynolds number leads to an improvement in the quality of heat transfer in both cases of the study. Rectangular micro-channel with LVG can improve heat transfer compared to smooth rectangular micro-channel while consuming more pressure drop. In the range of Reynolds numbers between 200 and 1200, a 2-21% increase in the mean Nusselt number was observed for micro-channels with LVG compared to smooth micro-channels. This occurs through better mixing of the fluid, reduction in the thickness of the thermal boundary layer, and increased heat transfer area. In addition, the friction factor has been increased by more than 50% compared to smooth micro-channels, due to the local resistance of LVGs and the presence of secondary flows.]]>
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var str = 'Soudani, Azeddine';
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]]>
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var str = 'Belkacemi, Zoubir';
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]]>
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var str = 'Rahmoune, Imane';
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]]>
Journal of Renewable Energies; Vol. 25 No. 2 (2022): Volume 25 (Regular); 229 – 250]]>
Journal of Renewable Energies; Vol. 25 No 2 (2022): Volume 25 (Regular); 229 – 250]]>
2716-8247]]>
1112-2242]]>
10.54966/jreen.v25i2]]>
Renewable Energy Development Center]]>
2023-10-04]]>
Copyright (c) 2022 Journal of Renewable Energies]]>
https://creativecommons.org/licenses/by-sa/4.0]]>
https://revue.cder.dz/index.php/rer/article/view/1084/1041]]>
application/pdf]]>
eng]]>
info:eu-repo/semantics/article]]>
info:eu-repo/semantics/publishedVersion]]>
https://revue.cder.dz/index.php/rer/article/view/1084]]>
10.54966/jreen.v25i2.1084]]>
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var str = 'Experimental and CFD investigation of cavitation phenomenon in the distributor of a Banki-Michell Turbine';
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]]>
Injector]]>
Ansys workbench]]>
Geometry]]>
Mesh]]>
Simulation]]>
Cavitation is a physical phenomenon that often occurs in hydraulic machines such as pumps, valves, and turbines. Although the Banki-Michell turbine has been used for a long time in small hydropower, no study related to this phenomenon of cavitation in the injector of this turbine has been done. In this study, we will present the results of a numerical study carried out in the nozzle of a Banki-Michell turbine. The numerical solution of the Navier Stokes cavitation equations of the Banki-Michell turbine injector was carried out considering a 2D geometry of the injector-rotor assembly. The simulation results showed that the cavitation phenomenon appears when the water flow area in the nozzle becomes less than 50%. Furthermore, the results also showed that the occurrence of this cavitation phenomenon in the injector is more likely at higher operating heads. The results of an experimental study of the geometry of the injector showed that the height of the water passage section varies linearly with the degree of opening of the stator valve.]]>
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var str = 'Niyonzima, Jean Bosco';
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]]>
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var str = 'Hendrick, Patrick';
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]]>
Journal of Renewable Energies; Vol. 25 No. 2 (2022): Volume 25 (Regular); 211 – 227]]>
Journal of Renewable Energies; Vol. 25 No 2 (2022): Volume 25 (Regular); 211 – 227]]>
2716-8247]]>
1112-2242]]>
10.54966/jreen.v25i2]]>
Renewable Energy Development Center]]>
2023-10-04]]>
Copyright (c) 2022 Journal of Renewable Energies]]>
https://creativecommons.org/licenses/by-sa/4.0]]>
https://revue.cder.dz/index.php/rer/article/view/1083/1040]]>
application/pdf]]>
eng]]>
info:eu-repo/semantics/article]]>
info:eu-repo/semantics/publishedVersion]]>
https://revue.cder.dz/index.php/rer/article/view/1083]]>
10.54966/jreen.v25i2.1083]]>
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var str = 'Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems';
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]]>
Photovoltaic (PV)]]>
Monitoring]]>
Classification]]>
Artificial intelligence]]>
Maximum power]]>
The performance of a photovoltaic system depends on several parameters such as temperature, clouds, and the season, which makes the study of PV performance from monitoring databases very complex given the size of the information and the complexity of the phenomena involved. This article applies an artificial intelligence (AI) method based on machine learning (ML). For more efficient analysis, the Support Vector Machine (SVM) is used to simplify and optimize the processing of these data for the study of the performance of PV systems. More precisely, we group a multi-class data variable according to the needs of the analysis using SVMs. In this article, we present all the stages of data processing based on the application of artificial intelligence (AI). We present as an example the results obtained in the study of the performance of a 150W monocrystalline photovoltaic (PV) module after one year of monitoring.]]>
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var str = 'Hafdaoui, Hichem';
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]]>
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var str = 'Kouadri Boudjelthia, El Amin';
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]]>
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var str = 'Bouchakour, Salim';
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]]>
var options = {
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var str = 'Belhaouas, Nasreddine';
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]]>
Journal of Renewable Energies; Vol. 25 No. 2 (2022): Volume 25 (Regular); 199 – 210]]>
Journal of Renewable Energies; Vol. 25 No 2 (2022): Volume 25 (Regular); 199 – 210]]>
2716-8247]]>
1112-2242]]>
10.54966/jreen.v25i2]]>
Renewable Energy Development Center]]>
2023-10-04]]>
Copyright (c) 2022 Journal of Renewable Energies]]>
https://creativecommons.org/licenses/by-sa/4.0]]>
https://revue.cder.dz/index.php/rer/article/view/1082/1039]]>
application/pdf]]>
eng]]>
info:eu-repo/semantics/article]]>
info:eu-repo/semantics/publishedVersion]]>
https://revue.cder.dz/index.php/rer/article/view/1082]]>
10.54966/jreen.v25i2.1082]]>
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var str = 'Analysis and Design of Modified Incremental Conductance-Based MPPT Algorithm for Photovoltaic System';
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]]>
Photovoltaic system]]>
MPPT]]>
Modified incremental conductance]]>
This study discusses the design of the Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems employing a modified incremental conductance (IncCond) algorithm to extract maximum power from a PV module. A PV module, a DC-DC converter, and a resistive load constitute the PV system. In the scientific literature, it is well-documented that typical MPPT algorithms have significant drawbacks, such as fluctuations around the MPP and poor tracking during a sudden change in atmospheric conditions. To solve the deficiencies of conventional methodology, a novel modified IncCond method is proposed in this study. The simulation results demonstrate that the updated IncCond algorithm presented allows for less oscillation around the maximum power point (MPP), a rapid dynamic response, and superior performance.]]>
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var str = 'Hadjidj, Nadjiha';
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]]>
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var str = 'Benbrahim, Meriem';
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]]>
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var str = 'Ounnas, Djamel';
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]]>
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var str = 'Mouss, Leila Hayet';
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]]>
Journal of Renewable Energies; Vol. 25 No. 2 (2022): Volume 25 (Regular); 187 – 198]]>
Journal of Renewable Energies; Vol. 25 No 2 (2022): Volume 25 (Regular); 187 – 198]]>
2716-8247]]>
1112-2242]]>
10.54966/jreen.v25i2]]>
Renewable Energy Development Center]]>
2023-10-04]]>
Copyright (c) 2022 Journal of Renewable Energies]]>
https://creativecommons.org/licenses/by-sa/4.0]]>
https://revue.cder.dz/index.php/rer/article/view/1081/1038]]>
application/pdf]]>
eng]]>
info:eu-repo/semantics/article]]>
info:eu-repo/semantics/publishedVersion]]>
https://revue.cder.dz/index.php/rer/article/view/1081]]>
10.54966/jreen.v25i2.1081]]>
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var str = 'Wind data modeling and energy mapping of the wind potential in the city of Douala (Cameroun)';
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]]>
Weibull parameters]]>
Wind potential]]>
Energy density]]>
Energy mapping]]>
Turbulence]]>
This work helps in the implementation of wind energy projects in the city of Douala. Wind data used (speed and direction) were collected from January 1, 2020, to December 31, 2020, at a height of 10m. The use of wind speed distribution laws allowed us to obtain predictions of the available wind energy on-site at different altitudes. Then, the wind direction is established for the orientation of the wind turbines and the turbulence analysis is done to highlight the exploitable wind periods. The results show that harnessing wind energy is possible on site from 10 AM to 06 PM. The wind potential at 138 m height has an average speed of 8.15 m/s for an overall energy density of 749.78 W/m2 for roughness class 0, and an average speed of 3.7 m/s for an overall energy density of 69.118 W/m2 for roughness class 4. The wind turbines will be installed in front of the north-north-east direction between 15 and 25°. Finally, the energy mapping of the city's wind resources allows us to estimate the total energy available at a reference height.]]>
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var str = 'Dantse, Yvan Martino';
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]]>
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var str = 'Eke, Samuel';
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]]>
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var str = 'Kouokam, Endouche';
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]]>
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var str = 'Mezoue, Cyrille';
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var str = 'Nsouandele, Jean – Luc';
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var str = 'Mouangue, Ruben';
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]]>
Journal of Renewable Energies; Vol. 25 No. 2 (2022): Volume 25 (Regular); 169 – 186]]>
Journal of Renewable Energies; Vol. 25 No 2 (2022): Volume 25 (Regular); 169 – 186]]>
2716-8247]]>
1112-2242]]>
10.54966/jreen.v25i2]]>
Renewable Energy Development Center]]>
2023-10-04]]>
Copyright (c) 2022 Journal of Renewable Energies]]>
https://creativecommons.org/licenses/by-sa/4.0]]>
https://revue.cder.dz/index.php/rer/article/view/1080/1037]]>
application/pdf]]>
eng]]>
info:eu-repo/semantics/article]]>
info:eu-repo/semantics/publishedVersion]]>
https://revue.cder.dz/index.php/rer/article/view/1080]]>
10.54966/jreen.v25i2.1080]]>
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var str = 'Artificial neural network-based modeling for the prediction of heat and mass transfer coefficient of the adiabatic liquid desiccant system';
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]]>
Artificial neural network]]>
Liquid desiccant]]>
Heat and mass transfer coefficient]]>
Dehumidification]]>
This work, based on the data obtained from the literature reported by (Varela et al., 2018), aims to use the artificial neural network approach to predict the heat and mass transfer in a dehumidifier system, using lithium chloride as a liquid desiccant. A neural network model was developed in MATLAB environment based on multilayer perceptron that included an input, hidden, and output layer. The network input parameters are air velocity, air temperature, air humidity ratio, liquid desiccant temperature, liquid flow rate, and liquid desiccant concentration. The network output includes two variables which are the heat transfer coefficient (Kh) and mass transfer coefficient (Km). The performance of the ANN model was evaluated using the statistical parameters between the prediction results and experimental values. The performance regression yields R2 and MSE values of 0.9344 and 9.0032, respectively, for the test data set of heat transfer coefficient (Kh). Moreover, for the mass transfer coefficient (Km), the regression parameter R2 and MSE values for the ANN tests were found to be 0.9657 and 2.0414, respectively. In addition, air velocity, air temperature, solution mass flow rate, and solution concentration are the most influential parameters on the heat and mass transfer between the air and liquid desiccant.]]>
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var str = 'Bouzeffour, Fatih';
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]]>
Journal of Renewable Energies; Vol. 25 No. 2 (2022): Volume 25 (Regular); 157 – 167]]>
Journal of Renewable Energies; Vol. 25 No 2 (2022): Volume 25 (Regular); 157 – 167]]>
2716-8247]]>
1112-2242]]>
10.54966/jreen.v25i2]]>
Renewable Energy Development Center]]>
2023-10-04]]>
Copyright (c) 2022 Journal of Renewable Energies]]>
https://creativecommons.org/licenses/by-sa/4.0]]>
https://revue.cder.dz/index.php/rer/article/view/1079/1036]]>
application/pdf]]>
eng]]>
info:eu-repo/semantics/article]]>
info:eu-repo/semantics/publishedVersion]]>
https://revue.cder.dz/index.php/rer/article/view/1079]]>
10.54966/jreen.v25i2.1079]]>
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var str = 'Cost optimization of a wind-solar-diesel system with battery storage';
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Hybrid energy system]]>
Optimization]]>
COE]]>
HOMER]]>
Metaheuristics algorithms]]>
This work presents the optimization of a wind-solar-diesel system with battery storage for a continuous and reliable production of electrical energy. In this context, detailed mathematical modeling of the present system and its operation algorithm has been presented. The objective function of the system is to minimize its cost of energy (CoE) which estimates the average lifetime cost of power production per kWh. The cost elements comprising the CoE include investment costs, fuel costs, and operation and maintenance costs. The optimization is performed in the HOMER software in addition to three metaheuristic optimization techniques namely the Cuckoo Search algorithm (CS), the BAT algorithm (BA) and the Firefly algorithm (FA). The simulations conducted in this paper are based on meteorological data collected from an installation in Bouzareah. Simulation results show the excellent properties and superiority of the CS optimization method compared to HOMER, BA and FA algorithms and demonstrate the feasibility of the proposed hybrid PV-wind-diesel-battery system in Bouzareah.]]>
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var str = 'Saheb-Koussa, Djohra';
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var str = 'Aries, Nawel';
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Journal of Renewable Energies; Vol. 25 No. 1 (2022): Volume 25 (Regular); 127 – 151]]>
Journal of Renewable Energies; Vol. 25 No 1 (2022): Volume 25 (Regular); 127 – 151]]>
2716-8247]]>
1112-2242]]>
10.54966/jreen.v25i1]]>
Renewable Energy Development Center]]>
2023-10-04]]>
Copyright (c) 2022 Journal of Renewable Energies]]>
https://creativecommons.org/licenses/by-sa/4.0]]>
https://revue.cder.dz/index.php/rer/article/view/1076/1049]]>
application/pdf]]>
eng]]>
info:eu-repo/semantics/article]]>
info:eu-repo/semantics/publishedVersion]]>
https://revue.cder.dz/index.php/rer/article/view/1076]]>
10.54966/jreen.v25i1.1076]]>