Publications Scientifiques

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    BiMnO3 thin films synthesized by sol-gel method: Efficient photocatalytic dye degradation and hydrogen production under sunlight and visible light
    (Elsevier, 2026) Allouane, Ouiza; Toubane, Mahdia; Tala-Ighil, Razika; Beldjoudi, Nadir; Ayouz, Katia; Chabira, Fares; Boudinar, Salem; Tazerout, Mohand; Douali, Redouane
    In this work, BiMnO3 (BMO) thin films with different thicknesses (3, 5, 7, and 9layers) were successfully fabricated on glass substrates via the dip-coating technique. These BMO thin films were characterized using various techniques. X-ray diffraction analysis reveals that the as-deposited films exhibit a monoclinic phas. The crystallite size varies from 17.79 to 9.09 nm, while the roughness increases from 13.97 to 54.70 nm as the film thickness increases. UV–Visible spectroscopy measurements indicate an increase in light absorption with increasing film thickness, rising from 0.53 for 3 layers to 0.74 for 9 layers. DFT calculations indicate a direct band gap of 1.97 eV, in good agreement with the 1.62–1.81 eV values obtained from the Tauc method. BMO thin films exhibit high photocurrent densities compared to literature values, reaching 11.1 mA/cm² under visible light. The photocatalytic degradation of methylene blue (MB) by the BMO thin films increased with thickness, reaching ∼94% after 180 min under sunlight. This work will promote the development of highly efficient BMO, highlighting its potential for environmental applications
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    Software-in-the-Loop Validation of a PEC9-Based Multifunction Solar Active Filter
    (IEEE, 2026) Khettab, Soufian; Kasri, Amel; Belkhier, Youcef; Kheldoun, Aissa; Benbouzid, Mohamed
    This paper presents a novel double-stage, singlephase photovoltaic (PV) system for grid connection and active power filtering, employing a Packed E-Cell nine-level (PEC9) inverter. The proposed architecture offers an integrated solution for harmonic mitigation, reactive power compensation, and maximum power extraction from the PV source, while ensuring efficient real power injection into the grid. A modified instantaneous power (P-Q) algorithm is developed to enhance dynamic performance by generating a reference current that combines both harmonic and solar power components. This reference current is accurately tracked using Model Predictive Control (MPC) strategy applied to the PEC9 inverter, ensuring precise and fast control. The system's performance is validated through software-in-the-loop (SIL) simulation using RT-LAB, demonstrating its effectiveness under realistic operational conditions. The results confirm significant improvements in total harmonic distortion (THD) reduction, unity power factor operation, maximum power tracking, and overall dynamic response. These findings highlight the potential of the proposed PEC9-based multifunction solar active filter as an efficient and reliable solution for enhancing power quality in grid-connected PV systems
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    Optimization of Selective Harmonic Elimination for Emerging Single-Phase Five-Level Inverter Using Genetic Algorithm
    (IEEE, 2026) Maamar, Alla Eddine Toubal; Naidji, Mourad; Abdelouahed, Touhami; Boudouda, Aimad; Porumb, Radu; Mekhilef, Saad
    This study focuses on the analysis and experimental investigation of Selective Harmonic Elimination (SHE) for emerging single-phase multilevel inverter using Genetic Algorithm (GA), among the best algorithms for optimization. The employed inverter, a widely adopted emerging topology, uses fewer switches than conventional designs while delivering a five-level output voltage. GA solves transcendental nonlinear equations to find SHE's optimal commutation angles. Simulation outcomes closely align with theoretical predictions, demonstrating the method's simplicity compared to analytical techniques, suitability for inverter control, and cost-effectiveness for real-time implementation on a low-cost Arduino ATmega2560 Microcontroller. Theoretical analysis is validated through MATLAB/Simulink simulations and a hardware prototype built with efficient electronic components. Both simulation and experimental findings validate the resilience and efficacy of the presented modulation technique for emerging single-phase five-level inverters
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    Additive Manufacturing in Industry 4.0: Role and Impact
    (IEEE, 2026) Mezahem, Sid Ahmed Rayane; Chabane, Ali; Benfriha, Khaled; Titri, Wali Eddine; Amrane, Ahmed
    Additive manufacturing (AM), commonly referred to as 3D printing (3DP), is a leading-edge technology that is significantly influencing the development of Industry 4.0. The importance of AM technologies is attributed to their capacity to revolutionize conventional manufacturing practices via improved customization, production on demand, and efficient use of materials. With applications spanning sectors such as aerospace, health-care and automotive, 3DP is proving to be a cornerstone in the era of smart and sustainable manufacturing, these technologies have been widely researched and implemented to produce homogeneous and heterogeneous products with complex geometries. This article addresses the role of AM within Industry 4.0, highlighting its integration with the latest technological developments in cyber-physical systems, digital twins, the Internet of Things, cloud computing, cognitive computing, and artificial intelligence. In consideration of these advances, we try to provide a holistic understanding of how AM drives innovation, optimizes processes, and contributes to the realization of intelligent manufacturing systems. Industry 4.0 uses a series of enabling technologies that can be categorized into nine pillars. These are the technologies that have the most applications under the industry 4.0 umbrella. However, some works have added another pillar known as ’other enabling technologies’ that has limited applications in agro-foods, bio-based economics, and energy consumption.
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    Digital Twin Development for the MAP-205 SMC Assembly Mini Cell: A Use Case Approach
    (IEEE, 2026) Bibani, Yasser; Ghazi, Nawel; Chabane, Ali; Benfriha, Khaled
    he first stage of creating a digital twin for the MAP-205 SMC assembly mini-cell, a tabletop mechatronic system used for industrial automation instruction, is covered in this paper. Building a working virtual model of the mini-cell was the primary goal in order to facilitate the future integration of Industry 4.0 technology. In order to create an interactive simulation environment, a comprehensive 3D CAD model of the system was created using SolidWorks and loaded into the Unity game engine. Early component visualization and functional testing are made possible by simulation’s replication of the physical system’s fundamental mechanical processes and assembly sequence. Predictive maintenance capabilities, IoT connectivity, and real-time sensor data integration are not yet included in the project stage
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    EFFECT OF STEEL FIBERS ON THE RHEOLOGICAL AND MECHANICAL PROPERTIES OF SELF-COMPACTING CONCRETE WITH TUFF POWDER
    (Fundatia Serban Solacolu, 2026) Naadia, Tarek; Gueciouer, Djamila; Ghernouti, Youcef; Mansour, Mali̇Ka Sabri̇a
    This experimental study focuses on the development of a steel fiber-reinforced self-compacting concrete (SFSCC) incorporating tuff powder as a local mineral addition. Five mixes were evaluated to assess the influence of fiber dosage on both fresh and hardened properties. The incorporation of steel fibers leads to reduced workability and longer flow times but significantly enhances the stability of the mix by minimizing segregation risks. Mechanically, the addition of fibers results in a marked improvement in flexural strength, with gains exceeding 40% at the highest fiber content. Ductility is substantially increased, reflecting a better ability to absorb post-cracking energy. In contrast, compressive strength shows only a moderate increase, around 11%, confirming that the main contribution of fibers lies in flexural behavior and toughness. The porous texture and pozzolanic activity of the tuff promote strong fiber–matrix bonding, contributing to improved cohesion and crack control. Overall, the findings highlight the feasibility of producing a high-performance, ductile, and stable self-compacting concrete using local resources, offering a sustainable and efficient solution for modern construction needs. © (2025), (Fundatia Serban Solacolu). All rights reserved.
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    Experimental Implementation of FS-Predictive Current Control with MPPT-OTC for Performance Optimization of PMSG-Based Wind Energy Systems
    (IEEE, 2026) Sayhi, Hicham; Bourek, Amor; Ammar, Abdelkarim; Teta, Ali; Bakria, Derradji; Chennana, Ahmed
    This study proposes a predictive current control strategy for standalone wind energy conversion systems (WECSs) based on permanent magnet synchronous generators (PMSGs). The approach integrates finite-set predictive current control (FS-PCC) with an optimal-torque-control (OTC) MPPT algorithm to achieve accurate stator current regulation, reduced ripple, and improved dynamic response under variable wind conditions. Experimental tests on a 3kW setup using the dSPACE 1104 platform validate the controller's fast transient behavior, stable standalone operation, and enhanced energy extraction efficiency, demonstrating robust performance in stochastic wind environments
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    IoT integration for predictive maintenance within an industry 4.0 infrastructure
    (IEEE, 2026) Bourkab, Hayet; Chabane, Ali; Boudhar, Hamza; Benfriha, Khaled
    Industry 4.0 is transforming industries by integrating advanced technologies to create smart factories where machines, devices, and systems are interconnected. In this context, maintenance practices are evolving to ensure the reliability and efficiency of these interconnected systems, known as cyber-physical systems. Predictive maintenance, a proactive strategy, has emerged as a key solution to prevent unplanned downtime and optimize industrial operations. This article presents a literature review on the integration of the Internet of Things (IoT) into predictive maintenance, focusing on how IoT devices collect real time data from equipment and how this data is analyzed using artificial intelligence (AI) and machine learning (ML) techniques. This article examines how these technologies work together to monitor equipment conditions, predict failures, and improve maintenance efficiency. It also discusses the impact of the IoT for maintenance on reducing downtime, lowering costs, and extending equipment lifespan while addressing the challenges associated with its implementation in Industry 4.0.
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    Code IM-Assisted RIS for D2D Communications with Hardware Imperfections and Phase Noise
    (IEEE, 2026) Belaoura, Widad; Althunibat, Saud; Mazen, Hasna; Qaraqe, Khalid
    In practical communication systems, residual hardware imperfections often impose distortion noise that compromises the performance of the system. This paper investigates the effect of hardware limitations on the performance of coded index modulation (IM)-assisted reconfigurable intelligent surface (RIS) for device-to-device (D2D) communication systems. We show that the concept of coded IM can enhance both the spectrum efficiency and error performance of D2D communications in the presence of hardware imperfections, including hardware impairment at the D2D transceivers and phase noise at the RIS. In this system, spread code selection is applied to convey additional information and achieve a higher diversity gain. The D2D receiver performs detection employing a two-stage match correlation approach to reduce the high complexity of the maximum-likelihood (ML) detector. Our simulation results demonstrate the superiority of the coded IM-assisted RIS solution in improving spectral efficiency and error performance compared to the conventional RIS-assisted D2D system, while reducing the impact of hardware imperfections.
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    Enhancing the Reliability of Fault Diagnosis in Lithium-Ion Batteries Using a Hybrid Model-Based and Machine Learning Approach
    (Institute of Electrical and Electronics Engineers, 2026) Maaradji, Taha Mohamed Abdelatif; Alem, Said; Gravante, Emanuele; D'Arpino, Matilde; Rizzoni, Giorgio
    Lithium-ion batteries (LIB) are essential for electric vehicles (EV) and renewable energy storage, and their safe, reliable, and efficient operation is critical. As their use grows, robust fault diagnosis becomes key to maintaining availability and performance. A major challenge is that many battery faults exhibit similar patterns, making it difficult to distinguish and isolate specific fault types. This paper presents a hybrid approach that combines model-based methods with data-driven techniques to improve the safety and reliability of LIB systems. The proposed diagnostic architecture uses structural analysis (SA) to identify analytical redundancy for fault detection and isolation (FDI), while Extended Kalman Filter (EKF) algorithms generate residuals that capture inconsistencies between model predictions and measurements. These residuals are processed into statistical features and passed to a machine learning (ML) classifier for accurate fault detection and classification. In this series-hybrid structure, using SA–EKF residuals as classifier inputs significantly improves diagnostic performance compared to ML-only features, and among the tested classifiers, Residual-based RF achieves the highest accuracy. The method achieves 98.73% accuracy in distinguishing fault types, demonstrating high sensitivity across all faults and robustness to varying noise levels. Overall, the proposed hybrid methodology enhances fault diagnosis effectiveness and supports the safe operation of LIB systems, contributing to the transition toward clean and sustainable energy solutions