The optimization of electrostatic separation systems is essential for increasing the recycling efficiency of electrical and electronic waste (e-waste) and supporting sustainable waste management. This work presents an innovative double-sided inclined electrostatic conveyor system, assisted by controlled vibrations, for separating aluminum–plastic composites from electronic waste materials. The device combines two complementary electrical mechanisms: electro-adhesion, which retains conductive aluminum particles through applied electric fields, and traveling-wave electric fields, which repel insulating plastic particles. Vibrations enhance particle mobility, limit agglomeration, and improve separation dynamics. A systematic optimization of the electrical system was performed using a three-level full factorial design (3²). Two operational parameters were selected: applied voltage (1.0, 1.5, and 2.0 kV) and conveyor inclination angle (20°, 30°, and 40°). The responses were aluminum purity and recovery efficiency. Eighteen randomized runs were carried out, and results were analyzed statistically by analysis of variance (ANOVA).

Experimental results confirmed that both voltage and inclination significantly affect separation performance. Increasing these electrical parameters improved aluminum purity, which reached 100% at 2.0 kV and 40°. Recovery efficiency remained consistently high, attaining 99% at optimum settings. The interaction between voltage and inclination was significant, highlighting the need to adjust these factors simultaneously. Regression models showed excellent predictive capability (R² > 98%), validating the design methodology's robustness. This study confirms the effectiveness of factorial design for optimizing electrostatic separation systems in electrical engineering applications. The identified optimal electrical operating conditions ensure maximum aluminum purity and recovery efficiency, offering practical perspectives for industrial recycling of aluminum–plastic waste from electrical and electronic equipment.

While the Neutral Point Clamped (NPC) converter, Flying Capacitor (FC) converter, and Cascaded H-Bridge (CHB) converter represent classical multilevel topologies, these topologies possess certain disadvantages that limit their applicability in certain contexts. NPC topology encounters difficulties in capacitor voltage balancing, particularly when the number of levels is high, the increasing number of clamping diodes complicates the voltage balance algorithm.
T-type inverters present a superior efficiency compared to their NPC counterpart, and the observed efficiency enhancement in T-type inverters can be attributed to their reduced conduction and switching losses. Furthermore, a key advantage of T-type inverters over NPC topologies lies in the elimination of clamping diodes, which are traditionally necessary for maintaining the neutral point potential at the negative or positive DC bus voltages. An active bidirectional switching device is utilized in T-type inverters to achieve voltage clamping. This device interfaces the midpoint of each phase leg with the common node of the series-stacked DC-link capacitors. Nevertheless, T-type inverters, much like NPC converters, confront the challenge of maintaining balanced voltages among the series-connected DC-link capacitor. In this paper, as a contribution, a new technique for capacitor balancing applied to three level T-type inverter based on redundant vectors of space vector modulation is presented and studied.

This article presents a comprehensive thermal analysis of the Switched Reluctance Machine (SRM), with the objective of improving its efficiency, reliability, and operational lifespan. The study begins with an in-depth examination of the electromechanical characteristics of the SRM, providing essential insights into its operating environment. Particular attention is given to the identification and quantification of the main sources of thermal losses, including mechanical, electrical, and magnetic losses.

To understand heat propagation within the machine, a thermal modeling approach is applied, covering the three principal modes of heat transfer: conduction, convection, and radiation. These models make it possible to analyze the distribution of heat among the internal components of the SRM, depending on their structure and the properties of the materials. The theoretical study is supported by numerical simulations performed using the FEMM 4.2 software.

The simulations explore different operating scenarios, including cases with insulated and non-insulated windings. This analysis highlights the crucial role of insulating materials in thermal management. More specifically, insulation enhances heat dissipation and contributes to a more uniform temperature distribution within the machine.

The results confirm that an optimized thermal design, particularly through the careful selection and application of insulating materials, can significantly improve the thermal performance of the SRM. This is reflected in higher energy efficiency and extended lifespan, making the SRM more suitable for demanding industrial applications

This paper investigates the effectiveness of a proposed approach for local power control of distributed generation (DG) units used for power and voltage support in passive distribution networks under high uncertainty in load and unexpected daily load variations. The approach is based on the use of an intelligent algorithm, Particle Swarm Optimization (PSO), to determine an asymptotic reference for the optimal real-time power control of small-scale DGs. The optimization problem is formulated with the desired objective functions, subject to network constraints. The principle of the proposed strategy is to determine the reference voltage at the connection nodes of the distributed generators (DGs) corresponding to optimal power generation, either as a fixed value or by deriving a regression line from a two-variable dataset comprising the DG’s optimal power generation and the voltage at its connection node. The optimal regression line is derived using the statistical least squares method. The optimal power–voltage datasets are obtained from various random load scenarios by applying the Particle Swarm Optimization (PSO) algorithm. In this study, the optimization algorithm is applied to minimize power losses and improve the voltage profile. The investigation includes simulation results and analysis conducted on a modified IEEE 33-bus radial distribution system with DG units providing both active and reactive power. The impact of active and reactive power generation on the objective functions is analyzed. Asymptotic reference values for the local control of DG units are derived for different scenarios, and the results demonstrate the effectiveness of the proposed strategy.

While unmanned aerial vehicles (UAVs) with thermal cameras are established tools for inspecting photovoltaic (PV) plants, most systems rely on offline or cloud-based data processing, which introduces latency and data transmission costs. This study presents a novel contribution by developing and validating a fully autonomous inspection system that performs real-time, onboard fault detection using embedded computer vision.

The core innovation lies in the implementation of a lightweight convolutional neural network (CNN) on a low-cost ESP32 microcontroller, a highly resource-constrained environment. This enables the system to analyze thermal images captured by the UAV and identify anomalies—such as hotspots and burned cells—directly in the field, eliminating the need for external processing infrastructure. The CNN model was trained on a custom dataset of annotated thermographic images, using data augmentation and class balancing to ensure robust performance.

Preliminary results demonstrate that this embedded approach enables accurate, autonomous, and non-invasive thermal inspections without requiring system shutdowns. By integrating georeferenced aerial data acquisition with immediate onboard analysis, our system significantly reduces operational costs and diagnostic latency compared to traditional methods. This work adds to the existing knowledge by proving the viability of deploying computer vision models on edge devices for industrial inspection, offering a scalable, efficient, and accessible architecture that advances intelligent predictive maintenance in the renewable energy sector.

This work presents the design and implementation of an intelligent assistant embedded system that integrates emotion recognition with real-time student assessment on a low-cost hardware platform. The system is based on an ESP32 microcontroller with a camera module, responsible for capturing student facial expressions during test activities. A Convolutional Neural Network (CNN), pre-trained for facial emotion recognition, is deployed in a hybrid architecture where the ESP32 performs frame preprocessing and transmits data to a server running inference services optimized with TensorFlow Lite.

The approach is motivated by findings in affective computing and educational psychology, which emphasize the strong correlation between emotional states, concentration, and test anxiety. To estimate student readiness, the system employs a weighted mapping method that combines output probabilities with predefined emotion weights. For example, neutral or happy expressions contribute positively to concentration, while fear or anger increase nervousness. Normalized concentration and nervousness scores are computed over time, and readiness is determined using threshold-based rules: students are considered prepared when concentration reaches at least 60/100 while nervousness remains below 50/100.

An interactive dashboard allows teachers to monitor student states in real time and analyze historical performance. The proposed assistant demonstrates the feasibility of combining embedded devices, lightweight deep learning models, and real-time analytics for educational applications. By providing interpretable metrics of emotional conditions during assessments, the system offers a novel tool to support both student evaluation and teacher decision-making.

The increasing integration of renewable energy sources into electrical grids necessitates efficient transmission solutions, particularly for offshore wind farms requiring connection to distant onshore networks. Modular Multilevel Converters (MMCs) present significant computational challenges in HVDC system simulations due to their complex structure involving hundreds of submodules and nodes. This study presents a novel detailed equivalent modeling approach using Thevenin equivalent circuits to efficiently simulate MMC behavior while maintaining accuracy for system-wide studies and DC fault analysis.

The research develops comprehensive Thevenin equivalent models that dramatically reduce computational complexity by representing hundreds of individual submodule nodes through three equivalent nodes per arm. The proposed modeling approach connects islanded offshore wind farms to onshore AC grids through MMC-based VSC HVDC symmetrical monopole systems, where the onshore converter controls DC voltage while the offshore converter maintains AC voltage magnitude and frequency as a slack bus.

The developed Thevenin equivalent models demonstrate excellent performance under both normal operating conditions and fault scenarios. During normal operation, the models accurately capture steady-state power transmission characteristics, dynamic response to wind variations, and control system interactions. Under fault conditions, the models successfully represent transient phenomena, fault current contributions, and system recovery dynamics for various fault scenarios including DC line faults and AC grid disturbances. The developed modeling technique provides a computationally efficient yet accurate representation of offshore wind farm integration via MMC-HVDC systems, enabling comprehensive analysis of both normal and fault dynamic performance for renewable energy integration studies.

Industrial steam generators serve an important role in a variety of industries, including the chemical and petrochemical industry, engineering industry, and nuclear applications. These devices are used to produce steam that can be used for a variety of purposes, such as heating and sterilization. In order to ensure the safety of a steam generator, nondestructive testing (NDT) techniques are applied. Tube support plates (TSPs) maintain tubes, make sure they are properly spaced, and help avoid fretting. However, TSPs are regarded as disturbing signals for tubes. In this paper, we investigate the influence of the support plate on eddy current testing (ECT) using a differential probe of a steam generator tube made of Inconel 600 and carrying a defect. The modeling of eddy current testing is based on solving a 2D axisymmetric finite element formulation under steady-state conditions via Comsol Multiphysics Software. The impedance of the differential coil is calculated using the direct method, and the signal of the difference impedance is obtained from two simulations: with flaws and without flaws.

The simulation of the studied system under the abovementioned conditions lets us conclude with the following points:

• The signal of the impedance difference is higher when the TSP is introduced in the simulation.
• The amplitude of the impedance difference increases as the TSP approaches the tube.

Indoor air quality plays a critical role in human health and well-being, as it is influenced by daily activities such as cooking, cleaning, or exposure to smoke. This study aims to assess indoor air quality in four distinct contexts: rest, cooking, smoke, and cleaning. To achieve this, an experimental setup was developed using MQ2, MQ4, MQ7, and MQ135 gas sensors, together with a DHT11 temperature/humidity sensor. The collected data were structured into CSV files and analyzed using two machine learning models: k-nearest neighbors (k-NN) and a classical neural network.

The results demonstrated strong classification performance from both models. The k-NN model achieved an accuracy of 97.75% on the training set and 96.8% on the test set. In comparison, the classical neural network outperformed k-NN, reaching a test accuracy of 98.5%. This indicates the superior ability of neural networks to capture nonlinear patterns in sensor data, providing more reliable classification of air quality contexts.

In conclusion, the findings highlight the potential of combining gas sensors with machine learning to build effective and intelligent systems for monitoring indoor air quality. Future work will focus on optimizing the neural network architecture, reducing computational cost, and integrating additional environmental parameters to further improve performance and applicability in real-world conditions.

Electrostatic separation is a proven technique for the selective sorting of solid materials and is widely applied in recycling and waste valorization. Conventional methods, which rely on forces acting on charged or polarized particles, often face limitations in selectivity and efficiency when treating complex metal/plastic mixtures. This study introduces and investigates a novel device, the Double-Side Electrostatic Actuator (DSEA), designed to generate a specific electro-adhesion force acting on metallic particles.

The DSEA consists of a dielectric layer with segmented parallel electrodes on the top surface and a continuous plate electrode on the bottom side. This double-sided configuration allows the application of high-amplitude polyphase voltages without breakdown, thus improving performance compared to single-sided systems. The electro-adhesion force was quantified using an experimental setup that combined the DSEA with a suction aspirator placed 5 mm above the surface to evaluate particle retention under airflow.

Experiments were performed with monocomponent copper particles and with binary mixtures containing 70% copper and 30% PVC. The results showed that adhesion depends strongly on electrode geometry, with smaller widths and gaps leading to stronger retention. At 2000 V and airflow ≤1.3 m³/min, copper recovery and purity reached 100%, while all plastic particles were completely removed. Numerical simulations with COMSOL confirmed the concentration of the electric field at electrode edges, explaining the selective adhesion mechanism.

These results demonstrate the potential of the DSEA for efficient metal/plastic separation, combining high recovery with high purity, and provide new insights into advanced electrostatic recycling technologies.