This article addresses diagnosis and fault detection in electric drives, particularly for an asynchronous motor coupled to an inverter. The objective is to ensure service continuity and fault tolerance, both of which are essential in industrial applications. Electric machines are a fundamental component of all industrial operations. They play a critical role across various sectors due to their robustness, cost-effectiveness, relative simplicity, and ease of maintenance. The faults under investigation may affect the motor itself, the power converter, the mechanical components, or the sensors. The system operates at variable speed and is controlled using field-oriented vector control, along with speed regulation. Based on fault characterization results, obtained either from simulations or experimental measurements, two diagnostic approaches are proposed:

• Signal Processing Approach: To address the challenges associated with variable speed operation, constant position increment sampling is applied.
• Analytical Redundancy Approach: Dedicated observers are designed and experimentally validated for effective fault diagnosis and detection.

Finally, based on the developed tools, strategies are proposed to implement a comprehensive electric drive monitoring system. This system is designed to continuously assess the health status of key components and to detect faults and malfunctions within the industrial process. As a result, this work contributes to enhancing system reliability, minimizing downtime, and enabling predictive maintenance in industrial environments.

Like any industrial process, a photovoltaic (PV) system can be subject to various faults and anomalies during its operation, leading to a decrease in performance or even system shutdown. This thesis focuses on advanced diagnosis, as well as the detection and localization of faults in a PV installation, with the aim of reducing maintenance costs and optimizing productivity. Photovoltaic solar energy represents a promising alternative to the gradual depletion of fossil resources, due to its many advantages: cleanliness, renewability, silent operation, and low environmental impact. The presented work involves monitoring the behavior of photovoltaic cells under various climatic conditions. The modeling is based on equivalent electrical circuits, allowing for the analysis of the I-V and P-V characteristics of the generator. The simulation, carried out using MATLAB/Simulink, highlights the effect of variations in irradiance and temperature on the energy performance of the system. In addition, a detailed study of typical faults affecting the various components (modules, cabling, junction boxes, converters, and inverters) is conducted, with each fault analyzed in terms of its potential impact on overall efficiency. Finally, monitoring and diagnostic methods are proposed, based on the real-time measurement of parameters and the calculation of derived quantities such as efficiency, energy losses, or the performance ratio. This work thus contributes to improving the reliability, efficiency, and maintenance of photovoltaic installations, from a sustainable development perspective and with the goal of optimal integration into the energy mix.

Single-walled carbon nanotubes (SWCNTs) were filled with various materials. Metal halides are the most popular group of filled materials. Among them are 3d, 4d, and 4f metal halides, such as iron halides [1], cadmium halides [2], and terbium halides [3]. In this work, we filled SWCNTs with novel 6p metal halides, lead chloride, lead bromide, and lead iodide. The novelty of this work is the new incorporated materials, which were filled in SWCNTs with a diameter of 1.4 nm. Indeed, we filled SWCNTs with three materials with various properties, and we investigated the filling and electronic properties of the lead halide-filled SWCNTs. The aim of this work was to analyze the filling ratios, microstructure, chemical composition, and electronic properties of lead halide-filled SWCNTs. The investigation methods were scanning transmission electron microscopy (STEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). STEM showed the filling of the SWCNTs, and XPS confirmed the chemical composition of the lead halide-filled SWCNTs. Raman spectroscopy at different laser wavelengths between 458 and 647 nm showed variations in the properties of the lead halide-filled SWCNTs. In the radial breathing mode and G-band, the components showed shifts, which indicated doping. The C 1s XPS data confirmed the p-doping of the filled SWCNTs. The obtained data are necessary for nanoelectronic applications of the filled SWCNTs.

[1] М. V. Kharlamova et al. Russian Nanotechnologies, 2009, 4(9-10), 77-87.

[2] M. V. Kharlamova et al., Journal of Materials Science, 2013, 48(24), 8412-8419.

[3] M. V. Kharlamova et al., Applied Physics A, 2017, 123(4), 239.

In power systems, the main control mechanism responsible for maintaining the grid frequency and voltage at their nominal values ​​and the exchange of real power between balancing zones at the programmed values ​​is called automatic generation control (AGC). The load frequency control loop named LFC controls the real power and frequency, while the automatic voltage control loop called AVR controls the reactive power and voltage. Variations in power demand disrupt the balance between active and reactive power, thus causing frequency and voltage deviations from the nominal values. Therefore, it is essential to design a robust and optimal regulator that is both accurate and responsive to maintain the system parameters within their nominal ranges. However, with the integration of clean energy and intermittent generation quality caused by climate change, generating fluctuations in system frequency, coordinated AGC-PMS control with hybrid storage systems is required to improve the quality and stability of power systems. A hybrid energy storage system (HESS) can improve frequency and voltage control by balancing supply and demand, stabilizing the grid, and ensuring backup power. This article proposes a study approach integrating both AGC and a power management system (PMS) within an isolated Microgrid. Taking into account the integration of renewable energy sources (RESs), the proposed approach favors the use of an HESS to reduce fluctuations induced by the integration of RESs, which are themselves affected by climate variations. The system studied includes a photovoltaic generator and wind farm. A hybrid energy storage system was installed to ensure power management and control. It consists of redox flow batteries (RFBs), superconducting magnetic energy storage (SMES), electric vehicles (EVs), and fuel cells (FCs). The coordinated AGC-PMS strategy was tested across different case studies. The results obtained show good performance and demonstrate the validity and effectiveness of the proposed method.

Phenoxymethylpenicillin is a narrow-spectrum β-lactam antibiotic commonly used in South Africa. It is considered a pharmaceutical pollutant in municipal wastewater treatment plants (WWTPs), requiring wastewater-based epidemiology (WBE). Realistic simulation of phenoxymethylpenicillin degradation in wastewater treatment plants is limited by multifaceted microbial dynamics and fluctuating physicochemical conditions. This research examines pseudo-first-order degradation kinetics utilizing MATLAB calculations to estimate the distribution and transition pathways of phenoxymethylpenicillin in wastewater treatment plants in Johannesburg. Existing high-performance liquid chromatography–mass spectrometry (HPLC-MS) analytical data was used as modeling data, calibrated in line with critical parameters including sludge retention time (5–25 days), hydraulic retention times (4–12 hours), influent concentration variability (0.1-10 µg/L), degradation constant k (0.05–0.2 h^-1), and operating temperature profiles (15–30°C). Sensitivity analysis identified critical parameters influencing degradation, whereas model validation included the coefficient of determination (R²) and mean squared error (MSE) to compare simulated outputs with measured concentrations. The established model confirmed that pseudo-first-order kinetics effectively characterizes the degradation curve, exhibiting performance metrics of R² at 0.9734 and an MSE of 0.675, which are within acceptable thresholds, aligning with similar β-lactam research. The validated model provides dependable predictions of phenoxymethylpenicillin, facilitating the amalgamation of chemical kinetics, simulation modeling, and environmental engineering techniques to combat pharmaceutical contamination in urban water systems.

The utilization of waste biomass, such as chicken manure (CM), for producing valuable products like fermentable sugars has gained increasing research attention. However, limited studies have explored the effect of acid pretreatment on sugar recovery efficiency specifically from CM. This study investigates the production of glucose and xylose from CM using dilute sulfuric acid (H₂SO₄) at concentrations of 0.6, 0.8, and 1.0 M under varying conditions. The results indicate that the highest yields were achieved from decrystallized CM, producing 46.21 mg glucose/g CM and 8.47 mg xylose/g CM under optimal conditions of 0.6 M H₂SO₄ and 100°C. In contrast, non-decrystallized CM yielded 13.98 mg glucose/g CM and 1.67 mg xylose/g CM under 1.0 M H₂SO₄ and 100°C. The decrystallization process using concentrated sulfuric acid effectively disrupted the lignin structure and partially hydrolyzed hemicellulose, enhancing cellulose accessibility during subsequent dilute acid hydrolysis. The study also revealed that glucose and xylose yields decreased as the dilute acid concentration increased from 0.6 to 0.8 M and temperature rose from 80 to 100°C for decrystallized CM. Conversely, for non-decrystallized CM, sugar yields increased with higher acid concentration and temperature. These findings highlight the critical role of pretreatment in improving sugar recovery from CM and suggest that optimizing acid concentration and thermal conditions can enhance the efficiency of biomass conversion. This research contributes to the sustainable valorization of agricultural waste into bio-based products.

Kick events during drilling operations represent critical safety risks, often escalating into blowouts if not detected and controlled promptly. Conventional detection relies heavily on isolated parameter alarms and manual interpretation, which may result in delayed responses. This study proposes the development of a real-time Early Warning Indicator Matrix (EWIM), a decision-support tool that integrates multiple real-time drilling parameters into a unified risk visualization model.

The EWIM is developed using simulation data generated from the DS-5000 Scientific Drilling Simulator, a high-fidelity tool replicating realistic drilling environments. The matrix integrates field-observable parameters such as flow in/out differential, pit gain/loss, standpipe pressure, rate of penetration, return flow percentage, hook load, and mud weight changes. These indicators are processed through a traffic-light threshold model (green, yellow, or red) to yield dynamic risk scores for kick onset.

A series of controlled kick simulations were executed to construct and calibrate the EWIM. Post-simulation analysis involving bottom-hole pressure and influx volume validates the matrix accuracy and sensitivity. Preliminary results demonstrate the EWIM's ability to provide earlier, more holistic warnings compared to traditional single-parameter monitoring.

This approach enhances both training outcomes and operational monitoring by offering a scalable, field-applicable framework for early kick detection. The proposed EWIM methodology makes a significant contribution to real-time process control and proactive safety in drilling operations.

The increasing digital integration of Industrial Control Systems (ICSs), including Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCSs), has brought both operational efficiencies and greater exposure to cyber threats. Traditional cybersecurity approaches, such as signature- and rule-based Intrusion Detection Systems (IDSs), often fail to detect novel and stealthy attacks, posing significant risks to critical infrastructure. This paper presents a deep learning-based threat detection framework tailored for ICS environments, leveraging sensor data, actuator signals, and network communication logs. The framework incorporates advanced neural architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer models to capture complex temporal and spatial patterns indicative of malicious activity. These models were trained and evaluated using the publicly available HAI security dataset. The results demonstrate high effectiveness across all models, with the Transformer achieving the highest accuracy (92%), followed by the CNN (91%) and LSTM (90%). Precision scores were 93% for LSTM, 92% for CNN, and 91% for Transformer; recall was 92% for Transformer, 91% for CNN, and 90% for LSTM. All models yielded an F1-score of 91%, reflecting a strong balance between precision and recall. While each architecture showed strengths, the Transformer exhibited superior generalization. The study also addresses key challenges such as data imbalance, overfitting, explainability, and deployment constraints. Solutions such as hybrid modeling, federated learning, and digital twin integration are discussed to enhance resilience and scalability. The proposed approach demonstrates that deep learning can significantly strengthen real-time cybersecurity monitoring in ICS, offering a robust defense against evolving threats.

Cagaita (Eugenia dysenterica), a native fruit of the Brazilian Cerrado, has high potential for the elaboration of fermented products due to its content of sugars and bioactive compounds. Alcoholic fermentation emerges as a promising alternative for the use of this raw material, resulting in beverages with differentiated sensory characteristics and added value. In addition, the study of the volatile compounds generated during the fermentation process is essential to understand the aromatic profile of the final product and its acceptance by the consumer. The objective of this study was to identify the volatile compounds present in the fermented juice of cagaita (Eugenia dysenterica). A sample of cagaita alcoholic ferment was filtered, properly stored and sent to the Laboratory of Extraction and Separation Methods (LAMES-UFG) in Goiânia-GO for the identification of volatile compounds by gas chromatography. Analyses were performed with 3 μL of the sample by Headspace in an Aglient gas chromatograph model 7890B coupled to a 7000D triple quadrupole mass spectrometer, equipped with a DB-WAX column (30 m x 250 μm x 25 μm). Five main compounds were identified: carbon dioxide (5.97%), acetaldehyde (0.94%), ethanol (89.08%), acetoin (1.73%) and phenylethylalcohol (2.28%). Ethanol was the major compound, accounting for approximately 89% of the volatile composition, followed by carbon dioxide and phenylethylalcohol. These results indicate that the fermentation process resulted predominantly in the production of ethanol, with the presence of other volatiles that may have contributed to the sensory profile of the beverage.

Introduction: Open-circuit cooling systems (OCCS), integral to industrial processes, often release blowdown water containing high concentrations of treatment chemicals. If unmanaged, these discharges pose substantial risks to aquatic ecosystems and human health. This study addresses the environmental implications of chemical emissions from OCCS blowdown by developing a predictive model to estimate contaminant concentrations in receiving water bodies.
Methods: The research employs a computational model based on mass balance equations to simulate the dynamics of chemical emissions from blowdown water. It incorporates key operational variables, including flow rates, degradation rates, and volatilization characteristics. The model evaluates two chemical dosing strategies: continuous and fractional, and their resultant pollutant dispersal patterns in river systems. Validation was conducted using empirical data from sulfuric acid (H₂SO₄) applications at the nuclear power plant between 2015 and 2022.
Results: The model showed strong agreement with observed sulphate ion concentrations in the receiving water body, confirming its predictive reliability. Continuous dosing resulted in stable levels of pollutants, while fractional dosing caused temporary peaks that did not exceed regulatory values.
Conclusion: The modeling of blowdown water reveals significant implications for river water quality and highlights the urgent need for more effective wastewater management strategies within industrial contexts. Validation with measured SO₄^2- concentrations confirmed the model’s accuracy, making it a valuable tool for guiding regulatory compliance and optimizing cooling water treatment practices.