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Photovoltaic cells convert absorbed solar energy into electricity by transforming the incident visible wavelengths of solar radiation on their surface, while the other wavelengths are transformed into thermal energy. However, the main issue they face is the elevated temperature of PV modules during operation, which reduces their energy production efficiency. Thermal control of photovoltaic panels using phase change materials (PCMs) has been a potential solution to overcome this problem and perform as a passive cooling material. In this study, the effectiveness of using a novel bio-based eutectic PCM in thermal regulation and efficiency enhancement of the PV panel was studied experimentally. The prepared PCM was characterized and then integrated onto the backside of the PV module. An indoor experimental study was conducted to compare the performance of PV-PCM with a reference PV panel without PCM. The DSC results revealed that the prepared PCM has an appropriate phase change temperature and latent heat capacity for cooling a PV module. In addition, the incorporation of PCM on the backside of the PV panel (PV-PCM) resulted in a significant reduction in surface temperature by 11.46 °C (14.45 %) compared to the reference PV (PV-ref) panel without PCM. Notably, an increase of 7.23 % in the maximum output power is observed in the PV-PCM system.

This study investigates the use of CuO-enhanced Paraffin wax (RT-55) in a horizontal co-axial regenerator, addressing the challenge of low thermal conductivity in Phase Change Materials (PCM). The focus is primarily on examining the influence of CuO nanoparticles on the thermal and energetic performance of the regenerator. The research begins by exploring the impact of CuO volume fraction on both the total melting time and energy density. Subsequently, the study incorporates the addition of 3 wt.% of nanoparticles to diverse regenerator configurations, including non-uniform longitudinal fins, eccentricity, and combined fins with eccentricity. The objectives are twofold: firstly, to evaluate the effect of nanoparticles on power densities across various geometries; secondly, to assess the implications of natural convection within each configuration upon nanoparticles addition. The results demonstrate that while the nanoparticles enhance the power density of the system, they concurrently modify the storage capacity of the Latent Thermal Energy Storage System (LTES). Furthermore, the impact of nanoparticles varies depending on the regenerator’s configuration, owing to the influence of natural convection. Therefore, it was demonstrated that in the eccentric case, which experiences the highest impact of natural convection, the total melting time is increased by 2% when nanoparticles are added. Conversely, the finned and combined cases show a 4% and 8% reduction in the melting time with the addition of the same amount of CuO nanoparticles.

Reverse flotation is a commonly used method for separating carbonate minerals from apatite, but its application to phosphate beneficiation coarse rejects, which are low in P2O5, is often costly due to the high collector dosages used. This study aimed to explore alternative techniques for preconcentration before flotation to improve recovery rates and reduce costs. Our investigation focused on dense medium separation and acid leaching. Dense medium separation, conducted at a cut-off density of 2.76, yielded a preconcentrate with 27% P2O5 and a recovery rate of 90%. The feed material had an initial P2O5 content of 20.52% and a particle size range of +40 µm to −4 mm. In contrast, acid leaching, employing an 8% acetic acid solution over 35 min, yielded a concentrate with 29.11% P2O5, an LOI of 8.99%, and a recovery rate of 100% from an ore fraction [400–200 µm] with an initial P2O5 content of 22.82% and an LOI of 15.78%. Furthermore, integrating flotation and leaching resulted in a concentrate with 32.27% P2O5 and a recovery rate of 98.38%. These findings suggest that combining acid leaching with flotation can enhance P2O5 recovery and reduce processing costs for low-grade phosphate ores. [ABSTRACT FROM AUTHOR]

Phosphorus is a vital nutrient essential for plant development and numerous biological functions. It is primarily obtained from phosphate rock through a process known as beneficiation. However, the declining reserves of high-grade phosphate rock, combined with the uneven global distribution of phosphorus and the environmental impacts associated with its extraction and use, highlight the need for a more efficient management of this critical resource. Increasingly, alternative sources of phosphorus, such as extraction from waste materials, are being explored. This study aims to assess the feasibility of recovering phosphorus from coarse rejects produced during phosphate beneficiation at a phosphate washing plant. Before conducting laboratory preparation and reverse flotation tests, the sample underwent initial laboratory examination and analysis. The sample was found to contain low-grade apatite minerals with a phosphorus pentoxide (P2O5) content ranging from 19% to 20%. Additionally, carbonate and quartz were identified as the primary accompanying minerals. Flotation experiments yielded a phosphorus recovery rate of 29% P2O5, with a carbonation rate of 1.6. Although this recovery rate is slightly below the commercial phosphate standard of 30% P2O5, it represents a significant improvement and demonstrates potential for further optimization to meet industry requirements. Consequently, these coarse discarded rejects could serve as a supplementary source of phosphorus in the future. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive and comparative study of solar energy forecasting in Morocco, utilizing four machine learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), recurrent neural networks (RNNs), and artificial neural networks (ANNs). The study is conducted using a smart metering device designed for a photovoltaic system at an industrial site in Benguerir, Morocco. The smart metering device collects energy usage data from a submeter and transmits it to the cloud via an ESP-32 card, enhancing monitoring, efficiency, and energy utilization. Our methodology includes an analysis of solar resources, considering factors such as location, temperature, and irradiance levels, with PVSYST simulation software version 7.2, employed to evaluate system performance under varying conditions. Additionally, a data logger is developed to monitor solar panel energy production, securely storing data in the cloud while accurately measuring key parameters and transmitting them using reliable communication protocols. An intuitive web interface is also created for data visualization and analysis. The research demonstrates a holistic approach to smart metering devices for photovoltaic systems, contributing to sustainable energy utilization, smart grid development, and environmental conservation in Morocco. The performance analysis indicates that ANNs are the most effective predictive model for solar energy forecasting in similar scenarios, demonstrating the lowest RMSE and MAE values, along with the highest R2 value. [ABSTRACT FROM AUTHOR]

This article presents an innovative approach to analyzing and extracting electrocardiogram (ECG) signals from the abdomen and thorax of pregnant women, with the primary goal of isolating fetal ECG (fECG) and maternal ECG (mECG) signals. To resolve the difficulties related to the low amplitude of the fECG, various noise sources during signal acquisition, and the overlapping of R waves, we developed a new method for extracting ECG signals using blind source separation techniques. This method is based on independent component analysis algorithms to detect and accurately extract fECG and mECG signals from abdomen and thorax data. To validate our approach, we carried out experiments using a real and reliable database for the evaluation of fECG extraction algorithms. Moreover, to demonstrate real-time applicability, we implemented our method in an embedded card linked to electronic modules that measure blood oxygen saturation (SpO2) and body temperature, as well as the transmission of data to a web server. This enables us to present all information related to the fetus and its mother in a mobile application to assist doctors in diagnosing the fetus's condition. Our results demonstrate the effectiveness of our approach in isolating fECG and mECG signals under difficult conditions and also calculating different heart rates (fBPM and mBPM), which offers promising prospects for improving fetal monitoring and maternal healthcare during pregnancy. [ABSTRACT FROM AUTHOR]

With the rapid development of the sustained economy, the sales of electric vehicles are increasingly pressing, and today the maintenance has severely impeded the marketing and usage of electric vehicles. The maintenance procedure for electric vehicles is considered a critical process, tell its affect the security and availability directly with the passengers. Many of research efforts are still devoted to develop and innovate on electric traction systems diagnostic and prognostic. Furthermore, in high-quality education, especially engineering education, topics concerning the vital and actual concerns should comprise more than theoretical knowledge, in purpose to close the relationship between the present technology and the student’s environment and provides hands-on engineering experience and training of general engineering skills, in order to avoid non-standard, unskilled maintenance work. The paper presents a first step towards designing a test bench of a fully electric vehicle’s Powertrain used for research and educational purposes using model-based systems engineering (MBSE) and systems modelling language (SysML) thorough the CESAM architecting and modelling framework. As the first step of the system’s design, an operational perspective layout of the diagnostic and prognostic’s test bench is built and presented using this technique.

The field of study of autonomous trucks especially in open pit mines has been the subject of considerable research over the past three decades. In the last ten years in particular, interest in this area has improved considerably. The implementation of autonomous systems is an integral organizational and cultural change. This work is a guideline for the safe and effective implementation of autonomous trucks in mines. It provides an integral view of the different approaches to implementing autonomous mining trucks with respect to safety criteria, operating costs, etc. It also presents the criteria and measures to be considered when maintaining the haulage routes (vertical/horizontal/combined alignment) that are a vital component for the success of an automation project. Mine site designers and planners should ensure that the design and construction of traffic areas for these trucks are compatible with autonomy and minimize interactions with personnel and non-autonomous equipment. A proposed infrastructural requirement for the autonomous truck zone is presented in this work.

This study conducted an in-depth analysis of the performance of the largest Grid-Connected Solar Photovoltaic System in Burkina Faso from 2019 to 2021. The research utilized measured data and simulated the plant's performance using the PVGIS database. The results revealed that the months with high solar radiation were the most energy-productive, indicating a direct correlation between solar irradiance and energy generation. During the rainy season (July and August), the PV plant exhibited the highest conversion efficiency. Conversely, the hot season (March and April) was associated with the lowest conversion efficiencies, with module temperatures reaching approximately 47 °C. Efficiency decreased from 12.29% in 2019 to 12.10% in 2021. The system's performance ratio ranged from 80.73% in 2019 to 79.36% in 2021, while the capacity factor varied from 19.89% in 2019 to 19.33% in 2021. The final yield, measured in hours per day, was 4.89 h/d in 2019, 4.61 h/d in 2020, and 4.92 h/d in 2021. These findings highlight the deterioration in the performance of the Zagtouli PV plant over time. The study emphasizes the utility of using PVGIS-SARAH2 to forecast solar radiation and estimate energy output in PV systems. A semi-automatic cleaning system is used to clean the modules. This cleaning mechanism is inefficient because it is inconsistent. To increase the PV plant's effectiveness, improved cleaning systems with more advanced mechanisms are required. This research, the first of its kind on the largest PV power plant connected to Burkina Faso's national grid, serves as a valuable model for other power plants currently under construction or in the planning stages. [ABSTRACT FROM AUTHOR]