Ibn Tofaïl University

Wind power plants penetration into power grids has grown considerably due to the fact of being cheap and clean. As a result, many countries require that the wind turbines must be capable of offering ancillary services such as reactive power control and voltage regulation. Furthermore, some voltage stability indices rely on both reactive and active power; consequently, it is useful to take also into account the active power during the planning stage so as to fulfil the load requirement and to enhance the voltage stability. The present study intends to present an optimization algorithm permitting to diminish the electrical losses and enhance the voltage profile by getting the fittest allocation of active power production of traditional generators and wind power plants. Besides, wind farms reactive power injection is determined. To verify the proposed algorithm, a 40 MW wind farm (WF) made up of doubly fed induction generator (DFIG) and the 14 IEEE bus system are used. In the case study, three different metaheuristic methods are used to resolve the objective function. Finally, the simulation results are reported.

In this study, we propose using a high-gain observer (HGO) in fuel cell systems and the FC-cascade boost converter to achieve precise state estimation and improve control accuracy. The HGO offers significant advantages, including robustness to disturbances, simplicity of design, and reduced dependency on costly sensors, thereby enhancing system performance and reducing operational costs. Our results demonstrate the superior accuracy and reliability of this approach, confirming its potential as a promising solution for fuel cell applications.

Precise voltage and current regulation is essential to ensure the required power output and maximum efficiency in charging stations, particularly those utilizing Wireless Power Transfer (WPT) systems. Effective regulation techniques are necessary to manage voltage and current in Battery Electric Vehicles (BEVs) operating under various charging modes. This study outlines the controller design for different methods for charging lithium-ion batteries in a WPT charger. Initially, the fundamental concepts of WPT systems and their equivalent circuit are introduced. Subsequently, the control strategy for current regulation is detailed for the Constant Current (CC) mode, the Multi-stage Current Method (MCM), and the Pulse Charging Method (PCM). Finally, the resilience and validity of this innovative approach to controlling various techniques for charging lithium-ion batteries are demonstrated through simulations.

The treatment of sewage wastewater generates a substantial amount of sludge, which poses significant ecotoxicological and environmental challenges. Managing this sludge is critical, as traditional disposal methods carry risks: agricultural use can lead to environmental contamination, while landfill disposal raises concerns about solid waste management and the preservation of landfill sites. Our research focuses on converting this urban sludge into activated carbon. We employed various techniques to characterize NaOH-activated biochar, including infrared (IR) analysis. BET analysis revealed a specific surface area of 62.35 m²/g. We also assessed NaOH-activated biochar for its effectiveness in adsorbing brilliant cresyl bleu (BCB). The SSE error calculations indicated a first-order adsorption, while the isotherms suggested a better fit with the Sips model, indicating heterogeneous adsorption with an ns value of 0.95. Our study highlights the effectiveness of biochar in adsorbing dyes such as brilliant cresyl bleu BCB.

This paper presents an optimized control strategy for a bidirectional interleaved totem pole Power Factor Correction (PFC) converter, employing a combination of Model Predictive Control (MPC) and a Proportional-Integral (PI) controller. The converter’s mathematical model is used to implement the MPC, which ensures a unit power factor during both Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) operations. Meanwhile, the PI controller regulates the DC-side voltage. It is worth noting that the MPC’s design eliminates the need for a comparator to generate the Pulse Width Modulation (PWM) signal, simplifying the control architecture. The proposed control technique’s effectiveness is validated through various simulation scenarios in both G2V and V2G power transfer modes, demonstrating satisfactory performance. This integrated approach provides a robust solution for efficient energy management and high-quality power conversion in bidirectional PFC converters.

In this study, we propose a methodical approach to selecting an appropriate statistical distribution for reliability analysis. In this approach, we have defined a methodology for testing reliability distributions based on the Kolmogorov Smirnov K-S test for MTBF Data collected from Self-Diagnostic of a sample of 50 critical components part of a complex automotive system. Finally, we proposed two solutions: the first involves migrating from one distribution to another according to the intervals, and the second allows for the selection of the distribution that is representative over a maximum number of intervals. These strategies were developed from the analysis of results after application of the K-S test on the distributions tested. This approach will contribute. to the reliability analysis of complex systems. As a result, in improving the models used to analyze complex systems behavioral analogies such as Petri nets or Markov chains.

In recent years, there has been growing interest in leveraging the Internet of Things (IoT) and Artificial Intelligence (AI) technologies for agriculture. A significant challenge for developers in this field is creating applications that provide precise data about plants, facilitating the smart automation of plant management. This paper presents Plantonome, an open-source application developed using the Flutter software development kit (SDK) and the Dart programming language. Designed to integrate with IoT devices, Plantonome quickly and accurately identifies ornamental plant genera or species using the Plant.id API for plant image analysis. The application also utilizes a NoSQL database for storing user data and plant preferences, and it includes a dataset of ornamental plants with details such as name, brightness, temperature, and humidity requirements. The development approach outlined in this paper accelerates the creation process and results in a high-performing application with a flexible user interface and smooth user experience. The application, tested on Android 5.0 (API level 21) or higher, achieved an accuracy of 94.64% for plant identification and received highly positive feedback regarding its functionality, usability, and efficiency. This work offers significant benefits to researchers and startups aiming to develop cross-platform applications that can automate various agricultural tasks, contributing to advancements in smart agriculture.

In a grid-connected photovoltaic (PV) power generation system, sudden changes in the output power of PV panels, caused by fluctuations in solar irradiation or shading, as well as real-time variations in system load, can lead to significant voltage fluctuations in the DC bus. If not properly managed, these voltage fluctuations can compromise the stability and efficiency of the entire system. To address this challenge and improve the performance of DC bus voltage regulation, this paper proposes a control method based on a fuzzy logic controller (FLC). The proposed method primarily relies on two key correctors: the first is an energy corrector, which regulates the energy stored in the DC bus capacitor, and the second is an FLC, which controls the current to maintain voltage stability. This dual-corrector system is designed to enhance the system’s response to power fluctuations and maintain a stable DC bus voltage. The performance of this FLC-based control approach is compared to the traditional double closed-loop PI control method. Simulation results, carried out in the Matlab/Simulink environment, demonstrate that the proposed FLC method outperforms the traditional PI control in terms of both system stability and dynamic response. This improvement is especially evident in handling sudden disturbances and maintaining consistent voltage levels.

As wind farms grow in size, the detrimental effects of wake interactions on energy yields become increasingly pronounced. This leads to the new challenge essential to the production of renewable energy. The two main objectives of offshore wind farm planning are to maximize annual energy production and minimize wake loss. To accomplish the twin goals of reducing wake impacts and maximizing energy production, this research tackles a novel method to investigate trade-offs between competing goals using multi-objective optimization algorithms. We introduce this problem with a sophisticated wake named the Bastankhah-Porté-Agel (BPA) model. To tackle this problem, the research has developed a multi-objective optimization framework in Python that shows the Pareto front, which illustrates the trade-off between wake effects and (AEP) by using a particle swarming optimization (PSO) algorithm. The proposed multi-objective optimization framework offers a disciplined way to balance energy production and wake loss, which advances the offshore wind farm design. The results indicate that the proposed method is robust in finding the optimized layout for improving sustainability and offshore wind energy efficiency. Before carrying out this process, the proposed tool has been validated using data obtained by a wind farm in Georgia.

This study explores the wind energy potential for a wind-powered desalination project in Dakhla, Morocco, by analyzing two models: the Weibull distribution and the WAsP model. Over two years of wind data were processed to estimate energy production, with the Weibull model offering a baseline and the WAsP model refining predictions with site-specific variables. The project, a Public-Private Partnership (PPP), serves 219 agricultural estates over 5200 hectares and ensures potable water for Dakhla’s residents. The PPP framework promotes financial sustainability, with funding shared between public and private stakeholders, ensuring long-term viability in addressing the region’s water-energy needs.

This paper investigates a ball-screw energy harvester shock absorber (BS-EHSA) equipped with a DC motor/generator and driven by an external sinusoidal force. A comprehensive parametric study examines the effect on the power delivered to the load of varying the sprung mass, external electrical resistance, and spring constant. A simulation study is also performed to demonstrate how the resonance frequency of the system impacts energy recovery. Numerical simulations are carried out in the MATLAB environment, providing insights into optimizing system parameters for maximum power efficiency.

Drought is a significant environmental stressor that induces changes in the physiological, morphological, biochemical, and molecular traits of plants, ultimately resulting in reduced plant growth and crop productivity. Seaweed extracts are thought to be effective in mitigating the effects of drought stress on plants. In this study, we investigated the impact of crude extract (CE), and polysaccharides (PS) derived from the brown macroalgae Fucus spiralis (Heterokontophyta, Phaeophyceae) applied at 5% (v/v) and 0.1% (w/v) respectively on radish plants Raphanus sativus L. subjected to varying levels of drought stress, specifically 80% of field capacity (FC) for no stress, 60% FC for moderate stress, and 40% FC for severe stress. Our examination of growth parameters, along with physiological and biochemical characteristics, revealed that both CE and PS increased the fresh weight over the control by 47.43% and 64% at 40% FC and 12.5% and 38% at 60% FC respectively. Under stress (40% FC), the application of CE and PS decreased proline content of radish leaves by 23.45% and 6.46% respectively in comparison with the control. Furthermore, PS treatment caused an increase of the alkaline phosphatase and urease activity in the soil by 182.5% and 34.6% respectively. CE and PS treatments led to decreased sugar content and total phenolics levels. Notably, lipid peroxidation was reduced in stressed plants treated with both CE and PS, with PS treatment yielding lower concentrations (3.75 nmol MDA.g− 1 FW at 40% FC). Overall, F. spiralis extracts interacted through several mechanisms using various compounds to mitigate the negative effects of drought stress on radish plants. These results demonstrate that seaweed extracts could be adopted in integrated production systems to boost food productivity under harsh climatic conditions.

Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96 years. The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits that can be used to screen for misreporting by participants in dietary studies. We applied the equation to two large datasets (National Diet and Nutrition Survey and National Health and Nutrition Examination Survey) and found that the level of misreporting was >50%. The macronutrient composition from dietary reports in these studies was systematically biased as the level of misreporting increased, leading to potentially spurious associations between diet components and body mass index.

We explore the quantum information resources within bipartite pure and mixed states of the quantum spin-1 Heisenberg dimer system, considering some interesting factors such as the $l_{1}$-norm of quantum coherence, relative coherence, negativity, and steering, influenced by the magnetic field and uniaxial single-ion anisotropy. Through a thorough investigation, we derive the system’s density operator at thermal equilibrium and establish a mathematical framework for analyzing quantum resource metrics. Our results unveil the system’s behavior at absolute zero temperature. We further observe temperature’s role in transitioning the system toward classical states, impacting coherence, entanglement, and steering differently. Notably, we find that increasing the exchange anisotropy parameter can reinforce quantum correlations, while adjusting the uniaxial single-ion anisotropy influences the system’s quantumness, particularly when it is positive. Some recommendations to maximize quantum coherence, entanglement, and steering involve temperature reduction, increasing the exchange anisotropy parameter and carefully managing the magnetic field and uniaxial single-ion anisotropy parameter, highlighting the intricate interplay between these factors in maintaining the system’s quantum properties.

The corrosion inhibition performance of a novel molecule, polymethylene dianiline (PMDA), on E24 carbon steel was investigated in sulfuric acid medium (0.5M H2SO4) using different experimental techniques. Weight loss and electrochemical measurements both demonstrated that PMDA significantly reduced corrosion rates, with inhibition efficiency increasing as the inhibitor concentration rose. Meanwhile, the protection efficiency was found to decrease at higher temperatures. Potentiodynamic polarization studies indicated that PMDA functioned as mixed type inhibitor, affecting both anodic and cathodic reactions. The adsorption pattern of PMDA on E24 carbon steel was consistent with the Langmuir isotherm model, suggesting monolayer adsorption. Surface analysis via scanning electron microscope (SEM) coupled with elemental analysis, conducted in the absence and presence of the optimal PMDA concentration, revealed the creation of a protective layer on the E24 metal in the sulfuric acid environment. Additionally, these experimental results were further validated by computational chemistry methods, including density functional theory (DFT), Monte Carlo (MC), and molecular dynamics (MD) simulations.

Nonlocality and quantum metrology are two fundamental ingredients in quantum physics. Measuring and characterizing nonlocal quantum correlations existing in any quantum state for the purposes of quantum information processing applications should receive therefore much interest. This work introduces a metrological-based approach for quantifying quantum nonlocality as a form of correlation. The scheme can be executed by exploiting the quantum estimation theory to define metrology-induced quantum nonlocality (MIQN) as the maximal increment of quantum Fisher information. MIQN can be done by acting a local commuting observable on one of the parts of a quantum bipartite probe state to estimate a parameter encoded by a local rotation. For the $2\times d$-dimensional mixed states, the closed form of MIQN is evaluated. Furthermore, we proved that the proposed quantifier fulfills all the criteria of a well-defined quantum correlations measure.

Small-scale biogas systems hold promise as reliable renewable energy sources in developing nations; however, adequate and consistent supply of feedstock remains a challenge. Agricultural residue, due to their lack of competition with food crops for resources, is touted as a dependable feedstock choice. This article therefore examines agricultural residues as potential biogas plant feedstocks in the Fès-Meknès region of Morocco, using a structured farm survey to evaluate livestock types, crop varieties, and residue utilization. Additionally, the study explores the challenges and drivers influencing biogas technology adoption in Morocco. Findings indicate a predominance of small-scale farms with livestock (averaging 11 cattle, 45 sheep, and 20 chicken) and mainly subsistence crop production, making these farms suitable candidates for small-scale biogas plants. Key barriers to adoption include a lack of awareness about the technology, along with technical and financial constraints. However, raising awareness, establishing demonstration plants, and offering financial and non-financial incentives are identified as potential drivers of adoption. This research provides a foundation for implementing biogas technologies in the case study area and other developing nations, guiding researchers and governmental and non-governmental organizations in disseminating small-scale biogas systems as a reliable energy source and a method for converting agricultural residues into sustainable energy (biogas) and fertilizer. Graphical Abstract