ABSTRACT Hydraulic fracturing represents an effective stimulation approach for exploiting low‐permeability oil and gas reservoirs, which is essential for boosting well productivity. However, conventional constant‐rate hydraulic fracturing (CRHF) is plagued by issues such as high operation pressure, difficulty in forming complex fractures, and induced seismicity. Recently, variable‐rate hydraulic fracturing (VRHF) has attracted attention, with preliminary laboratory experiments and field tests demonstrating its potential to mitigate these challenges. Nevertheless, the stimulation mechanism of VRHF remains unclear. Compared with CRHF, VRHF introduces a series of dynamic issues, rendering traditional static fracturing theories inadequate. This paper conducts a systematic analysis of the following aspects during the operation of VRHF, including the research on fluid pressure propagation in the tubing, reservoir stress distribution, reservoir fatigue damage, fracture initiation and propagation, and induced seismicity. The results indicate that VRHF can induce unsteady flow in the tubing, thereby generating fluctuating fluid pressure near the bottom‐hole reservoir. Dynamic stress is generated in the reservoir under the action of fluctuating fluid pressure and propagates as stress waves. Furthermore, fluctuating fluid pressure can induce fatigue damage in the reservoir. These effects are beneficial for reducing breakdown pressure, modifying fracture propagation behavior, and mitigating the magnitude of induced seismicity. For future research, it is imperative to develop a coupled simulation model that integrates tubing fluid flow and reservoir fracture propagation, together with seismic monitoring during fracturing operations. It is highly significant for field operations to optimize the operational parameters using the aforementioned model, with the objective of forming a complex fracture network under the constraints of maximum operating pressure and induced seismicity magnitude.
ABSTRACT Aiming at the problems of low top‐coal recovery rate and excessive residual triangular coal on the floor side in horizontal sublevel top‐coal caving mining of steeply inclined extra‐thick coal seams, this study takes the 5521‐25 working face of the second coal seam in Yaojie 3 Mine as the engineering background. Using PFC2D numerical simulation software, and taking top‐coal recovery rate, thickened top‐coal recovery rate, and top‐coal recovery amount as evaluation indicators, the top‐coal drawing laws under three different sublevel heights (15, 20, and 25 m) and two top‐coal drawing methods (single‐round interval drawing and multi‐round interval drawing) are comparatively analyzed. The influences of sublevel height and drawing method on top‐coal recovery performance are obtained. The results show that as the sublevel height increases, the top‐coal recovery amount gradually increases, but the top‐coal recovery rate and the thickened top‐coal recovery rate show a decreasing trend, and the decrease amplitude of the top‐coal recovery rate gradually enlarges. Among the three investigated cases, the 20 m sublevel height showed the best overall performance. Under the sublevel height of 20 m, compared with single‐round interval drawing, multi‐round interval drawing can improve the development state of the coal‐gangue boundary line, reduce the residual triangular coal on the floor side, increase the top‐coal recovery amount, and increase the top‐coal recovery rate from 79.9% to 85.8%, indicating that multi‐round interval drawing is more conducive to improving top‐coal recovery performance. This study can provide a reference for parameter design and process optimization of horizontal sublevel top‐coal caving mining in steeply inclined extra‐thick coal seams.
ABSTRACT Class Ⅰ gas hydrate deposits are regarded as the most promising type for commercial development due to the underlying free gas layer, and they offer a relatively cheap and safe method for developing gas hydrates using a vertical well and depressurization. Therefore, this paper studies the production characteristics of Class Ⅰ gas hydrate deposits developed by vertical wells and depressurization, to provide a basis for productivity prediction and stimulation. On the basis of the gas production rate, a method is proposed to stage the gas production process. And then a new index is built to evaluate the gas hydrate contribution to gas production in different stages and analyze the influencing factors. The research shows that the gas production rate and decomposition gas decrease with time. The contribution of gas hydrate decomposition to gas production cannot be ignored. The gas and water saturations in the hydrate layer increase over time, while the hydrate saturation decreases. Deposit energy is rapidly consumed during the early production stage, which is one reason for the low gas production rate in the later stage. The production process can be divided into two stages according to the relationship between the gas production rate and the decomposition gas rate. The ratio of cumulative decomposition gas to cumulative gas production can be used to evaluate the contribution of the gas hydrate layer. The contribution ratio of gas hydrate is over 1 in Stage Two because the decomposition gas not only directly contributes to gas production but also sustains it by replenishing the deposit energy. High gas saturation reduces cumulative gas production due to the low effective permeability of the hydrate layer; therefore, heat or fracture stimulation can be used to enhance productivity. The gas production rate and cumulative gas production increase with the gas layer thickness. An increase in deposit energy can enhance productivity by accelerating hydrate decomposition in Stage One. Lower production pressure can promote gas production. To release productivity, production pressure can be kept as low as possible in Stage One, and methods for supplementing deposit energy can be considered in Stage Two.
ABSTRACT The ever‐growing demand for recyclable and clean energy stimulates research on batteries. Sodium‐ion batteries are promising candidates for their rocking‐chair mechanism and high reserves. NaMnO 2 has been drawing attention owing to its environmentally friendly ingredients and outstanding cycling performance. However, insufficient sodium storage sites and unsatisfying specific capacity hinder its practical application and commercialization process. The common methods to solve these problems include doping and coating, but these lead to price increase and capacity loss. To address this, NaMn 0.99 Mo 0.01 O 2 cathode materials are synthesized with a simple solution‐gel method through doping a trace amount of Mo. Based on sufficient experimental evidence, Mo‐doping significantly improves SIB's reversible specific capacity, demonstrating great potential in promoting energy intensity. For instance, NaMn 0.99 Mo 0.01 O 2 shows an average working voltage of 3.0 V and an initial capacity of 183.7 mAh g −1 at 0.2 C. Especially, it has a remarkable specific capacity of 138.1 mAh g −1 and distinct cycling stability of 84.7% capacity retention over 100 cycles even at 1 C. This work provides a perspective on Mo doping and a promising choice for constructing high‐performance commercial batteries.
ABSTRACT The widespread adoption of photovoltaic (PV) systems for sustainable power generation underscores the necessity for high‐performance Maximum Power Point Tracking (MPPT) control strategies. This research proposes a structured Tri‐Meta Hybrid (TMH) MPPT controller for standalone PV systems by coordinating three established metaheuristic algorithms: Dandelion Optimizer Algorithm (DOA), Crow Search Algorithm (CSA), and Whale Optimization Algorithm (WOA). The contribution of the proposed controller lies in its sequential exploration–refinement–exploitation structure, in which DOA supports global duty‐cycle exploration, CSA refines promising candidate regions via memory‐guided search, and WOA performs local exploitation near the maximum power point. The primary objective of this study is to evaluate the effectiveness of the individual, Dual Hybrid (DH), and TMH controllers in improving the performance of standalone PV systems under constant, varying, and partial shading conditions. The proposed controllers were evaluated and compared through a multiparametric performance analysis in terms of power tracking, efficiency, convergence speed, and computational complexity using MATLAB/Simulink. Under constant irradiance and temperature of 1000 W/m 2 and 25°C, the DOA–CSA–WOA controller achieved the highest output power of 10,141 W, corresponding to a tracking efficiency of 99.19%, surpassing individual and DH algorithms with peak efficiencies of 97.11% and 98.49%. While considering DOA's average output power (6280.44 W) as a reference for varying irradiance and temperature conditions, CSA yielded 6260.01 W (–0.277%) and WOA 6238.25 W (–0.55%). DH's demonstrated improved gains, that is, CSA–DOA produced 6450.38 W (+2.31%), WOA–CSA 6418.25 W (+1.89%), and WOA–DOA 6389.75 W (+1.55%). The TMH, DOA–CSA–WOA achieved the highest output power of 6549.25 W (+3.73%). Whereas taking CSA's average output power of 7512.5 W as the reference under partial shading conditions, the controllers achieved: DOA –0.7%, WOA –0.42%, CSA–DOA +2.07%, WOA–CSA +3.28%, WOA–DOA +6.59%, and DOA–CSA–WOA +8.86%. The results confirm that hybridization improves MPPT performance by combining the complementary strengths of the selected algorithms. Among all tested controllers, the proposed DOA–CSA–WOA TMH controller achieved the best overall performance under constant, varying, and partial shading conditions.
ABSTRACT In the field of mining, conventional methods that rely on protective coal pillars result in significant wastage of coal resources. To achieve the dual objectives of resource recovery and roadway stability control, this study utilizes theoretical analysis, numerical simulation, and field experiments to investigate the technology of gob‐side entry protection through blasting‐induced roof cutting for pressure relief under gently inclined coal seam conditions with hard roofs. The research demonstrates that implementing high‐level roof cutting can actively guide roof fracturing outside the coal pillar, thereby significantly reducing the peak lateral abutment pressure and shifting it deeper into the coal mass. A calculation formula for inter‐hole microsecond delay timing, based on the principle of vibration wave interference cancellation, and a method for determining the optimal spacing between blast holes and guide holes, grounded in stress wave superposition theory, were proposed, forming a precise blasting technique for controlled fracture formation. After field application, the deformation of the roadway surrounding rock was controlled within 35 mm, yielding favorable application results. The key stratum‐targeted roof cutting and refined blasting control technology proposed in this study hold theoretical significance and technical reference value for improving coal recovery rates and enhancing roadway stability control.
ABSTRACT A piston bowl's design and spray angle improve air–fuel mixing, resulting in more complete combustion and higher efficiency. Ansys Forte 2023 R1 a CFD software was used to analyze the effects of five combustion chamber geometries of cylinder, modified re‐entrant, re‐entrant, double‐stepped, and stepped on engine performance, combustion, and emission characteristics at spray angles of 1000°, 1050°, 1100°, 1150°, and 1200°, respectively. This study looks into the effects of piston bowl shape and spray angle on engine performance and emission characteristics. The numerical investigation is carried out with a load of 0.44 MPa and a start of injection at 7° bTDC. The double stepped piston bowl with spray angles of 110°–115° has the maximum thermal efficiency and the lowest ISFC, while reducing VOC, UHC, soot, and CO emissions. The combustion and performance parameters for piston bowl geometries are optimized at spray angles of 110° and 115°. At these spray angles, improved fuel–air interaction leads to higher peak cylinder pressure, peak cylinder temperature, thermal efficiency, and ISFC consumption while producing reduced emissions. The results show that spray angle optimization is crucial, with 110°–115° emerging as the optimal spray angle range for piston bowl selection and combustion optimization. Among the bowl geometries, the double stepped demonstrate the most balanced performance‐emission trade‐off at spray angles of 110° and 115° compared to stepped, modified re‐entrant, re‐entrant, and cylindrical shapes.
ABSTRACT Carbon capture, utilization, and storage (CCUS) is essential for decarbonizing coal‐dominated power systems, yet its large‐scale deployment in China is constrained by high costs, uneven storage resources, and limited transport infrastructure. This study develops a plant‐level, region‐resolved multi‐objective optimization framework for CCUS deployment in China's coal‐fired power sector, integrating plant‐level capture choices, CO 2 transport, utilization, geological storage, policy incentives, and energy‐security considerations. Using data from 808 coal‐fired power plants, the Non‐dominated Sorting Genetic Algorithm II (NSGA‐II) is applied to identify Pareto‐optimal pathways that balance total system cost, net CO 2 mitigation, and a composite energy security index. Results show that the Pareto frontier spans 378–693 billion CNY in total system costs and 1.27–1.81 Gt CO 2 yr −1 in annual net mitigation. Policy incentives exhibit a saturation effect: carbon prices above 50 CNY t −1 CO 2 and subsidies exceeding 30% yield diminishing marginal returns once transport and storage constraints become binding. Diversified capture portfolios increase the energy security index by approximately 25% at only 3%–5% additional cost, while inter‐regional source–sink coordination reduces total system costs by 15%–22%. These findings support coordinated infrastructure planning and balanced policy design for China's coal‐power CCUS deployment.
ABSTRACT The gravitational water vortex power plant (GWVPP) has emerged as a promising renewable energy technology, characterized by low investment costs, a simple design, and minimal maintenance requirements. However, its performance has been constrained by suboptimal parameters, particularly in critical components such as the runner. This paper presents a comprehensive exploration of the optimization process for GWVPP runners, using a comparative analysis driven by a genetic algorithm (GA) prediction that integrates experimental data and computational fluid dynamics (CFD) simulations. The investigation focuses on three key performance parameters: power, torque, and efficiency, assessed over a rotational speed range of 1.91–3.26 rad/s. The findings show a consistent trend across all techniques, with performance improving with rotational speed up to an optimal range of around 2.6–2.7 rad/s, after which it declines. There is strong agreement between GA predictions and CFD simulations, demonstrating the GA's ability to capture and optimize system behavior. Experimental results show a similar pattern, but with lower efficiency, particularly at higher rotational speeds, due to practical losses, flow disruptions, and measurement uncertainties that are not fully captured in the numerical models. Sensitivity analysis emphasizes the impact of rotational speed, hub‐blade angle, and blade number on system performance. Overall, the findings demonstrate that GA is a reliable and effective approach for improving GWVPP performance, with results consistent with CFD simulations and supported by experimental data. The study emphasizes the viability of GWVPP systems as a long‐term energy option for low‐head applications.
ABSTRACT South Africa faces growing municipal solid waste (MSW) accumulation and energy insecurity due to fossil fuel dependence. Torrefaction improves MSW for waste‐to‐energy use, while conventional pyrolysis produces low‐quality oxygen‐rich bio‐oil. Catalytic pyrolysis with locally available iron‐based catalysts offers a cost‐effective method to upgrade bio‐oil and support sustainable energy production. This study investigates the catalytic pyrolysis of the torrefied organic fraction of municipal solid waste at 550°C–700°C and atmospheric pressure (1 atm) using a locally synthesized Fe 2 O 3 catalyst at a 1:10 catalyst‐to‐biomass ratio. Feedstock and catalyst characteristics were evaluated using thermogravimetric analysis, Fourier transform infrared spectroscopy, and Brunauer–Emmett–Teller surface analysis, while bio‐oil composition was analyzed using gas chromatography–mass spectrometry (GC–MS) and elemental analysis. The torrefied feedstock, with 48.6 wt.% volatile matter and 57.8 wt.% carbon, produced 38.6 wt.% bio‐oil non‐catalytically. Using an Fe 2 O 3 catalyst increased the yield to 47.5 wt.% at 600°C, reduced oxygenates, and improved fuel quality, achieving a higher heating value of 33.8 MJ kg −1 . GC–MS analysis identified phenolic and heterocyclic compounds, indicating effective catalytic cracking, deoxygenation, and hydrocarbon enrichment during vapor upgrading. These results demonstrate that locally sourced iron‐based catalysts effectively upgrade torrefied MSW pyrolysis vapors, producing higher‐quality bio‐oil and improving overall conversion efficiency. The proposed catalytic waste‐to‐energy approach provides a scalable and economically viable strategy for converting municipal waste into renewable fuels, strengthening energy security and sustainable waste valorization in South Africa, and supporting SDG 7, SDG 12, and SDG 13.
ABSTRACT Accurate prediction of solar panel energy output is vital for managing power systems effectively and maintaining a stable electrical grid. This is especially important in regions that rely heavily on renewable sources. This research provides a direct comparison of five machine learning (ML) algorithms, that is, Decision Tree Regression (DTR), Multiple Linear Regression (MLR), Random Forest (RF), k‐Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost) for forecasting PV power output in Jazan, Saudi Arabia. Jazan has a tropical desert climate with high humidity, seasonal wind speed variation, and coastal proximity, unlike the arid inland regions typically studied in the KSA. This makes it an ideal testbed for evaluating model robustness under varied meteorological parameters. The models were trained on a 6‐year (2017–2022) dataset comprising hourly measurements of four different meteorological parameters and evaluated using coefficient of determination ( R 2 ), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Wilcoxon signed‐rank test. Among the models used, XGBoost achieved the highest accuracy ( R 2 = 0.93, MAE = 7.3, RMSE = 20.38), outperforming all others. The results highlight the effectiveness of ensemble methods in PV power forecasting and offer valuable guidance for improving forecast accuracy in regions with comparable climates.
ABSTRACT The transition toward cyber‐physical smart grids has improved operational flexibility and enabled the wide integration of distributed generation, while also introducing new cyber‐induced vulnerabilities. Increasing reliance on communication networks, particularly within protection schemes, creates additional risks of coordinated cyber‐physical attacks that may compromise relay decisions and system stability. This paper provides a focused technical assessment of these risks and introduces an evaluation framework to analyse overcurrent protection under cyber interference. The approach employs an attack‐tree structure to characterise cyber‐physical paths and their propagation to the protection layer. Communication‐side disturbances are modelled using OMNeT, while ETAP is used to represent the physical network and relay coordination environment, allowing co‐simulation of cyber events and electrical responses. By examining the interaction between the cyber and physical domains, the framework shows how specific attack configurations affect fault detection, relay timing, and coordination margins. The results highlight key system weaknesses, identify conditions that could cause misoperations, and point to areas where additional resilience measures are required. This work introduces a step‐by‐step evaluation process using the IEEE 9‐bus network under different cyberattack scenarios, including blocking relay commands, changing relay configurations, and injecting false data. The results present the effects of these attacks on the power protection system's sensitivity and selectivity.
This study develops a framework for predicting building energy consumption using Graph Neural Networks (GNNs). A two-story reference building with seven thermal zones was represented as a graph, where each zone was a node and thermal or spatial interactions were edges. EnergyPlus simulations, driven by real hourly weather data from Tehran, London, and New York, generated the training dataset. The GNN was implemented in PyTorch Geometric and trained on these outputs. The proposed model predicts cooling and heating loads with high accuracy. It achieved mean absolute errors (MAE) of 0.8-1.0 kWh, a 15%-40% reduction compared with baseline models: Random Forest and XGBoost. Performance was stable across climates: the lowest errors appeared in London's temperate conditions, while slightly higher deviations occurred in the more extreme climates of Tehran and New York. At the zone level, accuracy was strongest in low-load areas, such as corridors and stairwells, while offices with greater internal gains showed modest deviations during peak usage. Residual analysis indicated that most predictions deviated within +/- 1.5 kWh, with only rare larger errors under severe weather. Sensitivity tests confirmed that outdoor temperature, solar radiation, and internal loads strongly affected predictions; for example, a 1 degrees C rise in outdoor temperature increased cooling demand by about 5%-8%. Overall, this study demonstrates that GNN-based models are fast, accurate, and scalable surrogates for building energy simulation. By capturing inter-zone thermal interactions, they bridge physics-based and data-driven approaches. Future work should emphasize real building data, HVAC system integration, and improved interpretability for reliable, climate-resilient energy management.
The electrical power system must be trustworthy and secure enough to provide a continuous supply to meet the power demand. With the complexity of the electricity system growing, the likelihood of blackouts and outages is rising. Therefore, an effective control system is required to increase the power system's safety, effectiveness, and reliability. Numerous new possibilities have been made possible by recent advancements in measurement, communications, and analytical technologies. Particularly, wide area measurement systems (WAMS) have gained attention for addressing anomaly operations. The fundamental component of WAMS is a Phasor Measurement Unit (PMU), which offers a dynamic view of the power system since GPS gives a timestamp and synchronized phasor. Combining these synchronized measurements in a central protection system (CPS), a wide area control, protection, and optimization platform is created by means of optical fiber communication. Since PMU devices are more efficient than standard SCADA (Supervisory Control and Data Acquisition) systems at capturing the rapid dynamics of the power system with high-accuracy measurement, this technology is becoming more and more popular in the utility industry. The suggested concept is designed to protect big power transmission networks with PMUs while overcoming conventional limitations. The proposed technique can identify faults within a few milliseconds, which is very rapid, and this technology has been tested in the Bangladesh Power System of the Chattogram region. The deployment of PMUs can produce massive quantities of data, and by analyzing large dataset, machine learning (ML) techniques can be very useful in preventing disasters caused by unexpected outages by promptly identifying them. This study has provided the classification methods for K Nearest Neighbors, the Logistic Regression method and the Support Vector Classifier. These results indicate that they are quite accurate at identifying anomalies in the data provided by PMUs. To increase the accuracy of WAMS with ML, a rectangular window feature is integrated in the generated data of PMUs. The window feature with ML shows a significant improvement in WAMS. A Unified Real-time Dynamic State Measurements (URTDSM) system with PMU and Phasor Data Concentrator (PDC) deployment plan has also been proposed for the Bangladeshi power system (BPS) using Wide Area Monitoring, Protection and Control (WAMPAC), which appears to be the most advanced technology for Bangladesh.
Driven by the global shift away from fossil fuels, solar and wind resources are increasingly important for sustainable power planning, especially in data-scarce regions. This study proposes a hybrid simulation-machine learning framework to estimate renewable energy yields across Iraq using minimal geographic inputs. Hourly meteorological variables for 2015-2024 (solar radiation, wind speed, temperature, and humidity) were retrieved from the NASA POWER database for ten representative Iraqi cities at a gridded resolution of approximately 0.1 degrees-0.2 degrees. These data were converted to EPW format and used in EnergyPlus to simulate electricity generation from a standardized PV system (20% efficiency, 1 m(2) area) at three fixed tilt angles (0 degrees, 30 degrees, 60 degrees) and from a 1 kW wind turbine. The resulting EnergyPlus outputs were then used to train two surrogate predictors: a random forest (RF) model for solar yield and a Feedforward Neural Network (FNN) for wind yield. Using an 80/20 split and cross-validation within the simulated dataset, the RF reproduced EnergyPlus-simulated solar energy with strong agreement within the modeled dataset (R-2 approximate to 0.98, MSE approximate to 1.45), while the FNN showed strong agreement with the simulated wind-energy outputs (R-2 approximate to 0.97, MSE approximate to 2.36). Feature analysis indicated that PV tilt and seasonal cycles dominate solar yield variability, whereas elevation and seasonality are the primary drivers for wind yield. For practical decision support, the trained models were deployed in a Streamlit web interface that returns monthly and annual kWh estimates from latitude/longitude, elevation, and configuration inputs. Because validation is performed against EnergyPlus simulation outputs (simulation-derived ground truth), reported accuracy should be interpreted as simulation-level fidelity rather than verified predictive performance against measured field generation.
With the depletion of conventional energy resources, the development and utilization of solar energy as a renewable resource have become increasingly urgent. Solar air collectors, which capture and convert solar radiation into thermal energy, have attracted considerable attention due to their simple structure and low maintenance cost. In this study, COMSOL multiphysics was employed to conduct a numerical analysis of the effects of structural parameters (air gap depth) and environmental parameters (solar radiation, ambient temperature, and air mass flow rate) on the performance of a solar air collector. The results demonstrate good agreement between the experimental and simulated data, with the error in instantaneous efficiency remaining below 6%. Within the investigated range, increasing the air gap depth and mass flow rate significantly enhances the system's useful energy, temperature difference, and outlet air temperature. The optimal overall thermal performance is achieved at an air gap depth of 8.5 cm and a mass flow rate of 0.01318 kg/s. Air gap depth is identified as the dominant factor affecting system performance, while mass flow rate plays a reinforcing role, particularly at larger gap depths. These findings provide useful references for future research and optimization of solar air collectors.
Urban natural gas is transported through buried pipelines that are exposed to water, chemical and electrochemical corrosion, making the pipe walls vulnerable to perforation and subsequent gas leakage. The deformation behavior of soil during leakage limits the accurate prediction of methane diffusion by numerical simulation, and is influenced by diffusion coefficient, leakage velocity, and soil characteristics. Therefore, a Darcy-Brinkman-Stokes-Diffusion model was developed using OpenFOAM and validated to describe the evolution of soil fracture morphology and gas diffusion behavior during gas leakage. Simulation and experimental results show that three main cracks with small cavities inside were formed when the injection velocity exceeded approximately 0.2 m/s. Under low injection conditions, soil deformation was negligible, but the velocity exhibited a significant non-uniform distribution. As leakage velocity increases, soil fractures widen, although crack orientation remains largely unchanged. Meanwhile, methane transport shifts from uniform diffusion to crack-guided directional flow. When the diffusion coefficient exceeds approximately 2 & times; 10-4 m2/s, the gas transitions from diffusing primarily along the sides of the crack to diffusing uniformly throughout it. In soils containing natural cracks, gas leakage induces new fractures that intersect existing ones, reshaping the crack network and enhancing methane migration. These results provide a novel predictive numerical model and new insights into natural gas leakage in soil.
ABSTRACT The Republic of Kazakhstan is one of the resource‐rich countries and is proactively embracing the sustainable development goals (SDGs) via different strategies. The country has also pledged to achieve significant reduction in the emissions of greenhouse gases by 2030 relative to 1990 level. One of the key approaches to achieving these goals in this country is the development of renewable energies. The present study provides a comprehensive review of existing reports, data and research on renewable energy in this country by analyzing sources obtained from scientific databases, as well as reports and data from international institutions. The aim is to assess the potential and current status of various energy sources and to identify related opportunities and challenges. Among different sources, wind energy has the most significant potential to be used for power generation. This evidenced by the fact that half of the country's territory has an average wind speed in the range of 4–6 m/s. Solar energy also has acceptable potential for power generation, especially in the southern and central regions of the country with an annual potential in the range of 1300–1800 kWh/m 2 . Additionally, other sources of energy possess acceptable potential and could serve both power generation and other purposes, such as heating. Challenges related to the development of these systems can include poor infrastructures, the need for investment and technical issues like grid balance. Overall, renewable energies offer the country promising alternatives to fossil fuels, with a variety of advantages including job creation, environmental protection, enhanced energy security.
ABSTRACT Microbial fuel cells (MFCs) offer a sustainable platform for concurrent wastewater treatment and bioenergy recovery, where anode material selection critically governs performance through its impact on biofilm formation and extracellular electron transfer. This review systematically evaluates recent progress in three primary categories of anode materials: carbon‐based (e.g., carbon paper, graphene, carbon nanotubes), biomass‐derived, and metal‐based (e.g., stainless steel, precious metals) electrodes. We critically analyze their physicochemical properties including biocompatibility, conductivity, surface area, and long‐term stability and assess advanced surface modification techniques such as thermal/chemical treatment, doping, and the application of nanomaterial or conductive polymer coatings. While these strategies significantly enhance power density and coulombic efficiency, challenges persist in scalability, cost, and durability under real wastewater conditions. The review identifies the development of hybrid and composite materials as a key pathway toward overcoming these limitations. By synthesizing current knowledge and highlighting research gaps, this work aims to inform the rational design of high‐performance, cost‐effective anodes for next‐generation MFCs with improved commercial viability.
ABSTRACT A parabolic trough collector (PTC) is a system to harness solar energy and hconvert it into usable thermal energy. However, one operational limitation of the PTC is that it requires continuous solar tracking to capture the maximum amount of energy. But adding a solar tracking mechanism complicates the parabolic collector system, increases initial and maintenance costs, and requires substantial energy to operate. This study investigates the performance of a nontracking PTC and proposes a compact nontracking system design using multiple parabolic trough reflectors. It is designed by positioning three distinct parabolic trough reflectors so that the reflected rays are focused on a stationary absorber tube. Experiments were conducted over several days from November 2023 to May 2024, using water as the working fluid flowing through the absorber tube. Throughout the experimental period, nearly constant mass flow rates of 0.3, 0.4, and 0.7 kg/min of working fluid, a slightly variable inlet water temperature, and variable irradiance conditions were maintained. The proposed PTC system could provide a maximum outlet water temperature of 63.3°C at an inlet temperature of 53.2°C when direct solar radiation was 770.25 W/m 2 , with the flow rate maintained at 0.3 kg/min. The system's maximum instantaneous efficiency was 51.78%, which varied with weather conditions and the working fluid's flow rate. Due to the absence of any tracking mechanisms, a considerable performance drop was observed during the early and late phases of the collection period. However, the use of multiple individual reflectors significantly mitigated the loss. This study focuses on simplified design, minimized heat loss, and improved optical performance while maintaining efficiency without the need for any tracking system in PTC.
