This study assesses the reliability of a proposed 50 MW wind farm in Zuwara, Libya, under variable wind conditions. A 10-year wind dataset comprising 3,652 daily observations was obtained from the NASA POWER database to characterize the site wind resource. Several probability distributions were evaluated, and the Johnson SB distribution was selected based on goodness-of-fit using the Kolmogorov–Smirnov (KS) test. The resulting wind-speed model was integrated with the power characteristics of a 50-turbine wind farm, each rated at 1 MW. Monte Carlo simulation was applied to represent wind-generation variability and evaluate its effect on generation adequacy under different demand levels. Using the Resistance–Load (R–L) approach, loss-of-load conditions were identified when available wind generation was insufficient to meet the considered demand. The results show that the estimated failure probability was approximately 88.39% for a 32-MW demand and 92.99% for a 38-MW demand. Increasing the number of simulation trials reduced the statistical error and improved the stability of the estimates. The findings demonstrate the strong influence of wind variability and demand level on the reliability contribution of the proposed wind farm and highlight the need for complementary resources to improve supply adequacy.

Peak electricity demand presents a continuing challenge to power-system operation in Libya, particularly during periods of high ambient temperature. This study examines a proposed 60-MW above-ground Compressed Air Energy Storage (CAES) system intended to provide short-duration peak-power support for the GT12 gas turbine at the West Tripoli Gas Power Plant. The proposed discharge duration is 15 minutes. The assessment draws on plant operating data collected on June 2–3, 2024, and on a validated thermodynamic model used to evaluate compressor and turbine performance. The analysis considers mass-flow rate, heat input, net power output, and the sizing of the compressed-air storage system. The results demonstrate that the proposed CAES system increases the available peak power from approximately 100 MW to 135 MW by completely offsetting the compressor work. Despite an increase in fuel consumption required to compensate for the lower temperature of the injected stored air, the system achieves a significant enhancement in net electrical power output compared to the baseline. Furthermore, the proposed modular above-ground storage configuration, utilizing an array of thirty 40-bar industrialtanks, demonstrates practical feasibility for regions lacking suitable underground geological formations. The study confirms that integrating CAES with existing gas turbine plants represents a practical solution for improving grid reliability and operational flexibility .

The Man-Made River (MMR) in Libya is one of the world’s largest water transfer systems, yet it consumes vast amounts of electricity to deliver water across long distances. This research addresses the challenge of energy demand by exploring the recovery of hydraulic energy that is normally wasted as pressure losses within the MMR pipelines. A hydraulic model of the pipeline was developed using EPANET, with actual design data serving as input. Four operational scenarios were evaluated (1.2, 1.3, 2.0, and 2.5 MCMD), and the results were analyzed to estimate recoverable energy and identify suitable turbine types. The methodology included simulation of head and pressure distributions, energy recovery calculations, and comparison with the system’s electricity consumption. The study found that at current operating flows (1.2–1.3 MCMD), turbines could recover between 686 and 733 MWh/day, meeting nearly half of the system’s electricity demand. At the original design capacity of 2.0 MCMD, recovery declined to about 610 MWh/day, while at 2.5 MCMD the net gain dropped to only 117 MWh/day due to pumping requirements. Francis turbines were identified as the most practical technology, with Pelton turbines as secondary options in high-head conditions. The research demonstrates that hydropower recovery from the MMR is technically feasible and environmentally beneficial. It could substantially reduce CO₂ emissions and strengthen Libya’s energy security by integrating renewable power directly into the national grid.

The examination of the flow instability is crucial in nuclear reactors to prevent the occurrence of flow excursion during a postulated accident. Instabilities can reduce the safety margins against these critical heat flux phenomena, where the fuel cladding surface temperature increases dramatically. In this paper, flow instability is investigated at the reactor of Tajoura Nuclear Research Centre with low enriched uranium (LEU) core, the recent reactor’s fuel assembly type is IRT-4M. The flow instability is assessed at the hottest cell of the reactor by implementing PLTEMP/ANL code.

The safety margin to flow instability is evaluated at the maximum power of the reactor (9.7 MW) by running the code at various core flow rates to find the point where FIR equals 1. The inlet coolant temperature is 45°C, the core pressure drop is 0.066 MPa, and the inlet pressure is 0.179 MPa. At the beginning, the calculations have been obtained at the flow rate of 8.1 kg/s. The results show that the maximum cladding surface temperature is 96.971°C which is less than the permissible value (102°C), the minimum FIR, ONBR, and DNBR equals to 2.034, 1.559, and 4.147, respectively. By reducing the core flow rate at the maximum power level, it is determined that FIR equals to 1.0 at the flow rate of 3.976 kg/s. Which means that the safety margin value to flow instability is 2.038. It is concluded from the safety margin value to flow instability that it is in agreement with the thermal-hydraulic safety requirements of TNRC’s reactor. 

Accurate wind speed modeling is essential for reliable wind resource assessment and wind farm planning. Although the Weibull distribution is widely used in wind energy studies, its accuracy may vary depending on regional climatic characteristics. This study evaluates the suitability of the Weibull distribution for modeling wind speed data in Libya and compares its performance with several alternative probability distributions. The analysis is based on 7,305 daily wind speed observations obtained from the NASA POWER database for the period 2004–2023 at a reference height of 50 m above Sea level for five locations in Libya: Tripoli, Ajdabiyah, Alkufra, Awbari, and Darnah. The mean wind speeds range from 6.419 m/s in Tripoli to 7.270 m/s in Darnah. Statistical analysis was performed using EasyFit and MATLAB, while the Kolmogorov–Smirnov (K–S) test was applied to evaluate the goodness of fit between observed and theoretical distributions. The results show that the Weibull distribution does not provide the best statistical representation for any of the studied locations. Instead, Johnson SB, Dagum (4P), and Burr distributions demonstrated superior performance. The estimated wind power density ranged from 52.57 W/m² in Tripoli to 78.97 W/m² in Darnah, highlighting the importance of flexible multi-parameter distributions for improving wind resource assessment and supporting more reliable wind farm planning in Libya.

The reliable operation of power plants is essential for the continuity of modern societies. The South Tripoli Power Plant in Libya plays a crucial role within the national power grid. However, it faces significant challenges, including frequent operational failures, aging infrastructure, and ineffective maintenance practices, all of which compromise its reliability and escalate operational costs. This research is motivated by the urgent need to improve maintenance strategies in accordance with documented failure patterns, thereby establishing a comprehensive framework for effective maintenance planning. To address these challenges, a maintenance optimization framework has been developed that integrates Failure Modes, Effects, and Criticality Analysis (FMECA) with Fuzzy Logic techniques to enhance decision making processes regarding maintenance. Furthermore, a fuzzy FMECA framework has been designed to categorize maintenance strategies, thereby facilitating data driven maintenance planning. The contributions of this research are aimed at improving plant reliability, reducing the incidence of unplanned outages, and ensuring the sustainable performance of energy infrastructure.

This review examines 56 peer-reviewed studies on gas-turbine emission modeling, covering data-driven, semiempirical,

physics-based, and hybrid approaches. It compares model fidelity, computational cost, and interpretability

across these methods and finds a clear hierarchy in which physics-based models provide the mechanistic

foundation, while hybrid and data-driven systems extend that fidelity for scalable, real-time prediction.

Together they establish a coherent basis for modeling NOₓ and CO under both conventional and fuel-flexible

conditions.

A structured search of Scopus, Web of Science, and supplementary databases (records = 260; screened = 230;

full-text = 110; included = 56) followed PRISMA methodology to ensure transparency and reproducibility.

Eligible studies offered quantitative validation of combustion or emission models and were classified into four

methodological groups. Each was examined for governing variables, validation approach, and performance

metrics, allowing comparison of predictive reliability across operating regimes.

Recent research shows that hybrid architectures, which couple mechanistic transparency with data-driven

adaptability, are now dominant. Physics-based simulations generate the high-fidelity datasets used for

training, whereas machine-learning surrogates interpolate rapidly within validated domains. Remaining challenges

involve transient validation, fuel-flexible kinetics, limited domain coverage, and the absence of standardized

uncertainty quantification. Adaptive, physics-guided learning frameworks that preserve interpretability

and quantify reliability across fuels, loads, and stages of engine aging appear most capable of addressing these

gaps.

Rotating machinery is a critical component in mechanical systems, widely used across industrial applications. Due to time-varying speed conditions and complex operating environments, it is highly prone to various failures. Without timely diagnosis and maintenance, such failures can lead to significant performance degradation or catastrophic outcomes. To address the challenges posed by non-stationary operating conditions and vibration signals, researchers have developed diverse fault diagnosis methods, including advanced non-stationary signal processing techniques and data-driven approaches. Among these, generalized demodulation (GD) has demonstrated particular effectiveness in extracting fault-related features from complex signals. This paper provides a comprehensive review of GD-based fault diagnosis methods for rotating machinery. It revisits the fundamental concepts and theoretical basis of GD, analyzes the limitations of traditional approaches, and systematically compares GD with other widely used methods. Furthermore, existing GD-based techniques are categorized into speed-dependent and speed-independent methods based on their reliance on rotational speed, with representative studies and applications discussed. Finally, future research directions and current challenges in GD-based diagnosis are outlined, offering valuable insights for researchers and practitioners in the field.

Planetary gear systems in industrial equipment, characterized by highly integrated structures, non-stationary operating conditions, and heavy-load characteristics, result in significant attenuation of fault-related vibration features, severely limiting the effectiveness of fault diagnosis. Traditional studies often use periodic modulation terms, such as the Hanning window function, to approximate the spatiotemporal distribution characteristics of meshing forces. Although these methods can effectively extract global vibration features of healthy gears (such as meshing frequency and its harmonics), they have significant limitations and are unable to analyze the nonlinear diffusion process of fault impacts through complex paths, resulting in distortion in the phase delay and amplitude attenuation patterns of fault impacts. To address these issues, this study investigates the vibration transmission characteristics of a planetary gearbox through rigid-flexible coupling simulation analysis, focusing on the transmission delay effects of impact responses to the vibration sensors at different housing locations. The study qualitatively clarifies the intrinsic relationship between transmission delay characteristics of the gear-sensor spatial relationship, providing a theoretical foundation for accurate analysis of vibration signals in planetary gear sets. The research highlights the significant spatiotemporal characteristics of the vibration responses when the fault collisions occur at different locations during the rotation and revolution of planet gears.

Resonance occurs when the operating frequency of a system aligns with its natural frequency, resulting in amplified vibration amplitudes. To prevent potential damage and ensure optimal performance of a compressor's base frame at Souq Al-Khamis Cement Factory, researchers found the resonance in has been occurred when both the natural frequencies and rotating frequency were overlapped. Resonant Vibration in the base frames arises when the rotating vibration frequency aligns with the frame’s natural modes that leads to structural instability, fault unplanned shutdowns and production losses. This study analyzes resonant vibration in a cement factory compressor base frame and proposes a redesign using finite element methods to mitigate this issue. Four distinct modifications were made to the base frame on its shape, weight and boundary conditions: the first introduces fixed points to enhance rigidity, the second adds supports for increased stability, the third incorporates elements to improve durability, and the fourth enhances the thickness of the compressor. The results indicate that the redesigned configuration most effectively mitigates resonance and improves the system's natural frequency response.