Solar energy electricity generation with concentrated photovoltaic (CPV) panels delivering substantially higher
power output than non-concentrated panels. However, CPVs suffer from excessive heat accumulation and non-
uniform temperature distribution, both of which severely impact electrical efficiency and shorten the panels’
lifespan. This study explores the implementation of a passive evaporative cooling strategy coupled with heat pipe
to mitigate CPV temperature rise. To address this challenge, a novel dual-function mechanism is proposed,
enabling efficient CPV cooling while simultaneously capturing and condensing fresh water to minimize evaporative
cooling losses. The study evaluates the proposed water harvesting indirect cooling mechanism
(WH-ICM) against two alternative cooling strategies: the direct cooling mechanism (DCM),
where the evaporative structure is directly attached to the panel’s backside, and the indirect cooling mechanism
(ICM),b which incorporates a heat pipe. The findings revealed that the ICM attained the highest average PV temperature reduction of 45.2 C, representing a 45.3 % improvement over the conventional PV system. Meanwhile, the WH-ICM followed closely with a 42.3  C reduction, marking a 42.4 % enhancement. Furthermore, WH-ICM boosted the average PV output power by 36.8 % and improved average efficiency by 30 % compared to the standard PV system. It also harvested
2.87 kg/day.m ◦2 of water, with a consumption of 25.36 kg/day.m2, recovering around 11.3 % of the total water
used. Notably, integrating WH-ICM increased the system’s daily average overall efficiency to 19.43 %, reflecting
a 36.26 % improvement over the conventional PV system, and a 7.7 % and 3.1 % raise compared to the DCM and
ICM, respectively.

This study evaluates the performance of a new standalone integrated system for independent hydrogen production combining waste heat recovery (WHR) via thermoelectric generators, humidification-dehumidification
(HDH), and a proton exchange membrane (PEM) electrolyzer. The system’s behavior is analyzed at varying
steam inlet temperatures and qualities to identify the best configuration for power generation. A complete
mathematical model of the whole system unit is constructed, programmed inside Matlab, solved, and validated.
Four configurations are studied for the thermoelectric generator unit distributions [100 × 100], [50 × 200],
[25 × 400], and [12 × 833]. Configuration 4 (12 ×833 TEG arrangement) outperforms others, generating
36.88 kW at 160 ◦C and 0.97 steam inlet quality, a 130 % increase compared to 100 C. This configuration extracts 337 kW from steam, enabling a hydrogen production rate of 15.5 kg/day with PEM efficiency peaking 73.65 % (declining to 68.94 % at 160 ◦◦C due to overpotentials). The HDH unit is operating at a GOR of 1.892. The system efficiency increased from 4.45 % at 100 ◦C to 4.95 % at 160 C, driven by enhanced TEG power generation. Economic analysis reveals a levelized hydrogen cost (LCOH) of 2.22–2.22–2.96/kg, competitive with blue hydrogen markets. Net Present Value (NPV) analysis shows profitability at 3–5 $/kg hydrogen, with breakeven 10–20 years for 3 $/kg and 4–5 years for 5 $/kg. Excess water utilization in the PEM electrolyzer reaches near-zero at 160 ◦ C, contrasting with 55 % excess at 100 ◦◦C. Trade-offs between steam quality and hydrogen yield are quantified: increasing quality from 0.05 to 0.97 at 100 C raises hydrogen production by 0.45 % (6.97–7.01 kg/day), while higher temperatures prioritize power over electrolyzer efficiency.

This study investigates the gasification of tea waste biomass in a fluidized bed reactor, with a focus on optimizing
syngas composition and energy content. A lab-scale hot flow fluidization bed reactor is designed, fabricated and
installed. The impact of fluidization parameters, velocity and gasification temperature on the quality of syngas
products is investigated. The effect of these parameters on the CO and H
percentages and calorific value of the produced syngas is studied. The results show that increasing air injection velocity enhances carbon monoxide (CO) production and reduces carbon dioxide (CO2) levels, with an optimal air injection velocity of 15 m/s for maximizing syngas calorific value. Furthermore, a gasification temperature of around 400  C is found to be optimal for producing syngas with high calorific value, balancing CO and hydrogen (H) production while minimizing CO2 . A higher CO/CO ratio is closely linked to increased syngas energy content, while the methane to hydrogen ratio also influences calorific value, though its impact is less predictable.

To achieve affordable and clean energy as part of the sustainable development goals, a techno-enviro-economic
performance of solar Photovoltaics (PV) and Vertical Axis Wind Turbines (VAWT) hybrid system for street
lighting load of New Borg El-Arab city, Egypt is presented. The system is designed for both standalone battery
storage and grid-connected at different ratios of energy sources “PV 100%”, “VAWT 100%”, “PV 50% and VAWT
50%”, “PV 33.3%, and VAWT 66.7%” and “PV 66.7% and VAWT 33.3%”. The physical model was created in
Autodesk INVENTOR, the spacing of the wind turbine was simulated on ANSYS FLUENT, the spacing of the solar
photovoltaic panels was simulated in Autodesk REVIT, the mathematical model was solved in MATLAB and
Microsoft EXCEL. The study outcomes reveal that the energy system distribution for the grid-connected and
standalone systems are about the same achieving smaller turbine numbers, PV panels, and land space in the case
of grid-connected systems. The minimum capital and payback period are achieved by the grid-connected system
with a land ratio of “PV 100%”. It has a payback period of 12 years while using a land area of 139 m
. It used 30 Solar PV panels to supply a total energy of 20,829 kWh for 1 year. This resulted in a levelized cost of electricity of 0.0096 $/kWh and 450 tonnes of CO emission savings. This shows 100% PVs grid-connected system is more economically viable than the hybrid system.

This study focuses on enhancing the efficiency of vertical axis Savonius Hydrokinetic turbines designed for
marine applications, historically characterized by a power coefficient below 0.1. Prior efforts aimed at improving
rotor performance have primarily involved modifications to blade designs. In this article, a new approach is
introduced, incorporating twisted blades inspired by the Archimedes screw turbine. Utilizing a 3D incompressible
flow analysis based on the Navier-Stokes equation, this research explores and compares the turbine’s
effectiveness with varying screw pitches (0.5, 0.75, 1). The system of equations is solved numerically using
ANSYS 2020 R2 fluid fluent. The performance assessment involves contrasting each proposed rotor against a
pitchless semi-circle rotor. An innovative aspect of this work involves investigating the impact of asymmetry
using two different ratios (2:1 and 3:1). Specifically, the lower half of the optimal pitch screw remains constant,
while the upper half varies based on these ratios. To understand performance trends, the study employs visualizations
of pressure, velocity contours, and streamlines to grasp the flow field and its underlying principles.
Turbulent kinetic energy and eddy viscosity are also visualized. The results reveal an 18.25 % improvement in
performance with the proposed rotor featuring a pitch screw of 0.5. Notably, the asymmetric rotor with a 2:1
ratio demonstrates the highest performance. According to the ANN, the optimum pitch screw value is determined
to be 0.6, achieving a power coefficient of 0.1938. This investigation employs novel design modifications and
asymmetrical configurations, offering valuable insights into significantly enhancing the performance of Savonius
turbines for marine applications.

As the adoption of renewable energy is on the rise all around the world, the use of wind and solar energy is
increasing rapidly. Most wind or solar farms only use a single energy resource for generation on a specific area of
land. So, this study presents a techno-enviro-economic evaluation of using both horizontal axis wind turbines and
photovoltaic panels on the same land area to increase land utilisation, it is presented on the map of Egypt as a
case study. The area of the wind turbine shadows on the solar photovoltaic panels are estimated using new
technique of image processing with Python code. An energy model was developed on MATLAB Simulink to
analyse the yield energy generation. The dual use of land is compared to cases using solar only, and wind only for
wind turbines ranging from 0.75 MW to 3 MW. The results demonstrate that the dual use land produces the
highest amount of energy yearly in 41 % of the governorates when using the 0.75 MW turbine. Then, the dual use
land produces the highest amount of energy in 100 % of the governorates when using the 1.5 MW and 3 MW
wind turbines. This shows that the bigger the size of the turbines, the lesser the effect of the shadows on the
overall energy being produced. In terms of economic viability, the best performing case used solar photovoltaic
only in Aswan governorate with a total energy generation of 594,856 MWh and total CO
emission reduction of 10,351 tonnes over the 25 years project lifetime. It has an LCOE of 0.01050 $/kWh, a payback period of 8.96 years, and a capital cost of $3,391,956. In conclusion, the study results show that dual-use land can improve the economic viability of projects using only wind turbines for energy generation in Egypt.

The increasing reliance on the renewable energy, particularly wind power, introduces significant challenges for modern power systems and can compromise system stability. This study proposes an improved pitch-angle control strategy for a 1.5 MW large-scale Wind Energy Conversion System (WECS) based on a Doubly-Fed Induction Generator (DFIG). To address the limitations of conventional controllers, which struggle with system nonlinearity and the requirement for highly accurate mathematical models, this study examined Proportional-Integral-Derivative (PID) and Fractional PID (FPID) strategies. These were integrated with Neural Network (NN) architectures, specifically Multilayer Feedforward (MLFFNN), Cascade Forward (CFNN), and Elman NN, to improve control performance. The results, using MATLAB/Simulink, show that the MLFFNN architecture provides superior performance. With a minimum Mean Square Error of 0.0027024 and a power performance efficiency reaching a 98.9% under step, ramp, and random wind speed variations, the proposed NN controller consistently outperforms both PID and FPID systems, offering a robust solution for large-scale wind energy applications.

Urban traffic congestion remains a persistent challenge for conventional fixed-time signal control, particularly under fluctuating and asymmetric demand. Although multi-agent reinforcement learning (MARL) has shown promise for adaptive traffic signal control, previous studies have often focused on isolated intersections, simplified synthetic networks, or deep-learning-based controllers without systematically comparing tabular and deep-value-based multi-agent approaches under equivalent operating conditions. This study addresses this gap by comparing three traffic signal control strategies: fixed-time control, Multi-Agent Tabular Q-Learning, and multi-agent Deep Q-Network control (MADQN). The evaluation was conducted in a microscopic traffic simulation environment using two complementary testbeds: a synthetic two-intersection corridor, which enables controlled analysis of multi-agent coordination, and a real-world digital twin of the 25 January Corridor in Assiut, Egypt, which tests controller robustness under asymmetric geometry and realistic turning movements. The controllers are assessed under low-, medium-, and high-demand scenarios using queue length, cumulative delay, and Time-To-Collision as operational and safety-related indicators. The results show that MARL-based controllers generally outperform fixed-time control, but their relative performance depends on demand intensity and network complexity. MADQN provides stronger generalization in low-demand and queue-dissipation conditions, whereas Tabular Q-Learning remains highly competitive and can achieve superior delay reduction in several medium- and high-demand cases. These findings indicate that deeper MARL architectures are not universally superior; rather, adaptive signal control deployment should match the controller architecture to the operational objective, traffic demand regime, and practical complexity of the target corridor.

Numerous engineering problems can be represented as minimax optimization problems, including machine learning, classification, robust optimal control, signal processing, game theory, and more. Typically, minimax problems are considered challenging, especially constrained ones. The recently introduced artificial rabbits optimization (ARO) is inspired by the natural behaviour of rabbits. ARO exhibits robust effectiveness in tackling optimization challenges. Despite its advantages, ARO converges early to local optima, especially in complex or multi-modal optimization problems, and it struggles to balance exploration and exploitation, often leading to premature convergence and reduced accuracy. In this paper, we present a chaotic ARO that employs five maps exhibiting randomization behaviour to refresh candidate solutions. We assess the performance of the suggested CARO by applying it to 46 benchmark functions (25 unconstrained and 21 non-smooth minimax) and 15 constrained test functions with diverse characteristics. We evaluate its performance against six swarm intelligence algorithms. Also, we employ the chaotic maps to ARO and the six compared algorithms, and we perform a non-parametric statistical test, the Friedman test, on all outcomes. The findings show that the proposed algorithm can solve both unconstrained and constrained minimax problems more effectively and efficiently than other swarm intelligence methods.

High Temperature Heat Pumps (HTHPs) are increasingly recognized as a key technology for decarbonizing industrial heating processes. This study presents a comprehensive thermodynamic and exergy analysis of various Low-GWP Refrigerants used in HTHP systems operating under different temperature lifts and condensation temperatures. The refrigerants evaluated include R718 (water), R600 (butane), R123, R1234ze(Z), R1233zd(E), R1224yd(Z), and R245fa. Results show that R718 consistently outperforms other refrigerants in terms of COP and exergy efficiency. At a temperature lift of 40 ◦ C and a condensation temperature of 150 ◦ C, R718 achieves a COP of 6.9 and an exergy efficiency of 49%. Even at an 80 ◦ C lift, its COP remains at 3.0, with exergy efficiency rising to 55%, indicating strong thermodynamic resilience. However, R718 also exhibited the highest discharge temperatures, requiring larger compressors and advanced system configuration. In contrast, R600 exhibits the lowest COP and highest exergy destruction, making it unsuitable for high-lift applications. Exergy destruction analysis identified the compressor as the dominant source of irreversibility, contributing more than 50% of total exergy destruction under all conditions. Total exergy destruction increased sharply with higher temperature lifts, ranging from 5 to 12% at 40 ◦ C to 10–30% at 80 ◦ C. Component-level analysis highlighted that improvements in compressor design and refrigerant selection are critical to minimizing system losses. Notably, R1233zd(E) and R1234ze(Z) showed lower compressor and condenser irreversibilities compared to other synthetic refrigerants. These results provide valuable guidance for refrigerant selection and system optimization in the development of efficient and sustainable HTHP technologies.