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.

High-temperature heat pumps (HTHPs) are a promising solution for reducing carbon emissions in industrial heating by upgrading low-grade waste heat to temperatures above 100 °C. A key challenge in designing efficient and practical HTHP systems is choosing the right refrigerant. This study presents a comprehensive thermodynamic analysis of seven low-global-warming-potential (GWP) refrigerants R718 (water), R600, R123, R1234ze(Z), R1233zd(E), R1224yd(Z), and R245fa for high-temperature heat pump (HTHP) applications. The refrigerants were evaluated under temperature lifts of 40 °C and 80 °C and condenser temperatures between 100 °C and 150 °C. Key performance metrics including coefficient of performance (COP), volumetric heating capacity (VHC), compressor pressure ratio (PR), discharge temperature, volumetric flow rate, power consumption, and second-law efficiency were analyzed to identify optimal working fluids. Results show that R718 achieved the highest COP, up to 6.9 at 40 °C lift and 3.0 at 80 °C lift, along with the highest second-law efficiency, reaching 55% at 80 °C lift, due to its superior thermodynamic properties. However, R718 also exhibited the lowest VHC (1900  kJ/m³ at 40 °C lift) and the highest discharge temperatures (>520 °C at 80 °C lift), requiring larger compressors and advanced materials. Conversely, R1234ze(Z) and R600 demonstrated higher VHC (>7000  kJ/m³ at 40 °C lift) and moderate discharge temperatures (<180 °C), enabling more compact and cost-effective designs but at a reduced COP (5.5 at 40 °C lift). This study highlights that R718 has clear advantages in HTHP applications. In industrial heat recovery, an R718-based HTHP can provide the required high output temperatures while achieving better overall performance. These findings provide practical guidance for engineers and designers working on next-generation heat pumps for industrial heating applications.

High-temperature heat pumps (HTHPs) are emerging as a cornerstone technology for industrial decarbonization, enabling efficient recovery and upgrading of low-grade waste heat to supply process heat and steam above 100 °C. Operating at temperatures between 120 °C and 200 °C, HTHPs address the heating demands of energy-intensive sectors such as chemicals, food processing, and metals. This review consolidates recent advancements in HTHP design, refrigerant selection, and integration strategies. Current systems achieve coefficients of performance (COP) of 2.5–4.0 for 100–150 °C outputs, while advanced configurations using low-GWP refrigerants report COPs up to 6.10. Environmental benefits are significant: HTHPs can reduce CO₂ emissions by 60–98% compared to gas boilers, with case studies demonstrating annual savings exceeding 30,000 tCO₂. Economic analyses indicate payback periods as short as 1.9–3 years for optimized designs. Key challenges include the development of low-GWP refrigerants, high initial investment costs, and maintaining efficiency under large temperature lifts. Future research should focus on advanced cycle configurations, integration with thermal storage and renewables, and innovative compressor technologies to accelerate adoption. Overall, HTHPs represent a critical pathway for low-carbon industrial heating, offering substantial energy recovery and a proven potential to reduce the carbon footprint of high-temperature processes.

Solar energy is a clean, abundant, and sustainable power source that forms the foundation of energy sustainability. Researchers have focused on examining various factors affecting solar energy generation and storage to improve the efficiency of solar collectors. They have evaluated different design criteria, considering environmental elements such as wind speed, solar radiation, and ambient temperature. Both experimental methods and numerical simulations, including Computational Fluid Dynamics (CFD), have been used. ANSYS-Fluent CFD modeling, in particular, provides a cost-effective alternative to experiments by simulating fluid flow and heat transfer within solar collectors. This article reviews recent advances in numerical modeling of concentrating solar systems, using ANSYS-Fluent, detailing the models and methods employed while discussing current challenges. It covers various solar concentrators, including evacuated tube collectors (ETC), Linear Fresnel reflectors (LFR), Compound Parabolic Collectors (CPC), and Solar Towers (ST). Summaries of previous studies are tabulated, highlighting different CFD models, techniques, and assumptions. The main goals and results of these studies are outlined. The article also discusses validation techniques and compares experimental data with simulation outcomes, assessing the employed numerical models and methods. It emphasizes common physical models, solution strategies, and assumptions used in analyzing different solar concentrating systems. Additionally, it identifies current challenges, suggests future research directions, and offers perspectives to help advance understanding. This work aims to support researchers in understanding current trends in the numerical simulation of high-concentration solar collectors. Scholars can use this resource to select appropriate models and methods, leveraging their strengths and avoiding common pitfalls in CFD analysis of solar collectors with ANSYS-Fluent.

Crude oil distillation is one of the most energy-intensive processes in petroleum refining, consuming up to 20% of total refinery energy. Improving the energy efficiency of crude distillation units (CDUs) is essential for reducing costs, lowering emissions, and achieving sustainable refining. Current studies often examine energy savings, operational flexibility, or renewable energy integration separately. This review brings these aspects together, focusing on heat integration, advanced control systems, and renewable energy options such as solar-assisted preheating and green hydrogen. Advanced column designs, including dividing-wall and hybrid systems, can cut energy use by 15–30%, while AI-based optimization improves process stability and flexibility. Solar-assisted preheating can reduce fossil fuel demand by up to 20%, and green hydrogen offers strong potential for decarbonization. Our findings highlight that integrated strategies, including advanced simulation tools and machine learning, significantly improve CDU performance. We recommend exploring hybrid algorithms, renewable energy integration, and sustainable technologies to address these challenges and achieve long-term environmental and economic benefits.

Distillation is a critical separation process widely used in various industries, especially in petroleum refining, where efficient separation significantly influences product quality and energy consumption. This study evaluates the performance of a crude distillation unit located in Upper Egypt, with the aim of enhancing its energy and exergy efficiencies by addressing region-specific operational challenges and inefficiencies. A comprehensive thermodynamic and exergy analysis was conducted using Aspen HYSYS, based on the first and second laws of thermodynamics. The simulation model was validated against actual plant data, demonstrating strong agreement and confirming its reliability. The analysis focused on key process units, including the preflash unit, fired heater, heat exchanger network (HEN), pumps, coolers, and particularly the distillation tower, which showed the highest exergy destruction. The distillation tower alone accounted for 41.8% of total exergy destruction (44.5 GJ h⁻¹), primarily due to irreversibilities associated with phase separation. In contrast, the preflash unit exhibited high performance, with an exergy efficiency of 97.1% and minimal destruction (459 MJ h⁻¹). The fired heater and HEN also demonstrated strong efficiencies 92.7% and 91.6%, respectively, though both contributed to non-negligible thermal losses. Coolers, however, had the lowest exergy efficiency (55.2%), responsible for 33% of total exergy destruction. Parametric studies revealed that increasing overhead pressure improved overall exergy efficiency by 4.3%, while excessive pump-around flow rates led to higher irreversibilities and reduced efficiency. These findings offer valuable, localized insights for improving energy recovery and operational performance in refining processes, particularly in developing regions with limited operational data. The study supports efforts to enhance sustainability and implement energy-efficient control strategies in crude oil refining.

Crude oil distillation is a crucial separation process in the petroleum refining industry, where crude oil is fractionated into various components based on their boiling points. This energy-intensive industrial process significantly contributes to global energy consumption and greenhouse gas emissions. Given the magnitude and environmental implications of crude distillation operations, there is an urgent need to analyze energy usage patterns rigorously and identify opportunities for enhancing efficiency. This review explores the extensive efforts made by researchers to develop advanced models and techniques for the techno-economic evaluation of crude distillation systems. It begins by highlighting the considerable energy demands and emissions associated with conventional crude distillation units (CDUs). Emphasizing the necessity of comprehensive energy analysis, the paper discusses how optimization strategies can improve CDU operations and enable retrofits aimed at reducing both energy consumption and environmental impacts. Various modeling approaches are examined, including rigorous process simulations using tools like Aspen HYSYS and innovative exergy-based analyses, which provide deeper insights into the thermodynamic principles and operational factors influencing CDU performance. The review focuses on key areas such as distillation tower configurations, operating conditions, and heat exchanger network designs, all aimed at identifying energy-efficient modifications. Additionally, the paper discusses advancements in process intensification techniques, including Dividing Wall Columns, Hybrid Distillation, and reactive distillation. These methods not only enhance separation efficiency but also contribute to significant reductions in energy usage. The findings from numerous case studies are synthesized, demonstrating their effectiveness in improving overall efficiency.