The rapid growth in global energy demand and the increasing penetration of renewable energy sources (RESs) have accelerated the deployment of microgrids (MGs) as decentralized, resilient, and sustainable energy systems. Despite their advantages, the intermittent nature of renewables and load variability introduce significant operational and control challenges. Conventional heuristic optimization techniques have been employed to address these complexities; however, they often exhibit limitations in handling nonlinear, non-convex, and multi-objective optimization problems. MHOAs have gained significant attention as efficient optimization tools due to their strong global search ability and adaptability in addressing complex optimization problems in MGs. This review provides a comprehensive analysis of recent developments in the application of MHOAs for techno-economic optimization, energy management, resilience improvement, and fault detection within MG systems. Moreover, this study incorporates machine learning (ML) methodologies into the MG management framework. Various ML paradigms—including supervised, unsupervised, reinforcement, and deep learning (DL) techniques—are systematically classified and comparatively assessed in terms of predictive capability, scalability, data dependency, and suitability for real-time implementation. The integration of MHOAs with ML techniques forms an intelligent and adaptive framework that strengthens the sustainability, reliability, and operational performance of next-generation MG systems, while also highlighting existing challenges and potential avenues for future research. Clinical trial number: Not applicable.
Plastic waste recycling from vehicles is an important aspect of responsible environmental management. It investigates novel methodologies for recycling plastic waste from the automotive industry, including out-of-service vehicle parts and manufacturing waste. Mechanical and chemical recycling processes convert plastic waste into high-quality secondary raw materials, which can be used in various industrial processes. Improving material recovery through the recycling process reduces environmental impact while promoting the circular economy by reusing recovered plastics in new automobile products. This research investigates sustainable manufacturing techniques in the automotive plastics industry by improving recycling as well as refurbishment practices, which helps to reduce environmental impact and promote resource efficiency. During manufacturing, scrap - excess burn-in pieces, scrap from grinding, refining, and trimming - is generated. The circular economy concept recycles all of the collected plastic waste and uses it as a raw material for manufacturing. Moreover, it explores remanufacturing sustainable production modes, including a carbon tax and screening cost, to reduce carbon emissions to get a system’s optimum total profit. Fuzzy numbers are often used to describe this uncertainty where inventory features and goals are not specified. Cost parameters are described using an intuitionistic fuzzy number. Because of these Fuzzy Parameters, The model itself is fuzzy then it is converted into a definite value using the Graded Mean Integration method (GMI).
To address the escalating global energy demand driven by technological advancement and increasing consumer requirements, microgrids (MGs) have emerged as promising systems that integrate renewable energy sources (RESs). These are small-scale, autonomous energy networks designed to operate independently through the utilization of distributed energy resources (DERs). However, the intermittent behavior of renewables and the inherent variability in power quality pose critical operational challenges that must be effectively managed to ensure optimal MG performance.To address these operational complexities, numerous heuristic optimization methods have been introduced to improve the efficiency and reliability of MGs. However, these approaches frequently encounter inherent drawbacks, including premature convergence toward local optima and challenges in achieving global optimality, especially when applied to non-linear and non-convex problem domains. Such deficiencies adversely influence key operational dimensions such as energy management, economic dispatch, system reliability, storage capacity determination, cyber-resilience, and grid coordination. As a result, these constraints complicate the processes of energy storage control, cost optimization, and renewable energy integration, thereby emphasizing the necessity for more advanced and resilient optimization paradigms.To overcome these shortcomings, the adoption of metaheuristic optimization algorithms (MHOAs) has gained significant attention. This study provides a comprehensive review of the current advancements and applications of MHOAs in improving the operational efficiency of MGs. Initially, it discusses the fundamental principles of MG optimization, highlighting the capabilities, requirements, and opportunities associated with the integration of MHOAs in MG systems. Subsequently, diverse MHOAs utilized within the MG framework are critically analyzed, focusing on their contemporary advancements and applications in techno-economic optimization, load prediction, resilience enhancement, operational control, fault detection, and overall energy management.The review indicates that Particle Swarm Optimization (PSO) is employed in nearly 25% of studies, whereas Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO) are implemented in approximately 10% and 5% of research works, respectively, for optimizing MG performance. These findings demonstrate that MHOAs provide system-independent optimization frameworks, thereby presenting a promising direction for enhancing the efficiency, adaptability, and intelligence of next-generation MGs. Finally, several existing challenges associated with MHOA integration are identified, offering valuable insights and future research opportunities for further advancements in this domain.
The impact of TeO2 addition on the optical, dielectric, and elastic properties of xTeO2-(0.40-x)MoO3-0.25ZnO-0.35P2O5 glassy systems has been systematically studied. The density of the glassy specimens increases from (3.78-4.11) g.cm- 3, while the molar volume decreases from (36.43-34.08) cm3mol- 1 with increasing TeO2 concentration. Optical analysis reveals a reduction in the optical bandgap from (3.37-2.67) eV, and an increase in Urbach energy from (0.63-0.92) eV. Dielectric studies are thoroughly performed using dielectric and modulus spectroscopy. The well-known Bergman's mode is applied to analyze the modulus spectra, while the HavriliakNegami (HN) formalism is employed for dielectric relaxation analysis. By scaling modulus spectra, the changes in relaxation paths of charge carriers with the alteration of temperature has been illustrated. Furthermore. The Ultrasonic velocity measurements are performed to evaluate elastic properties, including shear modulus (increased from 15.79 to 20.33 GPa), longitudinal modulus (increased from 48.52 to 59.01 GPa), bulk modulus (increased from 27.51 to 32.81 GPa), Young's modulus (increased from 39.77 to 49.98 GPa), and Poisson's ratio (decreased from 0.258 to 0.242). Also, the results obtained from the Makishima-Mackenzie model validates the acquired elastic moduli data from ultrasonic measurments. These obtained results suggests that the as-prepared samples are beneficial for designing the mechanically improved conductive glassy materials for reducing power dissipation, facilitating high frequency advanced devices.
With the rapid development of smart device technology, the current version of the Internet of Things (IoT) is moving towards a multimedia IoT because of multimedia data. This innovative concept seamlessly integrates multimedia data with the IoT-Edge Continuum. Recently, a distributed learning framework shows promise in revolutionizing various industries, including smart cities, healthcare, etc. However, these applications may face challenges such as the presence of malicious devices that invade the privacy of other devices or corrupt uploaded model parameters. Additionally, the existing synchronous federated learning (FL) methods face challenges in effectively training models on local datasets due to the diversity of IoT devices. To tackle these concerns, we propose an efficient and privacy-enhanced asynchronous federated learning approach for multimedia data in edge-based IoT. In contrast to traditional FL methods, our approach combines revocable attribute-based encryption (RABE) and differential privacy (DP). This guarantees the privacy of the entire process while allowing seamless collaboration between multiple devices and the aggregation server during model training. Also, this combination brings a dynamic nature to the system. Furthermore, we utilize an asynchronous weight-based aggregation algorithm to improve the efficiency of training and the quality of the final returned model. Our proposed scheme is confirmed by theoretical safety proofs and experimental results with multimedia data. Performance evaluation shows that our framework reduces the cryptography runtime by 63.3% and the global model aggregation time by 61.9% compared to cutting-edge schemes. Moreover, our accuracy is comparable to the most primitive FL schemes, maintaining 86.7%, 70.8%, and 86.1% on MNIST, CIFAR-10, and Fashion-MNIST, respectively. The experimental results highlight the remarkable practicality, resilience and effectiveness of the proposed scheme.
