Asian Journal of Convergence in Technology
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Dual Axis Sun Tracking System with Weather Monitoring System
Get PDFA world is now move towards renewable energy source due to various factors like pollution and cost of nonrenewable energy sources. One of the main renewable energy sources is Sun. In this project Arduino based Dual-axis solar tracking system proposed in order to get maximum solar energy. The Arduino is used to give command to rotate the solar panel. Solar trackers are used to improve the power gain from solar energy. Solar power is changes due to the seasonal variation and tilting of earth which change the position of the sun in the sky. In this regard dual axis solar tracking is practically implemented and performance is compared with fixed mount and single axis solar tracking system. Finally, project result that proposed method gives better efficiency compared to fixed mount and single axis solar tracking syste
A hybrid Multi-Response Optimization for the Best Biofuel Blend Selection using AHP–TOPSIS Methods
Get PDFThe selection of an appropriate biofuel blend is a multi-criteria decision-making (MCDM) dilemma based on various qualitative and inconsistent criteria, which are crucial for determining the feasibility of new energy sources. This paper presents a hybrid methodology using the analytical hierarchy process (AHP) to compute the relative criteria weights, whereas the technique for order of preference by similarity to ideal solution (TOPSIS) was used to rank the available alternatives. The results indicated that brake thermal efficiency (BTE) and nitric oxides (NOx) are the two most important criteria for rating the performance of a biofuel blend. The following preferences were attained for the blends by using the hybrid AHP–TOPSIS method: BD20CeO200 > BD100CeO200 > D > BD20 > BD100. Hence, after using the hybrid MCDM methods for various biofuel blends, the BD20 with Cerium oxide nanoparticles (200 ppm) was selected as the best biofuel blend for operating CI engines
Design and Evaluation of an Energy-Efficient Regenerative Braking System
Get PDFRegenerative braking systems (RBS) offer a promising solution to improve energy efficiency in modern electric and hybrid vehicles by recovering kinetic energy otherwise lost as heat during braking. This study presents the design, simulation, and experimental validation of a prototype regenerative braking system aimed at maximizing energy recovery and minimizing wear on conventional brake components. A stainless steel disc brake was designed using Fusion 360 and analyzed for thermal and structural performance under braking conditions using ANSYS Workbench. The prototype integrates a 775 DC motor functioning as a generator, converting braking energy into electrical output, which is visualized using an LED load indicator. Simulation results indicate that the disc brake reaches a peak steady-VWDWHWHPSHUDWXUHRIDSSUR[LPDWHO\ௗ&ZLWK maximum heat flux observed at the brake pad contact zone, validating the model’s thermal behavior. Experimental testing demonstrated successful energy recovery, with an output voltage UDQJLQJ EHWZHHQ ௗ9 DQG ௗ9 DW PRWRU VSHHGV RI – ௗ5307KHILQGLQJVFRQILUPWKHIHDVLELOLW\RIWKHSURSRVHG system for small-scale energy harvesting applications and provide a foundation for further development of efficient and adaptable regenerative braking systems for sustainable vehicle technologies
Artificial Intelligence and Machine Learning for Fault Detection and Energy Forecasting in Photovoltaic Systems: A Comprehensive Review
Get PDFWorldwide installation of PV systems has increased trash demand for efficient monitoring and energy forecasting. Efficiency, safety, and financial risk management are the basic cornerstones considered while monitoring PV systems. Classical approaches for fault diagnosis and power prediction of PVs have become obsolete due to their limitations in handling nonlinearities under uncertainties and scalability under varying operational conditions. With the evolution of artificial intelligence and machine learning, intelligent datadriven frameworks can be developed for real-time fault diagnosis, performance evaluation, and predictive maintenance. This study intends to present a critical review of the latest AI and ML techniques in PV system monitoring and forecasting, addressing issues relating to their aptitude in the identification of the most common faults, such as hotspots, partial shading, soiling, and inverter failures, together with improving short- and long-term energy prediction. Deep learning and hybrid AI models, which consider accuracy, sensitivity, and robustness across heterogeneous datasets, are far superior to traditional methods. Also, when integrated with IoT, edge computing, and digital twin technologies, they build on scalability, adaptability, and decision-making capabilities in real time. The review also highlighted concerning issues of data scarcity, generalizability across different climates, explainability, and cybersecurity. Finally, future directions are outlined to create standard datasets and benchmarking practices and construct explainable hybrid models with a trustworthy and transparent foundation, further leading to the wide adoption of AI in PV systems
Experimental Study on the Performance of a Vapor Compression Refrigeration System Using Alternative Refrigerants
Get PDFThis study experimentally examines the performance of a vapor compression refrigeration (VCR) system charged with four refrigerants R134a, R600a, R290, and R1234yf with a dual focus on thermodynamic performance and environmental impact. The investigation covers key indicators such as coefficient of performance (COP), cooling capacity, compressor power consumption, and discharge temperature, along with comparative assessments of volumetric cooling capacity, compressor displacement, and Life Cycle Climate Performance (LCCP). Experimental outcomes indicate that the hydrocarbon options, R600a and R290, provide up to 15% higher COP than R134a while maintaining a markedly lower global warming potential (GWP). Although R1234yf delivers slightly reduced efficiency (about 5–10% lower), it stands out as a viable replacement refrigerant owing to its extremely low GWP (<1) and compliance with current environmental regulations. Overall, the results highlight the trade-offs between efficiency, safety, and sustainability, offering guidance for system designers and policymakers aiming to align refrigeration practices with the Kigali Amendment
Investigation for Impact of Process Parameters on Mechanical Properties of Fused Filament Fabrication Components
Get PDFThe purpose of this article is to look at a variety of tactics used in different industries to optimize the operating parameters of 3D printing systems. Fused Deposition Modeling (FDM), one of the most well-known methods, has drawn a lot of interest because of its broad range of applications in fields including die-making and prototype development. FDM creates three-dimensional objects by layering materials one after the other. Because of its great versatility, the technique makes it possible to produce complex geometries that would be challenging to accomplish with conventional manufacturing techniques. However, FDM still has drawbacks with regard to printing speed, production time, and the structural soundness of the printed parts. The quality of the finished product is directly impacted by a number of variables, including the distance between layers, orientation during printing, percentage of internal fill, deposition angle, path width, and layer depth. Determining and modifying the most important factors in accordance with the particular needs of the item being produced is therefore crucial. To tackle these challenges, numerous researchers have explored advanced optimization tools like experimental design approaches, surface response modeling, evolutionary algorithms, neural network models, and fuzzy logic systems. Many academics have investigated cutting-edge optimization tools such as fuzzy logic systems, evolutionary algorithms, surface response modeling, experimental design approaches, and neural network models in order to address these issues. Strength, accuracy, and dependability are some of the important product attributes that are improved by using these instruments. Objective of this work is to present a thorough analysis of the body of research on enhancing FDM results via efficient process parameter adjustment
Security Aspects of IoT-Enabled Digital Twin Systems Focusing on Challenges Threats and Mitigation Strategies
Get PDFThe intersection of the Internet of Things(IoT) and Digital Twin (DT) has made it possible to synchronize physical and virtual systems in real time, bringing noteworthy innovation in sectors like manufacturing, healthcare, transportation, and smart cities. Although this convergence provides unparalleled visibility into operations and predictive accuracy, it also presents a broad range of cybersecurity risks that compromise the integrity, confidentiality, and availability of physical and digital assets. This research paper examines the security environment of IoT-enabled digital twin systems and determines the most common ten vulnerabilities, such as weak or hardcoded passwords, insecure network services, unprotected interfaces, absence of secure update mechanisms, out-of-date components, inadequate privacy protections, insecure data handling, insecure default settings, ineffective device management, and absence of physical hardening. All of these vulnerabilities are considered in light of their actual-world significance, particularly as digital twin systems become part of vital infrastructure and high-stakes industrial processes. In order to counter these threats, the paper suggests ten all- encompassing mitigation plans, including enforcing one-time credentials, limiting access to high-risk networks, enabling endpoint authentication and access control, validating secure firmware updates by means of digital signatures, substituting legacy components, and enforcing end-to-end encryption and secure boot protocols. The study highlights the importance of a lifecycle-security strategy that extends from deployment to decommissioning of devices, promoting proactive security steps such as continuous monitoring, secure onboarding, data minimization, and accountability on the user's part. By combining technical understanding with real-world security solutions, this research delivers an effective framework for securing next-gen digital twin environments. It highlights the need for stakeholders, from developers and makers to system integrators and policymakers, to integrate cybersecurity into the foundational design and deployment plans of IoT-connected digital twins. As Industry 4.0 evolves at a breakneck pace, no longer can it be optional but a vital necessity for secure and sustainable digital transformation
AI-Driven E-Waste Disassembly System
Get PDFWith the global population steadily increasing, the use of electronic devices is also rising, resulting in a significant accumulation of electronic waste (e-waste). This paper presents an intelligent robotic system designed to autonomously disassemble electronic devices and identify ewaste using a machine learning (ML) model trained entirely from scratch.
Unlike previous disassembly robots that relied on static linebased or rule-based methods—limiting flexibility— our robot leverages a flexible ML-based model trained on diverse device types, allowing it to adapt to various forms of e-waste. Inspired by Apple’s “Daisy” robot, which disassembles iPhones and other Mac devices, our system enhances automation and sustainability in e-waste management.
By deploying our robotic system, we aim to improve recycling efficiency, reduce hazardous impacts on human health, and minimize environmental damage
Impact of high-speed Trains on Vibration-Induced Wear and Fatigue in Rail Tracks
Get PDFThe global expansion of high-speed trains (HSTs) presents significant challenges related to rail track integrity, particularly concerning vibration-induced wear and fatigue that impact the safety and efficiency of railway systems. This study investigates the effects of HST operations on track deterioration, with a focus on the dynamic behavior of rail structures under high-speed movement. Finite Element Analysis (FEA) simulations and rig tests conducted over millions of loading cycles were used to evaluate stress distribution, vibration frequencies, wear rates, and fatigue life of rail tracks. The findings indicate that increased HST activity leads to higher stress and vibration levels, consequently reducing the fatigue life of rail infrastructure. Mitigation strategies explored in this research include enhanced track support systems, advanced rail materials, vibration-damping instruments, and improved monitoring technologies. The results demonstrate that adopting these techniques can significantly reduce vibration effects and improve rail durability. This study offers valuable insights for rail network operators and engineers in designing and maintaining robust infrastructure capable of withstanding the demands of highspeed rail transport
Screw Hydropower Turbine For Power Generation
Get PDFThe intention of any hydroelectric generating station is to convert potential energy associated with the water in a watercourse passing the station into electrical energy. Industry has broad experience in the field of screw pumps and screw pump installations. For nearly 100 years Micro-hydro power plants has designed, manufactured, supplied, erected and maintained many types of screw pump configurations. Since the early eighties Micro-hydro power plants had available a fully automated screw pump selection This project is used to select the optimum screw pump for a particular application with least or zero head. This can produce some electricity using small generator