Tech) four-year engineering degree courses in five disciplines. The college is affiliated to Maulana Abul Kalam Azad University of Technology(MAKAUT)..
The shift towards sustainable agriculture has become increasingly necessary due to the increasing demand for food globally, environmental degradation, and the shortage of labor. Robotics, Artificial Intelligence (AI), Internet of Things (IoT), and data-driven technologies are a new category of technologies that have become central to agricultural transformation through automation. The review paper presents a synthesis of the world's innovations in agricultural automation over the last five years, with a specific focus on the development of India and the peculiarities of its situation. Even though the world has made tremendous steps, the major research gap is in the application of automation technologies in various agroecological, socio-economic, and policy settings, especially in emerging economies such as India. This review takes a multidimensional approach through a systematic review of the scholarly literature, government reports, and industrial case studies as a means of assessing technological advancements, adoption forces, economic feasibility, environmental assessment, and socio-political preparedness. According to the findings, developing countries are crippled by factors such as high prices, poor infrastructure, and poor policy incentives, when compared to developed countries that have been keen to automate to achieve the full utilisation of resources and crop yields. Precision agriculture, drone uses and autonomous machines are pilot projects in India, but remain fragmented. The review concludes that in order to make sure that automation generates sustainable agricultural change in India, a local, inclusive strategy has to be developed in terms of attention to scalable innovation, capacity building, and well-built institutional frameworks.
Research interest in fuel blending technologies has grown rapidly as the demand for sustainable energy increases. Although oxygenated fuels offer significant potential for reducing greenhouse gases and air pollution, their unique physicochemical properties pose challenges for atomization and combustion. The blending of biofuel with conventional fuel is a practical way to enhance combustion efficiency and decrease emissions of internal combustion engines. Blended fuels change many of the key fuel properties, including viscosity, density, surface tension, volatility, cetane number, oxygen content, and lower heating value. These properties directly influence the process of spray penetration, droplet size, evaporation, air–fuel mixing, ignition delay, heat release process, and pollutant formation. However, most of the previous reviews have been focused on fuel properties, atomization, combustion, and emission separately without a good integration between them. This review focuses on the correlation between fuel blending, spray atomization, combustion characteristics, and emissions. The conventional and advanced fuel blends such as: biodiesel, alcohol fuels, hydrogen-enriched fuels, co-solvent-assisted fuels, and nanoparticle-based fuels, are discussed. The review also combines technical results, the bibliometric patterns, and correlation interpretation of the results, to establish the main research themes and the new directions of the research. It has been shown in the literature that optimized blending can help to enhance the atomization quality, increase combustion stability, and reduce emissions of carbon monoxide, hydrocarbons, soot, smoke, and particulate matter. But there are still challenges with nitrogen oxides control, long-term blend stability, phase separation, injector deposits, material compatibility, fuel system durability, and combustion instabilities. Further studies are needed on the advanced design of the atomizers, predictive modeling, stable multi-component blends, and optimization of the fuel–engine system for sustainable combustion and reduction of emissions to cleaner and more efficient combustion systems.
The engineering demands of power generation, aerospace propulsion, and chemical processing are now demanding the uniting of dissimilar metallic systems to achieve the best structural performance and economical performance. The most problematic in this area is the joining of ferritic martensitic (FM) steels, such as P91 or P92, and austenitic stainless steels (ASS) such as 304L or 316. These kinds of joints are prone to metallurgical instabilities, particularly the migration of carbon during welding and post-weld heat treatment (PWHT), the development of brittle intermetallic compounds (IMC), and hot cracking. To overcome these systemic weaknesses, the Eutectic High-Entropy Alloys (EHEAs), especially the AlCoCrFeNi2.1 system, are used as special interlayers. At the same time, the development of “nano- fluxes” in Activated Tungsten Inert Gas (A-TIG) welding, including nanoparticles like titanium dioxide (TiO2) and graphene nanoplatelets (GNPs), has become a niche area of research to improve penetration depth and grain structure. This is a review paper that tries to offer useful information in the field under investigation.
Industry 4.0 represents a transformative shift in the field of manufacturing and industrial processes that are associated with the interconnection of cyber-physical systems, the Internet of Things (IoT), and sophisticated data analysis. In this regard, predictive maintenance has become a vital approach to improve the efficiency of the operation process, minimize downtimes, and increase the lifetime of industrial resources. With the large volumes of data created each second by the IoT-based sensors, predictive maintenance uses data mining algorithms to find the trends and anomalies that can predict the possibility of equipment malfunction before it happens. Compared to the traditional reactive or scheduled maintenance, which is inactive and reactive, this proactive approach allows for smarter decisions and allocation of resources more optimally. An end-to-end data mining system for predictive maintenance in an Industry 4.0 IoT environment includes data collection over a variety of sensor networks, data pre-processing to guarantee the quality of the data, scalable storage systems, sophisticated machine learning algorithms to make accurate predictions, and visualization tools to facilitate maintenance scheduling and operational control. With the help of these elements, industries will be able to move to more robust and intelligent maintenance systems based on the objectives of Industry 4.0.
