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(Volume: 2, Issue: 6)
Efficiency Optimization of Induction Motors for Global Energy Conservation
All the industries worldwide aim for improved production with utmost consideration on associated machinery costs and power utilization. The AC asynchronous induction motors are better known to satisfy these industrial goals and the reasons can be attributed to its high-power factor and efficiency, self-starting rotor design, the absence of brushes or slip rings with enhanced motor life and reduced wear as well as tear. However, all these benefits are enjoyed within rated loads of operation. So, how will be the induction motor’s functionality at partial loads? This is actually a scenario, where the induction motor load demand is smaller than its supply and hence, the power factor and the efficiency of the motor is very low. In addition, if the motor is to be operated at rated torques at partial loads, the current drawn will be more. Now, imagine how massive will be the power consumed by a single industrial plant that has a vast number of operating units like, high duty electric machines, conveyers, pumps and so on. So, to reduce the global electrical energy intake by the industrial plants and to improve the motor efficiency, changes in motor design and adaptive control of rotor speed at varying loads becomes a mandate. Various researchers have contributed towards this context in past years. One of the researches identified in Sensors (MDPI), 22(7), by Niraj Kumar Shukla, Rajeev Srivastava and Seyedali Mirjalili do support this notion. Here, the efficiency optimization was made possible using a speed control strategy, which involved an adaptive control of the induction motor drive using the Scrounger Strikes Levy-based dragonfly algorithm (SL-DA). The researchers state that other meta-heuristic algorithms can also be used for proper tuning and efficiency enhancement. Hence the individuals, who are interested in developing highly efficient induction motors to support industrial plants in electrical energy conservation and operational cost minimization has a great path ahead in this domain. Image Courtesy: www.vecteezy.com
Radiomics Feature-Based Deep Learning Approach Aids COVID-19 Diagnosis
It is merely impossible to find persons with no knowledge on COVID-19, the most highly contagious disease that startled the entire globe from its onset on December 2019. Though we have come across three and a half years of the pandemic’s aggressive and adverse effects from various disease variants like, Alpha, Beta, Gamma, Delta and Omicron through intensive medical guidance, care and support, the emergence of new variants like “Pirola” in 2023 again question the life and livelihood of humans!!! Hence, disease prevention through self-care and early diagnosis can only help in this regard to minimize or even eliminate the morbidity or mortality rates from COVID-19 variants. When speaking about early diagnosis, there are manual approaches like, taking a Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) test or an expert’s identification of the disease directly from chest CT (Computed Tomography) images. However, they are much cumbersome and unreliable because the collected samples might be deficit to reveal the presence of the disease and the CT images might show structural similarity with other lung-related ailments or possess unclear data due to image acquisition changes, consuming hours to predict the disease for a single patient!!! Hence, automatic diagnosis procedures powered by Artificial Intelligence (AI), Machine Learning (ML) approaches and medical expert’s guidance becomes the sole preference to leap over the manual approaches’ shortcomings. One such diagnosis of COVID-19, with special attention towards radiomics feature extraction and deep learning-based classification, was presented by Dr. V. Anantha Natarajan, Dr. Syed Jahangir Badashah, Srilakshmi CH, Dr. M.Sunil Kumar and Jangam Bala Srinivasa Rao in Vol. 12, Issue: 01 of A Journal for New Zealand Herpetology. As per the authors, the radiomics features involving texture, shape, size or gray-level dependence enable fast and effective COVID diagnosis, since the hazy or cloudy non-transparent COVID- affected regions in the chest CT images can be easily discriminated from their normal or other ailment-related counterparts. Hence, their research might serve as an initiative for the future researchers, who aim at developing automated, time-efficient, reliable and more generalized early diagnosis procedures for novel disease identification, in an effect to plan for better medications and disease eradication. Image Courtesy: www.freepik.com
FIBER OPTIC APPROACH FOR MONITORING FROZEN SOIL
Have you ever heard the term ‘Frozen Soil’? It refers to the ground with micro or macro cracks, which result from the freezing of the water particles in the soil, as the climatic temperature drops below zero degree during changing climatic conditions. However, the temperature is not always going to be low. There are seasons, wherein the frozen soil undergoes thawing, decreasing the soil strength to cause a multitude of hazards such as landslides, collapses, and debris flows. These threats pose significant risks to local residents and the operation of critical infrastructures such as, roads, railways, oil and gas pipelines, and hydropower projects!!! Hence a high-precision approach, which can aid in the comprehensive in-situ monitoring of moisture migration and ice-water phase transitions in the frozen soil during the freeze-thaw cycles, becomes essential to prevent any devastating geological disasters in the colder regions. Professor Hong-Hu Zhu, Dean of the Institute of Earth Exploration and Sensing at Nanjing University, and his team have proposed a method in the Journal of Hydrology, 622(A) that combines active and passive distributed temperature sensing techniques, which can aid in this regard. Their specially designed temperature-sensing fiber optic cable has allowed high-precision, minimally- invasive and fully- automated monitoring of the temporal and spatial distribution of moisture in the seasonally- frozen soil using key physical quantities of frozen soil such as, temperature, ice content and unfrozen water content along the cable length. In collaboration with the Sun Yat-sen University, North China Institute of Science and Technology, and the Northwest Institute of Eco-Environment and Resources of the Chinese Academy of Sciences, Professor Zhu's research group have established field observation stations in multiple locations. Long-term monitoring of parameters such as, soil temperature, water content, ice content, atmospheric temperature, humidity, rainfall and solar radiation has allowed them to investigate the thermo-hydro-mechanical coupling mechanisms of the seasonally- frozen soil and the evolution patterns of relevant geohazards. Their work ensures greater technical support for future engineering projects and sustainable development in cold areas. The researchers with interest in automated frozen soil monitoring should make sure that their approaches alert and offers prior safety, even at challenging geological conditions. Image Credit: www.freepik.com
Air Pollutant Monitoring Using Ground and Satellite-based Data
Air pollution has silently grown to be the lifetaker of people with its toxic pollutants like, particulate matter, nitrogen dioxide, carbon monoxide, sulphur dioxide, lead and similar other pollutants in air. The World Health Organization (WHO) states that about 7 million people die prematurely in a year due to air pollution, since it causes simple to severe chronic respiratory ailments and associated health-related issues. Though several measures have been taken to keep the pollution in control and numerous researches do support in finding or minimizing the concentration of various air pollutants, novel approaches are still anticipated to find the accurate concentration of the pollutants from the invisible air. Normally, ground-based air pollutant monitoring using monitoring stations, gas sensors, filtering-based sampling or chemical absorption help in local air quality assessments and to take regulatory measures. However, it fails at rural or remote areas and regions with greater geographic coverage because of the high cost of installations and inflexible or insecure mode of measurement. This is the situation, where remote sensing- based air pollutant monitoring works well. However, providing accurate air quality details at varied times is not always possible with remote sensing data because smog and fog looks really the same!!! An UK- based research in the Remote Sensing of Environment, Elsevier, has attempted to eliminate these issues by proposing a multi-modal AI network that employs ground-based as well as satellite-based data. Constructing a dataset from the European Space Agency (ESA) Copernicus project satellite that include multi-spectral satellite imagery from Sentinel-2, low-resolution tropospheric NO2 concentration data from Sentinel-5P satellite and the tabular data that exploits some important information about the ground measurements centres (such as altitude, population density, station and area type), the researchers have used an Artificial Intelligence (AI)- based approach to predict the NO2, O3 and PM10 pollutant concentrations. The researchers’ motive was to highlight the pollutant distribution and to cause a change in the societal and the industrial behaviours. Future researchers with the same motive can render similar novel approaches for monitoring and controlling the air pollution levels. Image Credit: www.pexels.com
Automate Cableway Transportation Systems with Improved Safety
For the people living in terrain region with high mountains, valleys or inclined slopes, where road or railway construction is implausible, the only reliable mode of transport is the cableway transport. Well-known as aerial tramways, cable cars or funicular, the aim of cableway transportation is to aerially transport people from their start to destination through cables and cabins or gondolas connected to it. In recent years, this transportation mode has turned out as a public transport, in an effect to control air pollution and production/ maintainance cost, for easier installation and rapid transport of people in hilly or traffic-prone regions and to promote tourism. However, there are also drawbacks in terms of safety assurance and efficiency. Umberto Petruccelli and Diego Fabrizio from Università della Basilicata -Potenza, Italy, have critically analyzed the regulatory, technological and operational innovations of cableway transport in the past ten years, so as to reveal their effects on safety, design features, performances and cost. In their research being supported by MIUR PON R&I 2014- 2020 Program (project MITIGO, ARS01_00964), they have discussed about the design choices of the cableway system based on user evacuation during installation downtime/ failure. Further, they recommend minimum number of operating personnel and minimum energy demand to cut down the costs. In all, the researchers aim for an automated cableway transport system with greater number of passengers per transport, increased stability during strong winds, automation with no operating personnel involvement during transport system failures with greater safety assurance. The upcoming researchers too can develop automated cableway transport systems with all the above-mentioned features, prioritizing safety over the others. Image Courtesy: www.unsplash.com
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