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- Pandya, Sharnil, Researcher, 1984-, et al.
(author)
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Pollution Weather Prediction System : Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living
- 2020
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In: Sensors. - : MDPI. - 1424-8220. ; 20:18
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Journal article (peer-reviewed)abstract
- Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.
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| 2. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(author)
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Smart epidemic tunnel : IoT-based sensor-fusion assistive technology for COVID-19 disinfection
- 2020
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In: International Journal of Pervasive Computing and Communications. - : Emerald Group Publishing Limited. - 1742-7371 .- 1742-738X. ; 18:4, s. 376-387
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Journal article (peer-reviewed)abstract
- Purpose – The purpose of the presented IoT based sensor-fusion assistive technology for COVID-19disinfection termed as “Smart epidemic tunnel” is to protect an individual using an automatic sanitizer spraysystem equipped with a sanitizer sensing unit based on individual using an automatic sanitizer spray system equipped with a sanitizer sensing unit based on human motion detection.Design/methodology/approach – The presented research work discusses a smart epidemic tunnel that can assist an individual in immediate disinfection from COVID-19 infections. The authors have presented a sensor-fusion-based automatic sanitizer tunnel that detects a human using an ultrasonic sensor from the height of 1.5 feet and disinfects him/her using the spread of a sanitizer spray. The presented smart tunnel operates using a solar cell during the day time and switched to a solar power-bank power mode during night timings using a light-dependent register sensing unit.Findings – The investigation results validate the performance evaluation of the presented smart epidemic tunnel mechanism. The presented smart tunnel can prevent or disinfect an outsider who is entering a particular building or a premise from COVID-19 infection possibilities. Furthermore, it has also been observed that the presented sensor-fusion-based mechanism can disinfect a person in a time of span of just 10 s. The presented smart epidemic tunnel is embedded with an intelligent sanitizer sensing unit which stores the essential information in a cloud platform such as Google Fire-base. Thus, the proposed system favours society by saving time and helps in lowering the spread of coronavirus. It also provides daily, weekly and monthly reports of the counts of individuals, along with in-out time stamps and power usage reports.Practical implications – The presented system has been designed and developed after the lock-down period to disinfect an individual from the possibility of COVID-19 infectionsSocial implications – The presented smart epidemic tunnel reduced the possibility by disinfecting an outside individual/COVID-19 suspect from spreading the COVID-19 infections in a particular building or apremise. Originality/value – The presented system is an original work done by all the authors which have been installed at the Symbiosis Institute of Technology premise and have undergone rigorous experimentation and testing by the authors and end-users.
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| 3. |
- Sur, Anirban, et al.
(author)
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Influence of bed temperature on performance of silica gel/methanol adsorption refrigeration system at adsorption equilibrium
- 2020
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In: Particulate Science and Technology. - : Taylor & Francis. - 0272-6351 .- 1548-0046. ; 39:5, s. 624-631
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Journal article (peer-reviewed)abstract
- This paper presents a thermodynamic model for predicting the cooling performance of a single-bed single-stage silica gel/methanol adsorption refrigeration system. Solar heat was collected through flat plate collectors and then stored in a hot water tank. Desorber bed was heated by the hot water from the hot water tank. The temperature of the desorber bed was varied from 65 °C to 85 °C, and its effect on system performance was observed. A numerical model was developed on the basis of mass and energy balance equations, adsorption equilibrium, and kinetic equations (Dubinin–Astakhov equation) for predicting the performance of the adsorption refrigeration system under the said conditions for four major parts (adsorber, desorber, condenser, and evaporator). A programing code was written in FORTRAN for solving these equations under pre-defined material properties, and the simulation result was observed. A refrigeration effect of 577 kJ with a coefficient of performance (COP) of 0.38 could be produced for a maximum bed temperature of 90 °C, which was restricted up to 90 °C because after this temperature, the input increases more than the refrigeration effect, and COP values reduce due to a reduction in desorption mass.
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