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All Journal International Journal of Applied Power Engineering (IJAPE) Bulletin of Electrical Engineering and Informatics International Journal of Robotics and Control Systems Makara Journal of Technology
Kumar, Rakesh
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Combining solar panels with plants for sustainable energy and food production: state of the art Panda, Sampurna; Kumar, Rakesh; Panda, Babita; Panda, Bhagabat; Raj, Ashish
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i2.pp434-441

Abstract

The need for alternative energy sources becomes extensive because of the escalating cost of fossil fuels. The goal of this paper is to examine the effectiveness of combining photovoltaics and agriculture for better yield. Photovoltaic (PV) solar plants will compete with farms for available land. In this study, the methodologies are discussed how it is possible to maximize land utilization by placing solar arrays and food crops on the same plot of land. The term is proposed "agrivoltaic system" to describe this setup. Conventional solutions (discrimination of agricultural and energy extracting) were compared to two agrivoltaic schemes with varying density of PV arrays using land equivalent ratios. We utilized a crop model to simulate the amount of sunlight reaching the crop from an array of solar panels and to speculate on the yield reduction that would result from the partial shading. These early findings suggest that agrivoltaic systems may be highly effective; the two densities of PV panels were anticipated to boost worldwide land production by 73%. One possible explanation for the success of these hybrid systems is the presence of facilitation mechanisms analogous to those seen in agroforestry. At the end it is suggested that in places where arable land is rare, new solar plants may find it beneficial to produce both power and food.
Performance analysis of convolutional neural network architectures over wireless capsule endoscopy dataset Kaur, Parminder; Kumar, Rakesh
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5858

Abstract

Wireless capsule endoscopy is one of the diagnostic methods used to record the video of the gastrointestinal tract. The endoscopy capsule stays in the digestive system for at least eight hours. It is difficult for gastroenterologists to examine such a lengthy video and identify the ailment. Convolutional neural networks (CNN) are a powerful solution to several computer vision problems. CNN can speed up the reviewing time of the recorded video by classifying video frames into various categories. The primary emphasis of this research paper is to examine and evaluate the performance of three different CNN architectures-VGG, inception, and MobileNet-in classifying the disease. Experimental results demonstrate that MobileNetV2’s accuracy is 91%, whereas InceptionV3 and VGG16 have an accuracy of 94% which is better than the accuracy of MobileNetV3. However, MobileNeV2 performed relatively better than the other CNN models in terms of computational time and cost. The model’s F-score, precision, and recall values are computed and compared also.
Multi-Objective Particle Swarm Optimization for Enhancing Chiller Plant Efficiency and Energy Savings Bhardwaj, Yogesh; Shah, Owais Ahmad; Kumar, Rakesh
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1501

Abstract

This study aims to enhance operational efficiency in chiller plants by implementing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The primary objectives are to simultaneously reduce energy consumption and increase cooling efficiency, addressing the challenges posed by variable environmental and operational conditions. Employing the MOPSO algorithm, this research conducts a detailed analysis using real-time environmental data and operational parameters. This approach facilitates a dynamic adaptation to changes in ambient temperature and electricity pricing, ensuring that the algorithm's application remains effective under fluctuating conditions. The application of MOPSO has resulted in significant reductions in energy use and improvements in cooling efficiency. These results demonstrate the algorithm's capacity to optimize chiller plant operations dynamically, adapting to changes in environmental conditions and operational demands. The study finds that MOPSO's adaptability to dynamic operational conditions enables robust energy management in chiller plants. This adaptability is crucial for maintaining efficiency and cost-effectiveness in industrial applications, especially under varying environmental impacts. The paper contributes to the field by enhancing the understanding of how advanced optimization algorithms like MOPSO can be effectively integrated into energy management systems for chiller plants. A novel aspect of this research is the integration of real-time data analytics into the optimization process, which significantly improves the sustainability and operational efficiency of HVAC systems. Furthermore, the study outlines the potential for similar research applications in large-scale HVAC systems, where such algorithmic improvements can extend practical benefits. The findings underscore the importance of considering a broad range of environmental and operational factors in the optimization process and suggest that MOPSO's flexibility and robustness make it a valuable tool for achieving sustainable and cost-effective energy management in industrial settings.
Heat Transfer Enhancement in Nanofluid Flows Augmented by Magnetic Flux Arjun, Kozhikkatil Sunil; Kumar, Rakesh
Makara Journal of Technology Vol. 27, No. 3
Publisher : UI Scholars Hub

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Abstract

Heat transfer enhancement could be realized using magnetohydrodynamics together with nanofluids specifically in flow micro-convection in a microtube, flow past a vertical porous plate, and square duct flow with discrete heat sources numerically. A critical value for the Rayleigh number, a maximum value for the magnetic field strength, a low Reynolds number, and volume concentrations exists for thermal enhancement to simulate nanofluid flow in a microtube. Heat transfer enhancement is observed with a reduction in the magnetic field strength in a flow past a heated porous vertical plate. Alumina nanofluid subjected to Hartmann number 10 can boost 81% enhancement in Nusselt number in a square duct at lower Reynolds number using three discrete heat sources under the impact of thermal and solutal buoyancy. A 4% increase in the cooling effect near the center of the last heat source in a nanofluid flow is of practical use in hot spot cooling.
Co-Authors Arjun, Kozhikkatil Sunil Bhardwaj, Yogesh Kaur, Parminder Panda, Babita Panda, Bhagabat Panda, Sampurna Raj, Ashish Shah, Owais Ahmad