Editor(s)
Dr. Dariusz Jacek Jakóbczak
Koszalin University of Technology, Poland.

Short Biosketch


ISBN 978-81-972870-4-6 (Print)
ISBN 978-81-972870-0-8 (eBook)
DOI: 10.9734/bpi/strufp/v1


This book covers key areas of
science and technology. The contributions by the authors include hexavalent chromium, waste water, biosorption, butea monosperma bark, water management, machine learning, water harvesting and recycling, artificial neural network, recurrent neural network, crop management, support vector machine, electrical fault, power quality detection algorithms, customer-owned DERS monitoring, intelligent electronic devices, electric vehicles, slow charging concerns, sustainable transportation, urban pollution, WORLD7 model, zirconium, hafnium, superalloys, system dynamics, COVID-19 epidemic, deep learning, nonspecific pneumonia, computed tomography, 3D joint inversion of gravity and magnetic data, geophysical methods, potential theory, newton-gauss iteration, future circular collider, iron-free magnetic system, Cobham’s program, electromagnetic modelling. This book contains various materials suitable for students, researchers, and  academicians in the field science and technology.

 

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Chapters


Addressing Slow Charging Concerns in Current Electric Vehicles: A Comprehensive Analysis

Ashish Saji, Gauri Ansurkar

Science and Technology - Recent Updates and Future Prospects Vol. 1, 30 April 2024, Page 1-13
https://doi.org/10.9734/bpi/strufp/v1/7385B

This study investigates the challenges and potential solutions regarding the adoption of electric vehicles (EVs) amidst increasing pollution and climate change concerns. Through a comprehensive review of existing literature and analysis of survey data from 200 participants, this research sheds light on the factors influencing individuals' readiness to switch to EVs. Results indicate that while there is a growing trend towards EVs globally, significant barriers such as slow charging times hinder widespread adoption. The study explores the impact of battery size, charging infrastructure, and environmental factors on charging speed, proposing innovative solutions such as self-heating batteries and advanced charging technologies to overcome these challenges. By providing insights into the current state of EV adoption and suggesting strategies for improvement, this research contributes to the ongoing discourse on sustainable transportation and environmental conservation.

The long-term supply, price dynamics and recycling of zirconium and hafnium were assessed with the WORLD7 model. The total potential resources have been estimated at least 640-800 million tons of zirconium and 16-22 million tons of hafnium, it is estimated that about 320 million tons of zirconium is extractable. The WORLD7 model simulations show that the supply of zirconium will be in soft scarcity, caused by expensive manufacture and limited production capacity. Hafnium is in demand for superalloys in excess of the present production capacity (90 tons per year in 2022), rather than the size of the hafnium resource (20 million tons). The degree of recycling for zirconium and hafnium is at present far too low. The production and supply of these metals are predicted to peak around 2100, dependent on when the global population peaks and demand peaks. The zirconium resource will be sufficient for metal production for business-as-usual. For increased demand, scarcity may arise from delays in the adaptation of production capacity for zirconium and hafnium. The long-term sustainable extraction of zirconium is equivalent to a total extraction of 210,000 ton/yr of zirconium element for the supply of oxide and metal supply, far below the present extraction. Zirconium extraction would be sustainable at a recycling rate of 86%. For hafnium, the sustainable extraction is 2.5% of the total zirconium amount, or 5.2 tons/yr. Thus, hafnium extraction is not long-term sustainable, unless the recycling rate is better than 94%.

Machine Learning in Water Management

Aditya V Machnoor, Ajayakumar, Mallanna Malagatti

Science and Technology - Recent Updates and Future Prospects Vol. 1, 30 April 2024, Page 59-75
https://doi.org/10.9734/bpi/strufp/v1/97

Water management is a major issue already addressed in most international forums. Water harvesting and recycling are critical criteria for meeting the per capita availability of water (Krishna et.al., 2008). In this regard, we must focus on water management approaches that can be easily implemented across a wide range of applications (Benos et.al., 2021). Amid population increase and varied challenges, there is an urgent need to establish intelligent water management mechanisms for the effective distribution, conservation, and maintenance of water (Safder et.al., 2022). The present work highlights a few important application areas that are essential for precision water management in which artificial neural network (ANN), recurrent neural network (RNN) and random Forest (RF) are some of the most useful developments in machine learning (ML) models (Mokhtari et.al.,2020) in different aspects of water such as wastewater recycling, water distribution, rainfall estimation and irrigation water management that can be used to predict the future scenario. As a result, there is an urgent need to generate datasets and models/algorithms that can be used to deliver solutions for the above-mentioned applications. Machine learning architecture can aid in the development of a smart model for the sustainable use of natural resources (Lowe et.al.,2022), as well as the usage of AI/ML in conjunction with different neural network models and simple statistical analysis to create an effective water management framework to deal all water-related problems.

Electrical Fault and Power Quality Detection Algorithms and Customer-Owned DERs Monitoring with a Cyber Grid Guard System and DLT

Emilio C. Piesciorovsky, Gary Hahn, Raymond Borges Hink, Aaron Werth, Annabelle Lee

Science and Technology - Recent Updates and Future Prospects Vol. 1, 30 April 2024, Page 76-109
https://doi.org/10.9734/bpi/strufp/v1/3810G

In this study, the electrical fault and power quality detection algorithms and customer-owned DERs monitoring use cases were implemented, with a Cyber Grid Guard system and DLT. Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substations, and are associated with customer-owned distributed energy resources (DERs). Data from these IEDs, such as power meters and protection relays, must be kept confidential and of high integrity. Blockchain technology has the potential to increase microgrid resilience by enhancing data sharing security. The growing use of IEDs and customer-owned renewable energy sources (DERs) may make it necessary to connect Distributed Ledger Technology (DLT) with power system applications. We implemented the electrical faulted phase detection and power quality monitoring algorithms with a Cyber Grid Guard (CGG) system using DLT. In addition, the DERs (wind turbine farms) use case and protective relay cyber-event tests were assessed, by using the CGG system with DLT. In the experimental model, the testbed was created by using a real-time simulator and CGG system with power meters/ protective relays in the loop. The data collected from the CGG system and IEDs were compared with the same time stamp source. These results showed the successful assessment of protection, control and monitoring applications using a CGG system with DLT. In the future, power system applications for the ESGT with DERs and the CGG system will be based on executing smart contracts between electrical utilities and customer-owned DERs.

The present study highlights about adsorption of Hexavalent Chromium from Wastewater Utilizing Chitosan Based Activated Carbon Prepared from Butea monosperma Bark. Waste water from many industrial operations contains hexavalent chromium metal ions, which worsen environmental degradation. The literature contains documentation of common physical and chemical treatment methods for getting rid of hexavalent chromium. Not only are these methods costly and energy-intensive, but they also seem to produce a build-up of potentially dangerous byproducts. The current study used chitosan-coated Butea monosperma bark-activated carbon to extract hexavalent chromium from an aqueous solution. The batch experiment was used to investigate the pH effect, time of contact with the adsorbent, adsorbent dose, and the starting concentration of Cr (VI) ions. The pH of the biosorbent was found to be optimum at three for Cr (VI) sorption. The elimination of Cr(VI) from the solution will be accelerated with the rise of contact time.  Up to 94% more Cr (VI) can be extracted for every unit increase in adsorbent dose. As the blood level of Cr (VI) increased, so did the clearance rate. In this study, researchers discovered that a pre-treated biosorbent was an excellent material for eliminating Cr(VI) ions from contaminated water. The final results revealed up to a 73% reduction in percentage elimination of Cr(VI). Adsorbents have confined a wide variety of active sites to saturate at a certain concentration, which justifies this result.

The development of numerical solutions for joint inversion is very important in geophysics, especially in 3D. This is in an effort to produce a fast and accurate interpretation of subsurface conditions. One of the fundamental challenges in geophysics is the calculation of distribution models for physical properties in the subsurface that accurately reproduce the measurements obtained in the survey and are geologically plausible in the context of the study area. This is known as inverse modeling. Completing a 3D joint inversion of multimodal geophysical data requires a lot of processing power. Furthermore, because it involves modeling, iterative computations are needed to obtain a solution that meets the desired qualities, which can result in final results taking days or even weeks to receive. In this paper, we propose a robust numerical solution for 3D joint inversion of gravimetric and magnetic data with Gramian-based structural similarity and structural direction constraints using parallelization as high-performance computing technique which allows us to significantly reduce the total processing time based on the available Random-Access Memory (RAM) and Video Random Access Memory (VRAM) and improve the efficiency of interpretation. The solution is implemented in the high-level programming languages Fortran and Compute Unified Device Architecture (CUDA) Fortran, capable of optimal resource management while being straightforward to implement. Through the analysis of performance and computational costs of serial, parallel, and hybrid implementations, we conclude that as the inversion domain expands, the processing speed could increase from 4x up to 100x times faster, rendering it particularly advantageous for applications in larger domains. We tested our algorithm with two synthetic data sets and field data (Intraplate phreatomagmatic structure, Maar Los Contreras), showing better results than standard separate inversion. The suggested approach is beneficial for joint geological and geophysical interpretation of gravimetric and magnetic data utilized in geophysical exploration, such as prospecting and mineral, ore, and petroleum search. Its use will greatly speed up the data processing process and improve the dependability of physical-geological models.

Application of Deep Learning for Detection and Analysis of COVID-19 Outcomes: Deep-COVID

Muhammad Ibrahim Khalil, Mahwish Kundi, Saif Ur Rehman, Tahani Brarakah Alsaedi

Science and Technology - Recent Updates and Future Prospects Vol. 1, 30 April 2024, Page 156-174
https://doi.org/10.9734/bpi/strufp/v1/11898F

The COVID-19 epidemic, which began on December 31, 2019, with the revelation of nonspecific pneumonia indications in Wuhan, China, swiftly became a significant outbreak, with great ramifications worldwide. The coronavirus epidemic (COVID-19) was growing quickly around the globe. The first acute atypical respiratory illness was reported in December 2019, in Wuhan, China. This quickly spread from Wuhan city to other locations. Deep learning (DL) algorithms are one of the greatest solutions for consistently and readily recognizing COVID-19. Previously, many researchers used state-of-the-art approaches for the classification of COVID-19. In this paper, we present a deep learning approach with the Efficient netB4 model, centered on transfer learning, for the classification of COVID-19. Transfer learning is a popular technique that uses pre-trained models that have been trained on the ImageNet database and employed on a new problem to increase generalization. We presented an in-depth training approach to extract the visual properties of COVID-19 in exchange for providing a medical assessment before infection testing. The proposed methodology is assessed on a publicly accessible X-ray imaging dataset. The experimentation work was conducted at the university using the Anaconda 3 software environment. Performance measurements were employed on the COVID-19 dataset to evaluate and verify our proposed approach. The proposed framework achieves an accuracy of 97%. Our model’s experimental findings demonstrate that it is extremely successful at identifying COVID-19 and that it may be supplied to health organizations as a precise, quick, and successful decision support system for COVID-19 identification. More data may be integrated into future work for improved outcomes, which would enhance the proposed framework even more.

Comparing Detector Magnetic Systems for the Future Circular Hadron-Hadron Collider

Vyacheslav Klyukhin, Austin Ball, Christophe Paul Berriaud, Benoit Curé, Alexey Dudarev , Andrea Gaddi , Hubert Gerwig, Alain Hervé, Matthias Mentink, Werner Riegler, Udo Wagner, Herman Ten Kate

Science and Technology - Recent Updates and Future Prospects Vol. 1, 30 April 2024, Page 175-191
https://doi.org/10.9734/bpi/strufp/v1/3463G

This work describes a detailed study of two possible options for the magnetic system of a Future Circular hadron-hadron Collider detector. The conceptual design study of a Future Circular hadron-hadron Collider (FCC-hh) to be constructed at CERN with a center-of-mass energy of the order of 100 TeV requires superconducting magnetic systems with a central magnetic flux density of an order of 4 T for the experimental detectors. The developed concept of the FCC-hh detector involves the use of an iron-free magnetic system consisting of three superconducting solenoids: the main coil of 10.9 m inner diameter and 18.954 m length with a total current of 69.6 MA-turns that give a central magnetic flux density of 4 T, and two superconducting forward coils of 5.6 m inner diameter and 3.3997 m length with a total current of 12.6 MA-turns each that give a central magnetic flux density of 3.2 T in each coil. A superconducting magnet with a minimal steel yoke is proposed as an alternative to the baseline iron-free design. This design includes the same three coils enclosed in the 22,240-ton steel flux-return yoke. In this study, both magnetic system options for the FCC-hh detector are modeled with Cobham’s program TOSCA. All the main characteristics of both designs are compared and discussed.