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37th International Conference on Lightning Protection, ICLP 2024

作者： Ferreira, Gabriel A.V.S. Leal, Adonis F.R. DiGangi, Elizabeth A. Lapierre, Jeff Zhu, Yanan Graduate Program in Electrical Engineering Federal University of Para Belem66075110 Brazil Langmuir Laboratory and Physics Department New Mexico Institute of Mining and Technology 801 Leroy Place SocorroNM87801 United States AEM GermantownMD United States

ISBN: (纸本)9783800764174

In general, natural lightning flashes can be distinguished based on the charge that they neutralize (positive or negative), and if they reach the ground (Cloud-to-Ground - CG) or remain in the cloud (Intracloud - IC). On average, about 100 to 200 lightning strikes occur worldwide per second. This equates to roughly 8 million lightning strikes per day. Accurate lightning classification is crucial for identifying various types of thunderstorms and assessing their severity. Modern lightning location systems effectively capture a substantial portion of all lightning that occurs on Earth. However, due to the vast amount of data gathered, accurately classifying each lightning pulse poses a significant challenge. In this study, we evaluate the use of Earth Networks Total Lightning Network (ENTLN) data to develop a lightning classification model based on a Residual Neural Network (ResNet). We develop a new methodology to retrieve lightning electric field waveforms from ENTLN non-uniformly sampled data. The model was optimized to work in real-time, and its performance scores are accuracy = 95,2%, and F1-score = 92,6%. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Lightning protection

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4e Analysis of the Thermal Power Plant of a Coke Integrated Steelmaking Process

SSRN

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SSRN 2023年

作者： Leal, Elisângela Martins Coura, Dimas Pereira Graduate Program in Mechanical Engineering Department of Mechanical Engineering School of Mining Federal University of Ouro Preto Brazil

The steelmaking industry in Brazil plays a vital role in the country's energy landscape, being a major electricity consumer. To remain globally competitive, the industry requires a strategic plan for efficient energy use, low environmental impact, and cost reduction. One promising approach is implementing thermoelectric power plants that utilize waste gases from steelmaking. This study analyzes the energy, exergy, environmental, and economic aspects of such a plant, considering green hydrogen addition achieved through water electrolysis using photovoltaic power. A computational model with Scilab is used, validated with data from a real thermal power plant. Computer simulations using different fuel mixtures show that adding up to 30% hydrogen to blast furnace gas achieves energy and exergy efficiency equivalent to the reference scenario (scenario zero) while reducing fuel costs. Effective energy management within organizations can lead to significant financial gains. The real operating model (scenario zero) exhibited a lower exergetic efficiency of 23.87%, highlighting the need for sustainable solutions. Embracing hydrogen-based technologies can pave the way for a greener, more energy-efficient future in Brazil. © 2023, The Authors. All rights reserved.

关键词： Economic analysis

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Refining the Regional Aftershocks Catalog of the Ambon Earthquake Mw 6.5 on 26 September 2019 using manual picking and a non-linear algorithm: Preliminary Result 9

Refining the Regional Aftershocks Catalog of the Ambon Earth...

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9th Asian Physics Symposium 2021, APS 2021

作者： Kamaruddin, B. Sahara, D.P. Halauwet, Y. Master Program of Geophysical Engineering Faculty of Mining and Petroleum Engineering Bandung Institute of Technology Indonesia Global Geophysics Research Group Faculty of Mining and Petroleum Engineering Bandung Institute of Technology Indonesia Earth Sciences Graduate Program Faculty of Earth Sciences and Technology Bandung Institute of Technology Indonesia Ternate Indonesia Ambon Indonesia

A destructive earthquake with magnitude Mw 6.5 occurred in Ambon, Indonesia, on 26 September 2019. This earthquake generated an aftershock sequence. In this study, we determined the location of the aftershock sequence using a non-linear algorithm from the Non-Linear Location (NonLinLoc) program. The catalog and waveform data were collected using 11 seismographs from BMKG regional network located around the Maluku archipelago. The data was recorded between 26 September and 18 October 2019. We have manually picked the P-and-S wave phase by using Seisgram2K software. In our preliminary result, we have located about 1,300 events of aftershock sequence with about 8,500 phases of P and S waves. Overall, the location of the focus depth of the aftershock sequence is dominantly distributed in between 5 - 25 km. Approximately ∼70% of the distribution of aftershock locations show a North-South (N-S) oriented pattern from west Seram to Ambon Islands. Meanwhile, about ∼20% of the aftershock's locations are distributed around Ambon Island. Our ongoing and future study is to determine accurate hypocentre location derived from double-difference hypocenter relocation with waveform cross-correlation. © Published under licence by IOP Publishing Ltd.

关键词： Location

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Towards a Novel Edge AI System for Particle Size Detection in Mineral Processing Plants 25

Towards a Novel Edge AI System for Particle Size Detection i...

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25th International Conference on Enterprise Information Systems, ICEIS 2023

作者： Cardoso, Flávio Wellb Silva, Mateus Coelho de Meira, Natália F.C. Oliveira, Ricardo Augusto Rabelo Campos Bianchi, Andrea G. Graduate Program in Instrumentation Control and Automation of Mining Processes Instituto Tecnológico Vale Federal University of Ouro Preto Ouro Preto Brazil VALE S.A. Nova Lima Brazil Department of Computer Science Federal University of Ouro Preto Ouro Preto Brazil

ISBN: (纸本)9789897586484

Monitoring and controlling the particle size is essential to reducing the variability and optimizing energy efficiency in mineral process plants. The industry standard utilizes laboratory processes for particle size characterization;the problems that arise here are obtaining representative sample from the bulk and finding a rapid method of particle size assessment. We propose a machine vision concept based on Edge AI architecture and deep convolutional neural algorithms to enable a real-time analysis of particle size, as an alternative to offline laboratory process. The present paper is part of this proposed concept and aims exclusively to validate a deep convolutional neural network algorithm trained from synthetic datasets. The proposed model reached a mean Average Precision (mAP) of 0.96 and processing times of less than 1s. The results demonstrate the feasibility of deep convolutional neural networks for real-time particle size segmentation and establishes the first step towards a novel Edge AI system for particle size measurement in mineral processing plants. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Particle size

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Improved ACO Rank-Based Algorithm for Use in Selecting Features for Classification Models 25

Improved ACO Rank-Based Algorithm for Use in Selecting Featu...

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25th International Conference on Enterprise Information Systems, ICEIS 2023

作者： Delamora, Roberto Alexandre Coelho, Bruno Nazário Sabino, Jodelson Aguilar Vale S.A. Nova Lima Brazil Graduate Program in Instrumentation Control and Automation of Mining Process Federal University of Ouro Preto Instituto Tecnológico Vale Ouro Preto Brazil Artificial Intelligence Center Vale S.A. Espirito Santo Vitória Brazil

ISBN: (纸本)9789897586484

Attribute selection is a process by which the best subset of attributes in a given dataset is searched. In a world where decisions are increasingly based on data, it is essential to develop tools that allow this selection of attributes to be more efficiently performed, aiming to improve the final performance of the models. Ant colony optimization (ACO) is a well-known metaheuristic algorithm with several applications and recent versions developed for feature selection (FS). In this work, we propose an improvement in the general construction of ACO, with improvements and adjustments for subset evaluation in the original Rank-based version by BulInheimer et al. to increase overall efficiency. The proposed approach was evaluated on several real-life datasets taken from the UCI machine-learning repository, using various classifier models. The experimental results were compared with the recently published WFACOFS method by Ghosh et al., which shows that our method outperforms WFACOFS in most cases. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Feature Selection

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Barium-Doped Niobium Pentoxide Enhances the Performance of Zinc-Ion Battery

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SSRN 2024年

作者： Erhardt, Camila Stockey Lubini, Marcieli Rodrigues, Yásmin Alves, Annelise Kopp Graduate Program in Mining Metallurgical and Materials Engineering Universidade Federal do Rio Grande do Sul Av. Osvaldo Aranha 99 Porto Alegre90035-190 Brazil

The capacity of zinc-ion batteries can be significantly enhanced by using other materials in the electrodes to expand their conduction potential. In this study, Nb2O5 was employed in one of the electrodes to enhance its performance. To optimize the characteristics, Nb2O5 was doped with barium (Ba). Niobium pentoxide was synthesized through microwave-assisted hydrothermal synthesis and doped with 1%, 3%, and 5% Ba. Samples of Nb2O5 and its doped variants were characterized using X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM), N2 adsorption isotherms for specific surface area analysis (BET), diffuse reflectance spectroscopy for bandgap determination, and X-ray photoelectron spectroscopy (XPS). The analyses revealed a nanostructured orthorhombic crystalline structure, and a significant increase in surface area and bandgap was correlated with Ba concentration. Batteries with doped Nb2O5 exhibited better stability and storage capacity in electrochemical assays than undoped ones. Specifically, doping with 3% Ba provided the best stability and electrochemical storage performance. This study elucidated the beneficial effects of barium doping on niobium pentoxide, showcasing the promising potential for its application in zinc-ion batteries. © 2024, The Authors. All rights reserved.

关键词： Energy gap

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Modelling Background Seismicity Source for Probabilistic Seismic Hazard Analysis in Manado City, North Sulawesi 9

Modelling Background Seismicity Source for Probabilistic Sei...

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9th Asian Physics Symposium 2021, APS 2021

作者： Zulkifli, M. Triyoso, Wahyu Earth Sciences Graduate Program Faculty of Earth Sciences and Technology Bandung Institute of Technology Bandung Indonesia Global Geophysics Research Group Faculty of Mining and Petroleum Engineering Bandung Institute of Technology Bandung Indonesia Manado Indonesia

This study is used a background source model to calculate probabilistic seismic hazard analysis (PSHA) in Manado City. The gridded seismicity model is applied to characterize the background source model based on the seismicity rate on each grid 0.1 × 0.1 degrees. The results obtained from this study was the contribution of background sources for PSHA in Manado City for the probability of excedence 10% in 50 years is 0.18 ≥ g ≤ 0.19. © Published under licence by IOP Publishing Ltd.

关键词： Hazards

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Whale Optimization Algorithm for structural damage detection, localization, and quantification

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Discover Civil Engineering 2024年第1期1卷 1-22页

作者： Monteiro, Daniele Kauctz Miguel, Letícia Fleck Fadel Zeni, Gustavo Becker, Tiago de Andrade, Giovanni Souza de Barros, Rodrigo Rodrigues Graduate Program in Civil Engineering (PPGEC) Federal University of Rio Grande Do Sul (UFRGS) Porto Alegre Brazil Department of Mechanical Engineering (DEMEC) Graduate Program in Mechanical Engineering (PROMEC) Graduate Program in Civil Engineering (PPGEC) Federal University of Rio Grande Do Sul (UFRGS) Porto Alegre Brazil Graduate Program in Mining Metallurgy and Materials Engineering (PPGEM) Federal University of Rio Grande Do Sul (UFRGS) Porto Alegre Brazil Department of Mechanical Engineering (DEMEC) Federal University of Rio Grande Do Sul (UFRGS) Porto Alegre Brazil Applied Mechanics Group (GMAp) School of Engineering Federal University of Rio Grande Do Sul (UFRGS) Porto Alegre Brazil

This paper introduces an approach for vibration-based damage detection based on matrix updating aided by the Whale Optimization Algorithm (WOA). The methodology uses the Data-driven Stochastic Subspace Identification (SSI-DATA) technique to determine the modal parameters, which are compared with those obtained from both healthy and damaged conditions of the structure. The methodology’s efficacy is assessed through three distinct steps: numerical simulations, experimental data, and real-world data from a bridge. Initially, numerical analyses are conducted on a cantilever beam, a 10-bar truss, and a Warren truss subjected to environmental vibrations with varying damage cases and noise levels. Subsequently, experimental validations are performed on a test system and in the Z24 Bridge. Results from the computational simulations demonstrate the method’s promise to identify, locate, and quantify single and multiple damage cases, even amidst signal noise, variations in the first vibration mode as minimal as 0.015%, and complex structures with 54 elements. Moreover, the matrix updating method utilizing WOA showcased superior accuracy compared to existing techniques in the literature. In addition, the Z24 Bridge example validated the capability of the presented damage detection method to localize structural damage solely based on natural frequencies.

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Study of chlorine removal from shredder residue: Thermal dechlorination and water leaching

Sustainable Chemistry for the Environment

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Sustainable Chemistry for the Environment 2024年 6卷

作者： Manera, Christian Perondi, Daniele Restelatto, Danielle Godinho, Marcelo Vilela, Antônio Cezar Faria Graduate Program in Mining Metallurgical and Materials Engineering Federal University of Rio Grande do Sul (UFRGS) Rio Grande do Sul Porto Alegre Brazil Graduate Program in Process Engineering and Technologies University of Caxias do Sul (UCS) Rio Grande do Sul Caxias do Sul Brazil Graduate Program in Materials Science and Engineering University of Caxias do Sul (UCS) Rio Grande do Sul Caxias do Sul Brazil

Shredder residue (SR) is an inevitable waste in the production of steel by scrap melting, and the presence of chlorine is the main responsible for its non-use of energy. This study aimed to remove chlorine from SR by thermal dechlorination and water leaching. Thermal dechlorination was first studied from a PVC sample to evaluate the optimum parameters of temperature, heating rate, residence time and atmosphere (N₂ or steam). Under optimal conditions for PVC dechlorination, chlorine removals of 73.7 (N₂) and 68.0% (steam) were achieved. Optimal parameters were applied to thermal dechlorination of a Brazilian SR with a chlorine content of 2.12 wt%. Reductions in chlorine content to 0.95 and 0.51 wt% were observed with N₂ and steam atmospheres, respectively. In an additional evaluation (conducted also with the Brazilian SR and in order to investigate the reactor rotation effect), it was observed that the reactor rotation does not influence the release of chlorine during the thermal treatment but modifies the granulometry of the produced solid. Samples obtained from this test were subjected to a water leaching study to further reduce the chlorine content. The parameters studied were the type of water (tap and distilled), granulometry and carbonation (contact with CO₂) in a total of five washing cycles. Carbonation and use of distilled water achieved the highest chlorine removal resulting in a reduction of chlorine content from 1.13 to 0.45 wt%. The obtained results show directions for chlorine removal aiming at the energetic use of shredder residue. © 2024 The Authors

关键词： Carbonation Chlorine leaching Design of experiment Polyvinyl chloride Shredder residue

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Pyrolysis characteristics and dynamics analysis of a coal mine roadway conveyor belt

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Journal of Thermal Analysis and Calorimetry 2023年第11期148卷 4823-4832页

作者： Wang, Weifeng Liu, Hanfei Yang, Bo Zhang, Duo Lyu, Huifei Song, Xianming Shu, Chi-Min School of Safety Science and Engineering Xi’an University of Science and Technology Shaanxi Xi’an710054 China Postdoctoral Research Mobile Station in Mining Engineering Xi’an University of Science and Technology Shaanxi Xi’an710054 China Postdoctoral Program Xi’an University of Science and Technology 58 Yanta Mid. Rd. Shaanxi Xi’an710054 China Mine Rescue Team Yankuang Group Ltd. Co. Shandong Jining273500 China Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan

Coal mine conveyor-belt fires account for numerous mine disasters. Accurate prediction of burning degree is one of the critical factors in preventing and controlling conveyor-belt fires. The sparse qualitative smoke-detection method is challenging to meet the precise early warning of belt fire. Therefore, the Tangkou mine transportation belt was used to analyze TG-DTG curve change late, gas generation law, conversion rate, and apparent activation energy, and other factors at four heating rates via TG-FTIR experiments. The seven characteristic temperatures and their thresholds during belt combustion were experimentally determined, and the process was divided into three stages (I, II, III). The process mainly generated CH4, CO, CO2, and other gases, and the temperature range of various gases was identified. The conversion degree rate of each stage was positively correlated with the temperature. In Stages I and II, activation energy increased with the conversion rate and then decreased. Rate and temperature corresponding to the maximum activation energy were determined. Dynamic compensation effect of the belt combustion process was found. The reactions that could occur at each stage were inferred based on the above-mentioned characteristics;these findings were essential to intelligent comprehensive monitoring and early warning of conveyor-belt fire in a mine laneway. © 2022, Akadémiai Kiadó, Budapest, Hungary.

关键词： Coal

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