Wearable Real Time Health and Security Monitoring Scheme for Coal mine Workers

This paper deals with implementing a supervision system for coal mine and underground workers, which is essential to avoid the workers illness and death. The proposed recovery system consists of all primary aspects of the coal mine and underground areas. This system incorporates a sensor array, GSM, RF and controller modules. ARM 7 (LPC2148) Microcontroller is fully automated measuring system. ARM7 processor is used for measuring the environment parameters with high reliability and accuracy and smooth control by using sensor networks. Consequently, advance detecting crucial conditions the microcontroller starts alerting the mine workers by the alarm system and sends the alert messages to fire and ambulance services by using GSM modem. In addition, the observed parameter's value will be displayed on a PC by using RF (CC2500) module, which is at the control station. At the hazardous situation, this system shows the shortest and available way out path for the workers to move away from the harmful environment. DOI: 10.17762/ijritcc2321-8169.15037

Low-cost sensors technologies for monitoring sustainability and safety issues in mining activities: advances, gaps, and future directions in the digitalization for smart mining

Nowadays, monitoring aspects related to sustainability and safety in mining activities worldwide are a priority, to mitigate socio-environmental impacts, promote efficient use of water, reduce carbon footprint, use renewable energies, reduce mine waste, and minimize the risks of accidents and fatalities. In this context, the implementation of sensor technologies is an attractive alternative for the mining industry in the current digitalization context. To have a digital mine, sensors are essential and form the basis of Industry 4.0, and to allow a more accelerated, reliable, and massive digital transformation, low-cost sensor technology solutions may help to achieve these goals. This article focuses on studying the state of the art of implementing low-cost sensor technologies to monitor sustainability and safety aspects in mining activities, through the review of scientific literature. The methodology applied in this article was carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and generating science mapping. For this, a methodological procedure of three steps was implemented: (i) Bibliometric analysis as a quantitative method, (ii) Systematic review of literature as a qualitative method, and (iii) Mixed review as a method to integrate the findings found in (i) and (ii). Finally, according to the results obtained, the main advances, gaps, and future directions in the implementation of low-cost sensor technologies for use in smart mining are exposed. Digital transformation aspects for data measurement with low-cost sensors by real-time monitoring, use of wireless network systems, artificial intelligence, machine learning, digital twins, and the Internet of Things, among other technologies of the Industry 4.0 era are discussed.The authors are indebted to the projects PID2021-126405OB-C31 and PID2021-126405OB-C32 funded by FEDER funds—A Way to Make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/. The financial support of the Research Department of the Catholic University of Temuco and the Civil Engineering Department of the University of Castilla-La Mancha is also appreciated.Peer ReviewedPostprint (published version

Smart Wearable Gadget for Miners Using IOT

Safety is the most important part of any kind of assiduity is safety. In extreme circumstances, safety-related negligence could result in the destruction of expensive clothing or the loss of human life. Every min-ing diligence adhere to a few basic preventative measures in order to avoid any generally unwelcome wonders. The most important component at this time is communication in order to continuously monitor various pa-rameters and take the appropriate actions as a result to avoid any risks linked with the product or the management of mortal funds. A stable and wide-range effective communication system between personnel in the mine and the control centre must be built in order to increase safety in un-derground mines. The cable communication network technology is inef-fective within underground mines. Here we can tackle the matter of acci-dents which end with death of several people per annum. It is discovered that the speed of fatality within the coal pit industry is almost six times the speed for all private industries. And most of those accidents are because of toxic gases, fires, and a lack of rescue systems. By implementing mine surveillance gadgets, which may be used within the mine and detect the number of various gases, fall, emergency detection and report to them. This article focuses on the design and analysis of the smart wearable gadget for miners in the mining industry using IoT

Evolution of Microcontroller-based Remote Monitoring System Applications

This study reviews the evolution of smart applications of microcontroller-based wireless/wired remote monitoring systems. Rapid developments in science and technology offer the advantages of using integrated embedded chips, microprocessors, and microcontrollers. The use of microcontrollers in industrial processes, such as automobiles, aeronautics, space, robotics, electronics, defense applications, mobile communications, rail transport, and medical applications, is rapidly increasing. This study aims to review the progress of microcomputers in smart remote monitoring and controlling applications for the control and management of different systems using wireless/wired technique

Utilidade de vestíveis tecnológicos na mineração: Use of computacional wearables in mining industry

Os vestíveis tecnológicos estão sendo implementados em muitos domínios como em monitoramento da saúde, práticas esportivas, na vida cotidiana e também na indústria. As novas proposições de organização e planejamento da industrial estão ficando mais complexas e integradas de tal forma que já se evidencia o limiar de uma nova revolução industrial, denominada Industria 4.0. A mineração também está em consonância com o progresso e implantação dessas novas tecnologias. A diversidade de dispositivos vestíveis como óculos virtuais, pulseiras, capacetes e outros dispositivos de formas e tamanhos variados quando dotados de tecnologia permitem alcançar novos patamares de interação homem-máquina e processos produtivos. Este estudo mostra como os vestíveis tecnológicos podem ser usados no contexto da mineração onde o monitoramento fisiológico mostra-se uma oportunidade interessante de melhoria da segurança de trabalho especialmente para área e ventilação subterrânea, que se for combinado com a localização que pode auxiliar na tomada de decisão. Foi buscada uma ampla literatura para entender os dispositivos computacionais vestíveis em desenvolvimento. A pesquisa indica que os vestíveis tecnológicos já estão em desenvolvimento para o setor mineral

An IoT-Aware Smart System Exploiting the Electromagnetic Behavior of UHF-RFID Tags to Improve Worker Safety in Outdoor Environments

Recently, different solutions leveraging Internet of Things (IoT) technologies have been adopted to avoid accidents in agricultural working environments. As an example, heavy vehicles, e.g., tractors or excavators, have been upgraded with remote controls. Nonetheless, the community continues to encourage discussions on safety issues. In this framework, a localization system installed on remote-controlled farm machines (RCFM) can help in preventing fatal accidents and reduce collision risks. This paper presents an innovative system that exploits passive UHF-RFID technology supported by commercial BLE Beacons for monitoring and preventing accidents that may occur when ground-workers in RCFM collaborate in outdoor agricultural working areas. To this aim, a modular architecture is proposed to locate workers, obstacles and machines and guarantees the security of RCFM movements by using specific notifications for ground-workers prompt interventions. Its main characteristics are presented with its main positioning features based on passive UHF-RFID technology. An experimental campaign discusses its performance and determines the best configuration of the UHF-RFID tags installed on workers and obstacles. Finally, system validation demonstrates the reliability of the main components and the usefulness of the proposed architecture for worker safety

NIOSH Mining Program: Evidence Package for 2008-2018

This document contains materials to demonstrate the relevance and impact of the Mining Program\u2019s work in the areas of disaster preparedness and response, ground control, and respirable hazards

PREDVIĐANJE KLJUČNIH PARAMETARA KVALITETE KOKSNOGA UGLJENA U STVARNOME VREMENU POMOĆU NEURONSKIH MREŽA I UMJETNE INTELIGENCIJE

High quality coke is a key raw material for the metallurgical industry. The characteristics of the coal have a significant influence on the parameters of the coke produced and, consequently, on the valuation of coal deposits and the economic assessment of mining projects. Predicting the quality of coking coal allows for the optimisation of production processes, including the planning and management of operations and the early detection of quality problems. In this study, using the principles of a smart mine, it is proposed to determine the quality of coal based on the combination of mining and geological conditions of mineral deposits and its quality indicators. Possible interrelationships between the quality of the coal in the deposit and the characteristics of the final product have been identified. A neural network is used to determine the priority of individual indicators that have a significant impact on the quality of coking coal. An important part of the research is its practical implementation in the conditions of the Jastrzębska Spółka Węglowa SA. Qualitative and quantitative parameters of coking coals were obtained for each mine of the region by the method of sampling and statistical processing of data such as: degree of metamorphism, thickness, deviation of volatile substances, presence of phosphorus, ash content, etc. For their evaluation, the Group Method of Data Handling was used to compare the factors of quality indicators depending on the priority of influence on the final characteristics of the coking coal. Based on the results obtained, it is shown that not all coal quality indicators have a significant impact on the quality of the final product. The study shows that it is possible to predict the main indicators (CRI – Coke Reactivity Index, CSR – Coke Strength after Reaction) of coke quality using neural networks based on a larger number of coal quality parameters and to eliminate parameters that have virtually no influence on the value of the final product. This method can also be used to improve the results of economic valuation of a deposit and to better plan exploration and mining operations.Koks visoke kvalitete ključna je sirovina u metalurškoj industriji. Svojstva ugljena imaju velik utjecaj na kvalitetu proizvedenoga koksa, a time i na vrednovanje ležišta ugljena i ekonomsku ocjenu rudarskih projekata. Predviđanje kvalitete ugljena za koksiranje omogućuje optimizaciju proizvodnih procesa uključujući planiranje i upravljanje procesima te rano otkrivanje problematične kvalitete. U ovoj studiji korištenjem načela pametnoga rudnika predlaže se određivanje kvalitete ugljena na temelju kombinacije rudarsko-geoloških uvjeta ležišta mineralnih sirovina i njegovih pokazatelja kakvoće. Utvrđeni su mogući međuodnosi između kvalitete ugljena u ležištu i svojstava konačnoga proizvoda. Neuronskom mrežom utvrđuje se prioritet pojedinih pokazatelja koji imaju znatan utjecaj na kvalitetu koksnoga ugljena. Važan je dio istraživanja njegova praktična provedba u kompaniji Jastrzębska Spółka Węglowa SA. Metodom uzorkovanja i statističkom obradom podataka dobiveni su kvalitativni i kvantitativni parametri koksnoga ugljena za svaki rudnik kao što su: stupanj metamorfizma, debljina, odstupanje hlapljivih tvari, prisutnost fosfora, sadržaj pepela itd. Pomoću grupne metode obrade podataka uspoređeni su pokazatelji kvalitete ovisno o prioritetu utjecaja na konačna svojstva ugljena za koksiranje. Na temelju dobivenih rezultata pokazalo se da svi pokazatelji kakvoće ugljena nemaju znatan utjecaj na kvalitetu konačnoga proizvoda. Studija pokazuje da je moguće predvidjeti glavne pokazatelje (CRI – indeks reaktivnosti koksa, CSR – čvrstoću koksa poslije reakcije s CO2) kvalitete koksa korištenjem neuronskih mreža na temelju većega broja parametara kvalitete ugljena i eliminirati parametre koji nemaju praktički nikakav utjecaj. na vrijednost konačnoga proizvoda. Ova se metoda također može koristiti za poboljšanje rezultata ekonomskoga vrednovanja ležišta i za bolje planiranje istražnih i rudarskih radova

A Technology review of smart sensors with wireless networks for applications in hazardous work environments

Workers in hazardous environments such as mining are constantly exposed to the health and safety hazards of dynamic and unpredictable conditions. One approach to enable them to manage these hazards is to provide them with situational awareness: real-time data (environmental, physiological, and physical location data) obtained from wireless, wearable, smart sensor technologies deployed at the work area. The scope of this approach is limited to managing the hazards of the immediate work area for prevention purposes; it does not include technologies needed after a disaster. Three critical technologies emerge and converge to support this technical approach: smart-wearable sensors, wireless sensor networks, and low-power embedded computing. The major focus of this report is on smart sensors and wireless sensor networks. Wireless networks form the infrastructure to support the realization of situational awareness; therefore, there is a significant focus on wireless networks. Lastly, the "Future Research" section pulls together the three critical technologies by proposing applications that are relevant to mining. The applications are injured miner (person-down) detection; a wireless, wearable remote viewer; and an ultrawide band smart environment that enables localization and tracking of humans and resources. The smart environment could provide location data, physiological data, and communications (video, photos, graphical images, audio, and text messages)

NIOSH Exposure Assessment Program : evidence package for 2006-2016

Development of tools and strategies for conducting exposure assessment has been a fundamental part of the National Institute for Occupational Safety and Health (NIOSH) since its establishment in 1970. Exposures to hazards at worksites have evolved over time. NIOSH researchers have developed and evaluated exposure assessment methods and direct-reading monitors and methods to address these evolving exposures.The NIOSH Exposure Assessment Program (EXAP) is distributed across both intramural programs (work conducted by NIOSH researchers) and extramural activities (grants and cooperative agreements funded by NIOSH). In recent years, the intramural component of the program has received an average of approximately $12 million in funding and the extramural component have received approximately$14 million in funding.EXAP priorities are largely driven by available workplace and worker health surveillance data and related stakeholder needs. The EXAP priorities have been articulated in two strategic goals: (1) fostering research and providing guidance to develop or improve exposure assessment strategies and (2) developing or improving specific tools or methods to assess exposures of workers to critical occupational agents and stressors. Two of the most emphasized areas of work over the last 10 years have focused on methods development including the enhancement and expansion of the NIOSH Manual of Analytical Methods (NMAM) and on direct-reading exposure assessment methods and sensors. The ultimate goal of the EXAP is to reduce the exposures of workers to hazards.NIOSH_EXA_Evidence_Package_April_2017-508.pdf2017Contract-200-2016-F-899831002