Abstract
In the challenging environment and dynamic topology of an underground mine, reliable and effective communication is a high-stakes issue. Automation by remote and automated systems has resulted in enhanced real-time response to events and improvements in employees’ workplace health and safety, operational management, energy efficiency, and cost-efficiency. An integrated wireless ad hoc network (WANET) and GIS system are introduced to ensure continuous underground mine communication, monitoring, and control. The proposed system improves health and safety and decreases capital expenditures (CAPEX) and operating expenditures (OPEX). Considering the ZigBee network and ArcGIS applications, the functions for real-time underground monitoring (temperature, humidity, and gas concentration), ventilation system control, and communication in emergency conditions by the surface user would be practicable. The system is fortified with automated or/and remote triggers action plans for measured environmental elements. At normal (green) conditions, the received attributes are within safe limits. In this state, readings are recorded at 30-min intervals, and mining operations continue. At the transient (yellow) condition, the attributes are between average and threshold value limits. In this situation, trigger actions are set up to automatically switch on the auxiliary fans and texting messages to authorized personnel and reduce the reading’s intervals to 15-min. At unsafe (red) conditions, the progress of measurements is more significant than threshold values. In this state, the system sends text messages to all underground personnel to immediately evacuate hazardous levels, and 5-min reading intervals are monitored. Additionally, the WANET-incorporated GIS runs multi-user operations and 3D monitoring to understand the environmental attributes and miners’ conditions in underground mines. It plays a critical role in growing Artificial Intelligence (AI) in the mining industry by mitigating risks and reducing costs.
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Moridi, M.A., Sharifzadeh, M. (2022). Advanced Analytics for Autonomous Underground Mining. In: Soofastaei, A. (eds) Advanced Analytics in Mining Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-91589-6_23
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DOI: https://doi.org/10.1007/978-3-030-91589-6_23
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