Development of Situational Awareness Platform for the Safety in Mining

Abstract

Mining Industry has several safety requirements as per the regulations laid down by the government and other agencies. The environmental impact of the mining industry is one of the important aspects which needs to be monitored continuously as its impact concerns the health and safety of workers as well as residents. The gas samples from the mine area are generally drawn and checked for oxygen, methane, CO2, and CO gas. More methane gas in the absence of proper ventilation can cause severe health hazards to miners. Any deviation in the composition of the atmosphere especially in methane or CO could be sensed early and any untoward incidents like explosion or fire breakout could be prevented. The center of the study is to monitor, update, analyze and respond to a situation in and around mines. the center of the study conducted. To monitor the situation, sensor networks are utilized. The data from sensor networks helps to monitor the environmental parameters. Wireless Sensor Network (WSN)s are useful in many fields such as coal mine safety monitoring, agriculture management, healthcare, and also for vehicle monitoring. Sensor data collection using different embedded sensors, ARM7 microcontroller, and Zigbee is studied. The use of Arduino microcontroller board for monitoring is also studied. The study is mainly to monitor the parameters in the deep mining environment. The possibility of remotely monitoring updating and controlling the mining environment using Raspberry Pi is studied. The use of sensors and Thingspeak to get the sensor data on the web and to obtain its graph in realtime is explored. Then the controlling of the raspberry pi with the help of XBee communication and remotely controlling with the help of a computer is studied. This is done for the moisture level control using a relay and pump as an example. This method has also other applications. Making use of other types of sensors that are relevant for the mining environment, monitoring and control can be achieved. To analyze the situation, the data of five parameters namely Carbon Monoxide (CO), Sulfur Dioxide (SO2), Particulate Matter 10 (PM10), Particulate Matter 2.5 (PM2.5), and Ozone were analyzed for the year 2018 and 2019 for Singrauli of Madhya Pradesh state, where 10 open pit mines are operating. For Talcher of Odisha state, where deep coal mine is operational, the analysis was performed for the year 2019. The analysis is performed using different machine learning techniques like neural network curve fitting analysis and Self Organizing Maps. Graphical User Interface is developed using Matlab software to analyze the data and to display the environmental situation. This is done for both locations. The analyzed situation is tabulated for both locations.