# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 2 Study Area  
In this study, an open-pit mine owned by Hanil Cement Co., Ltd. in the Republic of Korea (position coordinates: 128\({}^{\circ}\) 19\({}^{\circ}\)58\({}^{\prime\prime}\) E; 37\({}^{\circ}\) 1\({}^{\prime}\) 59\({}^{\prime\prime}\) N), was selected as the investigation location. The mine produces limestone, from which approximately 8.1 million tons of cement is manufactured each year. Figure 1 depicts an aerial view of the selected open-pit mine. The mine is approximately 2-km long, 700-m wide, and 50-m deep. There are two dumping zones in the mine, each with an ore-processing shaft.  
The mine is equipped with 10 shovels, two loaders, and three dozens to facilitate ore production, along with 15 dump trucks with loading capacities of 45, 60, and 84 tons for ore haulage, which is accomplished via real-time dispatch. A production manager determines loading points by considering daily ore-production targets and target ore grades. Additionally, these managers inform truck drivers of  
Figure 1: Aerial view of investigation location (image source: National Geographic Information Institute in South Korea, map datum: World Geodetic System (WGS) 1984).  
their destinations. Truck drivers drive to loading points to load ore into their trucks, and subsequently, drive to the nearest dumping zones. If there is no truck waiting to dump the ore it is carrying, they immediately dump the ore into the shaft; otherwise, they wait for their turn. If the quantity of dumped ore exceeds the shaft capacity, the trucks dump the excess ore into the storage yard within the dumping zone. Next, the truck drivers drive back to loading points, and repeat the ore-hauling operation. The ore dumped into the shaft is subsequently crushed and transferred to the cement plant via use of a conveyor belt.  
The open-pit mine selected in this study is installed with an ICT-based mine-safety-management system that tracks the locations of equipment and mineworkers, as well as monitors operating conditions. Four wireless access points (APs) are installed in the investigation location considered. These wireless APs confirm the equipment location by recognizing tags attached to the equipment and send this information to a web server in real time. An operation manager can check this equipment location and its operating status in real time by visualizing the information sent to the server on his office dashboard (refer Figure 2). Figure 3 depicts the structure of packet data sent to the web server upon identification of equipment tags by wireless APs. Packet data are categorized into tag-recognition date, tag-recognition time, IP address of the identifying wireless AP, and tag-location data. The tag-location data are further divided into start of text (STX), data type, emergency type, tag sequence, tag ID, latitude, longitude, CHECKSUM, and end of text (ETX). The tag sequence indicates packet-data continuity and comprises numbers between zero and 255. Truck locations are expressed in the form of latitude and longitude coordinates, as measured by GPS, recorded in the degree-minutes-seconds (DMS) notation. Approximately 700,000 packet data are sent to the web server per day.  
Figure 3: Packet-data frame derived using information communication technology (ICT)-based mine-safety-management system.  
Figure 2: Mine-safety-management system for fleet management of mine equipment: (**a**) Control center; (**b**) main dashboard.