# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 3 Methods
### Design of DNN Model  
As already mentioned, two DNN models were designed in this study to independently predict morning and afternoon ore productions owing to variations in the truck-haulage operation characteristics during different periods of the day. Figure 5 depicts the structure of a DNN model comprising a single input layer, \(l\) hidden layers, and one output layer. The input and output layers comprise 19 nodes and a single node, respectively. The hidden layer was designed to comprise m nodes in all \(l\) layers.  
Figure 4: Description of deep neural network (DNN) model: (**a**) Typical structure of DNN model; (**b**) principle of value prediction using DNN model.  
In general, truck-haulage systems simulation algorithms analyze how often trucks transport ore from the loading zone to the dumping zone during unit working time to predict ore production [6]. For this purpose, simulation factors (daily working time, number of dispatched trucks, truck loading capacity, etc.) and temporal factors (truck-haulage operation time) are entered into the simulation algorithm [38]. Truck-haulage operation time consists of discrete events, such as ore loading, traveling, ore dumping, spotting, and waiting, and can be defined by the truck cycle time theory proposed by [PERSON] [69]. In the truck cycle time theory, truck cycle time (TCT) can be expressed as the sum of spotting time at the loader (STL), loading time (LT), travel time of the loaded truck (TL), stopping time at the dumping zone (STD), dumping time (DT), travel time of the empty truck (TE), and average delay time (AD), as defined by Equation (3).  
\[\text{TCL}=\text{STL}+\text{LT}+\text{TL}+\text{STD}+\text{DT}+\text{TE}+ \text{AD} \tag{3}\]  
In this study, the input nodes in the input layer were set to the input factors of the truck-haulage system simulation that were modified to fit the system of the study area. The input nodes in the input layer can be divided into truck-haulage operating conditions and haulage-operation time. The output-layer node was either set to ore production during the morning or afternoon haulage-operation period, conditionally.  
Table 1 lists the different input nodes based on their type and measurement unit. To control the scale of the input variables, operation times were expressed in the relative sense. For example, if morning operation starts at 8:30 a.m., the corresponding start time was set to 0 min. Thus, if the morning operation ends at 12:00 p.m., the corresponding end time equals 210 min. The same reasoning applies to afternoon start and end times. The interval between operation times refers to the difference between relative operation start and end times. The number of trucks dispatched by loading capacity equals the number of trucks observed at the operation start time. The average travel time of a truck equals the time required by a truck to travel to the loading point (from the dumping zone), perform ore loading, and return to the dumping zone.  
Figure 5: Proposed DNN-model structure to predict ore production during morning and afternoon haulage-operation periods.