# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 3 Methods
### Inference Using DNN Model  
Estimates of morning and afternoon ore-production amounts in February 2019 were obtained using optimized DNN models developed and trained as described above. The first week of February 2019 was a week of national holidays in the Republic of Korea, thereby resulting in reduced ore production. The daily average ore production in the first week of February was observed to have been reduced by approximately 10,000 tons compared to the previous month. The utility of the proposed DNN model with regard to accurate ore-production predictions at the identified mine location was assessed by applying the same to perform the said predictions during morning and afternoon operation times over five days: 9, 11, 12, 13, and 14, February 2019. Ore production in the morning accounted for the amount of ore mined in 210 min between 8:30 a.m. and 12:00 p.m. Accordingly, afternoon ore production accounted for mining operations performed between 1:00 p.m. and 4:30 p.m. Input data for DNN models were generated by processing approximately 280,000 packet-data samples recorded over the above-mentioned five days. Table 5 lists variable values provided as input to the two DNN models. Based on these input data, it was revealed that the number of 45-, 60-, and 84-ton trucks dispatched for operation on the morning of 9 February amounted to four, two, and four, respectively, and that all these trucks used dumping zone B. Additionally, it was revealed that the average stay time of the trucks in dumping zone B, the average travel time, and calculated ore-production were 2.83 min, 10.6 min, and 6819 tons, respectively. To account for the error-increase rate between the calculated and observed values, the inference error was evaluated by the percentage error (PE), as defined by Equation (13):  
\[\text{PE}=\ \frac{\text{y}_{\text{i}}-\hat{\text{y}}_{\text{i}}}{\text{y}_{ \text{i}}}\times 100\ (\%) \tag{13}\]  
Figure 8: Process of optimizing DNN model configuration.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{11}{c}{**Morning Operation Time**} & \multicolumn{11}{c}{**OP1**} \\ \cline{2-13}  & & & & & & & & & & & & & & & & & & & & & & & & \\ \hline
2/9 & 0 & 210 & 210 & 4 & 2 & 4 & 45 & 60 & 84 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 2.83 & 0 & 10.6 & 6819 \\
2/11 & 0 & 210 & 210 & 2 & 3 & 5 & 45 & 60 & 84 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 6.21 & 0 & 12.33 & 4791 \\
2/12 & 0 & 210 & 210 & 3 & 3 & 5 & 45 & 60 & 84 & 0.09 & 0.91 & 0 & 1 & 0 & 1 & 8.95 & 3.04 & 13.2 & 12.08 & 7467 \\
2/13 & 0 & 210 & 210 & 3 & 2 & 4 & 45 & 60 & 84 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 4.44 & 0 & 9.36 & 7686 \\
2/14 & 0 & 210 & 210 & 5 & 3 & 4 & 45 & 60 & 84 & 0.57 & 0.43 & 0.44 & 0.56 & 0 & 1 & 3.23 & 1.81 & 15.08 & 9.87 & 9075 \\ \hline \multicolumn{13}{c}{**Afternoon Operation Time**} & \multicolumn{11}{c}{**OP1**} \\ \cline{2-13}  & & & & & & & & & & & & & & & & & & & & & & \\ \hline
2/9 & 0 & 210 & 210 & 3 & 2 & 4 & 45 & 60 & 84 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 4.35 & 0 & 11.27 & 6033 \\
2/11 & 0 & 210 & 210 & 3 & 2 & 4 & 45 & 60 & 84 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 3.46 & 0 & 10.12 & 6501 \\
2/12 & 0 & 210 & 210 & 4 & 3 & 5 & 45 & 60 & 84 & 0.1 & 0.9 & 0 & 1 & 0 & 1 & 13.88 & 4.4 & 7.4 & 10.69 & 6117 \\
2/13 & 0 & 210 & 210 & 3 & 2 & 4 & 45 & 60 & 84 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 4.56 & 0 & 10.58 & 4872 \\
2/14 & 0 & 210 & 210 & 4 & 3 & 4 & 45 & 60 & 84 & 0.72 & 0.28 & 0.74 & 0.26 & 0 & 1 & 4.09 & 1.5 & 10.41 & 11.91 & 8256 \\ \hline \multicolumn{13}{c}{\({}^{1}\)One production.} & \multicolumn{11}{c}{**} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data provided as input to DNN models for predicting ore production during morning and afternoon operation times.