# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 5 Discussions  
### Real-Time Ore-Production Prediction Using Optimum DNN Models  
In this study, ore-production predictions were predicted every 30 min during the morning and afternoon operation times on 9, 11, and 14 February, 2019 using optimum DNN models to account for changes in haulage-system conditions that occur in real time. Seven ore-production predictions were performed at start times of 8:30 a.m. (0 min), 9:00 a.m. (30 min), 9:30 a.m. (60 min), etc. up to 12:00 p.m. (210 min) during mornings. Corresponding start times in the afternoon were 1:00 p.m. (0 min), 1:30 p.m. (30 min), 2:00 p.m. (60 min), etc. up to 4:30 p.m. (210 min). The number of trucks dispatched equaled the number of 45-, 60-, and 84-ton trucks dispatched for haulage operations at the start time of each prediction. The dumping-zone utilization, average stay time of trucks in the dumping zone, and their average travel time were set using results of statistical analysis described in Section 3. Figure 12 depicts prediction results obtained for 9 February, 2019. Predicted ore production values during the morning and afternoon operation times along with cumulative ore production up to the end of the last prediction are shown in the figure. Owing to a gradual increase in average stay time in dumping zone B between 8:30 a.m. and 10:00 a.m., corresponding ore production demonstrated a gradual decrease. At 10:30 a.m., one 45-ton truck was removed from dispatch; this illustrates the lowest ore production at the said time. However, the average travel time of trucks decreased from 11:00 a.m. onwards, and consequently, ore production tended to increase again. At 1:30 p.m., one 45-ton and 60-ton truck each were removed from dispatch, thereby resulting in a significant decrease in ore production. However, given the decrease in average truck-travel time from 2:00 p.m. onwards, ore production was observed to increase once again. Figure 11: Inference results obtained using optimum DNN models: (**a**) Ore-production prediction during morning operation (tons); (**b**) ore-production prediction during afternoon operation (tons); (**c**) total ore production in a day (tons). Figure 13 depicts ore-production predictions performed every 30 min during ore-haulage operations on 11 February, 2019. The observed value of accumulated ore production until 12:00 p.m. was less compared to the corresponding predicted value (predicted at 8:30 a.m.) owing to removal of one 84-ton truck from dispatch at 9:00 a.m. Similarly, the accumulated actual ore production until 4:30 p.m. exceeded the afternoon ore production predicted at 1:00 p.m. because some trucks were assigned to loading points close to dumping zones, thereby resulting in shorter average travel times and more-than-expected ore production. Figure 14 depicts the comparison between predicted and actual cumulative ore productions observed every 30 min during haulage operations on 14 February, 2019. The average travel time of trucks decreased at 9:00 a.m. compared to that at 8:30 a.m. This led to prediction of increased ore production. At 11:30 a.m., both the average travel and stay times of trucks decreased, thereby resulting in prediction of even higher ore production. In contrast, during afternoon haulage operations, the average stay time of trucks increased at both dumping zones A and B, and therefore, the DNN model predicted a gradual decrease in ore production. Figure 12: Ore-production predictions performed every 30 min on 9 February, 2019. Figure 13: Ore-production predictions performed every 30 min on 11 February, 2019. Performing multiple ore-production predictions per day affords several advantages over performing only a single prediction in the morning or afternoon at the start of the haulage operation. Haulage operating conditions prevalent at the start of operation may change as the operation progresses.