## 3 Results and Discussion
### ANN Modeling for Heavy Metals' Concentration  
Unlike pH and Eh, heavy metals have a diverse range of concentrations. Therefore, the X- and Y-axes are diverse depending on experimental conditions. Since the model's optimization process is based on MSE, it was shown to be more predictable for high concentrations of results that have a significant impact on MSE. Figure 4: Observed and ANN-predicted Eh changes with a topology of 9:8:1. The numbers are from Table 1, while the red number is the result from the data for further verification. Figure 3: Observed and ANN-predicted pH changes with a topology of 9:15:1. The numbers are from Table 1, while red number is the result from the data for further verification. The Cu concentration results and the prediction by ANN model are shown in Figure 5. Table 2 shows that, under Conditions #7 and #8, copper content was higher than other conditions, resulting in relatively high leaching of copper under both conditions. Unlike pH and Eh, the results change relatively significantly with changes in temperature and dosage. Overall, the higher is the temperature and the higher is the dosage, the greater is the amount of heavy metals being leached. For additional experimental conditions, the trend is similar to the Pyrite in Condition #8. However, this ANN model, which reflects the mineral composition, was able to confirm that the predictability was significantly different. To overcome these limitations, experiments were conducted in more diverse compositions of minerals, reflecting these results to ANN training. The Pb concentration results and the prediction by ANN model are shown in Figure 6. Unlike the results in copper, Pb does not increase leaching as the content of Pb increases. Compared to Condition #2, the amount of elution was lower despite the high Pb content of Condition #3. We could also confirm that the minerals of Conditions #5 and #3 are the same, but the leaching of Condition #5 is much greater. For the Pb concentration data, the degree of value varies, but there are few data, so it seems to fit well regardless of the conditions. The results of Condition #2 suggest that elution increases as the temperature rises from 42 to 62 \({}^{\circ}\)C. For this reason, under the additional experimental conditions of 52 \({}^{\circ}\)C conditions, similar to those of Condition #2, the ANN model was determined to have leaching between 42 and 62 \({}^{\circ}\)C. However, the actual results are similar to those at 42 \({}^{\circ}\)C. In other words, the effect of temperature is not linear, so it is necessary to look further at the effect of temperature. The Zn concentration results and the prediction by ANN model are shown in Figure 7. In the case of Zn, the higher is the content of Zn in the mineral, the more likely it is to be leached. However, while Condition #2 had the highest Zn content and the highest leaching, Condition #7 tended to have higher leaching, even though Conditions #3 and 5 had less Zn content. The effects of temperature and dosage showed a tendency similar to other heavy metals. Similar to Cu concentration, the higher is the concentration, the more accurate is the prediction. In addition, ANN predictions tend to fluctuate at low concentrations, which seems to be the result of minimizing MSE since ANN is not a gradually enhanced model. To overcome these shortcomings, it is necessary to optimize the model by utilizing the structure of the ANN model in the form of Recurrent Neural Network (RNN) or by taking logs at the concentration of Zn. The forecast for the additional experiment was much lower than the actual value, which appears to be similar to Pb because it does not reflect actual measurements that are not linearly affected by temperature. Figure 5: Observed and ANN-predicted Cu concentration changes with a topology of 9:8:1. Thenumbers are from Table 1, while the red number is the result from the data for further verification. Figure 6: Observed and ANN-predicted Pb concentration changes with a topology of 9:8:1.