## 3 Results and Discussion  
### ANN Modeling for pH and Eh  
Table 3 presents the MSE and R value for each topology for each output variable. Regardless of the type of output variable, the optimal topology was when the neurons in the hidden layer were greater than 8, probably because many neurons under the same hidden layer structure can include small cases. However, there was no optimal case with the largest number of neurons, apparently due to the increasing number of neurons in the model's optimization process making it more difficult to find the optimal w and b matrices. Table 4 presents the values of weights and biases for each layer and neuron from the best topology for each output variable. The diagnostic plots between observed and predicted values in the ANN model are illustrated in Figure 2, which shows a high correlation coefficient (\(R\)) value.  
Predicting the results from the data for further verification was also performed to examine the predictability of the developed ANN models. The pH results and the prediction by ANN model are shown in Figure 3. The X-axes are diverse depending on experimental conditions. In the case of pH, the range of values does not vary much by studies, while, in the case of bacteria, the lower tendency is universal. Therefore, the presence or absence of bacteria has the greatest influence. Temperature and dosage were found to have relatively little effect on pH changes. For this reason, the prediction of the ANN model was similar to the actual results, regardless of the experimental conditions. However, the results from further verification were less predictable. In other words, it was predicted that the pH would be lower if bacteria existed, but, in reality, there was no significant difference depending on the presence or absence of bacteria in these conditions. This prediction is due to the fact that most of the data used in ANN model optimization are cases where pH varies greatly depending on the presence or absence of bacteria. Because there are no learning data under conditions where pH does not change much, it is difficult to predict the results under conditions that do not change much. Therefore, it is necessary to conduct additional experiments under conditions where the presence or absence of bacteria does not significantly affect pH and use them for the advanced ANN model.  
The Eh results and the prediction by ANN model are shown in Figure 4. The X-axes are diverse depending on experimental conditions. Similar to the pH results, Eh also has similar levels of value, so it can be seen that overall predictions are good. Eh also showed a big difference in trends, as with pH, depending on the presence or absence of bacteria. The effect of temperature and dose was also smaller, similar to the pH result. For further verification, the only difference is in the dosage compared to Experimental Condition #4. Although the difference between the dosage is not significant in the corresponding results, it is judged that the effect of the dosage was greater for the ANN model. In the case of the Eh model, it is expected that the more sophisticated ANN model will be obtained by performing additional experiments involving various dosage conditions and applying the results to the training.  
Figure 2: Diagnostic plots between observed and predicted values in the ANN model with each output variable’s best topology: (**a**) pH; (**b**) Eh; (**c**) Cu concentration; (**d**) Pb concentration; (**e**) Zn concentration; and (**f**) Fe concentration.