## 4 Discussion
### Model Validation
Figure 2: Changes in the concentrations of (**a**) gaseous H\({}_{2}\)S, (**b**) gaseous NH\({}_{3}\), (**c**) aqueous H\({}_{2}\)S, and (**d**) aqueous NH\({}_{3}\) under different H\({}_{2}\)S inlet concentrations (dotted lines indicate model prediction results). In Figure 2, the dotted lines represent the behavior of the EWS predicted, using the model. Overall, the predicted results effectively simulated the experimental results. However, when the H\({}_{2}\)S inlet concentration was 2000 ppm, the predicted aqueous H\({}_{2}\)S concentration varied slightly from the experimental value. The concentration of aqueous hydrogen was approximately 60 mg dm\({}^{-3}\) at 60 min after the start of the experiment but attained equilibrium at approximately 40 mg dm\({}^{-3}\) in the model simulation. This indicates that an error occurs in the model simulation when the loading rate is high compared to the current density. This may be due to variations in the oxidation rate via direct oxidation between the batch and continuous inflow experiments. In the numerical model proposed in this study, the oxidation capacity of the electrochemical oxidation device was expressed, using kinetic constants derived from the batch experiments. In the batch experiments, the effect of direct oxidation decreased because the initial concentrations in solution were fixed. The diffusion of pollutants to the electrode surface was limited owing to a reduction in concentration inside the reactor over time. Under the continuous inflow condition, however, the direct oxidation rate increased because diffusion to the electrode surface increased as the liquid phase concentration continuously increased over time. In particular, if the current density was relatively low compared to the loading rate, the direct oxidation rate further increased owing to an increase in the probability of diffusion to the electrode surface without reacting with the RCS. In other words, the direct oxidation rate decreased under diffusion limiting conditions [21]. Therefore, the error between the experimental and model values increased with increase in the loading rate. Therefore, the results for simulations must be interpreted carefully in cases where the loading rate is high, or the current density is low. In addition, supplementing the model to reduce errors is necessary. Under the conditions of different loading rates, the simulation results for NH\({}_{3}\) appropriately described the experimental results. This may be a result of the tendency for stable NH\({}_{3}\) removal that is not affected by different experimental conditions, which was also reflected in the model simulation. The model properly expressed the experimental results, indicating that it can aid in predicting the performance of the EWS in the future. Since each parameter affects the results of the model simulation process differently, examining the sensitivity of each parameter is essential. For the sensitivity analysis of each parameter, the values were varied by \(\pm\) 20% in the numerical model to predict any changes in the removal efficiency. Figure 3 shows the results of the sensitivity analysis. The solid lines represent the simulation results for the default values without variation, while the dotted lines represent the simulation results for the values varied by \(\pm\) 20%. Wider gaps between the solid and dotted lines indicate that the specific parameter has a significant influence. Among the parameters for numerical model, the kinetic constant showed the highest sensitivity. This indicates that the kinetic constant term was greater significance than the mass transfer rate in the EWS. However, the mass transfer rate seemed to affect the performance of EWS rather than the kinetic constant because EWS was operated under diffusion limiting conditions as mentioned above. Hence, certain prediction results revealed a gap with respect to the experimental results. Therefore, the estimation of kinetic constants must be carefully performed.