# Novel ZIF-67@Bentonite (ZIF-67@BNT) Nanocomposite for Aqueous Methyl Orange Removal
## 3 Results and Discussion
### Predicting MO Adsorption Capacity with Machine Learning Techniques  
In this study, we also used ML modeling using the four-regression model, namely GPR, SVR, ANN-based MLP, and RF, to optimize and analyze the obtained adsorption data and the descriptive analysis shown in Table 7. The MLP Regressor model with two hidden layers (65 and 42 neurons) and the ReLU activation function effectively captures non-linear patterns in regression tasks. Using the L-BFGS solver ensures faster convergence, while the adaptive learning rate optimizes performance dynamically. Further improvements can be made by fine-tuning hyperparameters, exploring alternative activation functions, and integrating feature selection techniques. The RF model used 100 estimators, a minimum of three samples for splitting nodes, a squared error threshold, and a maximum depth of six. This collection of parameter combinations was chosen to maximize the model's performance while balancing complexity and general applicability of results. SVR used a 5-degree polynomial kernel with an auto gamma and regularization parameter (C = 1.0). The GPR was set with a given kernel with 20 optimization restarts, a learning rate of 0.014 for regularization, and target value normalization enabled. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Adsorbent Dose (mg)** & **Methyl Orange (mg/L)** & **Time (Hours)** & **Adsorption Capacity (mg/g)** \\ \hline Count & 22 & 22 & 22 & 22 \\ Mean & 8.40 & 26.81 & 9.68 & 112.49 \\ Median & 5 & 20 & 5 & 85.41 \\ Mode & 5 & 10 & 24 & - \\ Minimum & 5 & 5 & 1 & 15.18 \\ Maximum & 15 & 100 & 24 & 241.80 \\ Range & 10 & 95 & 23 & 226.61 \\ Standard deviation & 4.19 & 22.60 & 10.10 & 73.91 \\ Sample variance & 17.58 & 510.82 & 102.13 & 5463.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Descriptive statistics for the machine learning dataset. Figure 14: FTIR Characterization of ZIF-67@BNT nanocomposite Before and After Adsorption. Metrics including coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and mean absolute error (MAE) for both training and testing were analyzed and compared in order to assess the model fit, as seen in Table 8. The actual and predicted outcomes showed a very significant connection with the enhanced models, indicating R\({}^{2}\) values for training and testing as follows: GPR achieved 0.996 and 0.962, RF reached 0.955 and 0.927, while SVR had 0.998 and 0.907, and finally ANN had 0.999 and 0.872, respectively. With the highest R\({}^{2}\) and the lowest RMSE and MAE scores, as demonstrated in Table 8, the GPR model outperforms the other three models on testing datasets, demonstrating its superior accuracy in forecasting the MO adsorption capacity of the synthesized ZIF-67@BNT. The validation observations validate that the models generalize well to new data and underline the effectiveness of the measures taken to avoid data leaking. This suggests that, in comparison to the ANN, RF, and SVR models, the GPR model offers the greatest fit, the most precise forecasts, and the most efficiency in outcome prediction. Figure 15 provides a visual comparison of the performance of each model. A Taylor diagram was used in this work for both training and testing datasets in order to identify the best model and assess its dependability. The standard deviation, root mean square error (RMSE), and correlation coefficient of the prediction models are all graphically depicted in the Taylor diagram (Figure 16). The GPR model (represented by the violet circle) is positioned closest to the observed data point in the Taylor diagram displayed in Figure 16, whereas the ANN model exhibits overfitting with high accuracy for the training dataset and comparatively moderate accuracy for the testing dataset. These assessment techniques provided convincing proof that the GPR model is the best option for adsorption capacity prediction, outperforming the independent MLP-based ANN, RF, and SVR models.