*3.3. Comparison of the Predictive Capability of Models*

Table 6 displays the findings obtained from different statistical metrics used to test the built models. For both developed models (RSM and ANN), the high R values (Table 6) indicate that there is a strong link between the real and the predicted yields of bio-jet fuel. For both RSM and ANN, the R and R2, were 0.9963, 0.9926, and 0.9979,0.9957, respectively.



The good fit of the models is representative of these high values. RMSE is a test of the dataset adherence to the regression line. For both RSM and ANN models, the values of RMSE that were obtained were all low, confirming the models' good fit. To calculate the residuals (deviation from actual objective) of the built models, SEP (percent) and MAE were used. The ANN model had less divergence from experimental values of SEP (0.6889 percent) and MAE (0.022) than the RSM model, as seen in Table 6. In comparison, lower chi-square (0.782) values confirmed that the most reliable was the ANN model with the lowest error term values and highest R and R2 values. It was noted in this analysis that ANN was found to be superior than RSM.
