## 3 Results and Discussion  
### Parametric Analysis and Descriptive Statistics of the Data  
The parametric analysis showing the relationship between input variables and hydrogen production is depicted by the contour plots in Figure 3. The contour plot analysis helped to determine what range of values of the input variables significantly influence the biohydrogen production based on the available experimental data. It was successfully employed in the analysis of fluid catalytic cracking catalysts, as reported by [PERSON] and [PERSON] [28]. Figure 3a shows the effect of an interaction between waste glycerol and \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**The Activation Function**} & \multicolumn{1}{c}{**The Activation Function**} & \multicolumn{1}{c}{**Optimization Algorithm**} & \multicolumn{1}{c}{**Number of Units in**} \\  & **in the Hidden Layer** & **in the Outer Layer** & **for Training** & **the Hidden Layers** \\ \hline MLPNN 1 & Hyperbolic tangent & Identity & Scaled conjugate gradient & 10 \\ MLPNN 2 & Hyperbolic tangent & Hyperbolic tangent & Scaled conjugate gradient & 10 \\ MLPNN 3 & Hyperbolic tangent & Sigmoid & Scaled conjugate gradient & 10 \\ MLPNN 4 & Sigmoid & Identity & Scaled conjugate gradient & 10 \\ MLPNN 5 & Sigmoid & Hyperbolic tangent & Scaled conjugate gradient & 10 \\ MLPNN 6 & Sigmoid & Sigmoid & Scaled conjugate gradient & 10 \\ MLPNN 7 & Hyperbolic tangent & Identity & gradient descent & 10 \\ MLPNN 8 & Hyperbolic tangent & Sigmoid & gradient descent & 10 \\ MLPNN 9 & Hyperbolic tangent & Hyperbolic tangent & gradient descent & 10 \\ MLPNN 10 & Sigmoid & Identity & gradient descent & 10 \\ MLPNN 11 & Sigmoid & Hyperbolic tangent & gradient descent & 10 \\ MLPNN 12 & Sigmoid & Sigmoid & gradient descent & 10 \\ RBFNN-1 & Softmax & Identity & ordinary & 10 \\ RBFNN-2 & Softmax & Standardized identity & standard & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the various MLPNN and RBFNN configurations used. urea on biohydrogen production. The interaction effect of waste glycerol and urea on biohydrogen production can be seen to vary. Using a high concentration of urea inhibited biohydrogen production, as can be seen from the green color zone in Figure 3a, which is consistent with the report by [PERSON] et al. 2022 [29]. However, at a concentration of urea in the range of 0.25-0.75 g/L and a waste glycerol concentration in the range of 5-20 g/L, a high volume of biohydrogen was produced. Also, in Figure 3b, high biohydrogen production was favored at a urea concentration range of 1.25-0.25 g/L using endo-nutrients up to 0.35 g/L. Figure 3c revealed that using Na\({}_{2}\)HPO\({}_{4}\) in the range of 3-6 g/L while maintaining the concentration of urea between 0.1-0.175 can facilitate high biohydrogen production from waste glycerol. Figure 3d shows that the interaction between waste glycerol and Na\({}_{2}\)HPO\({}_{4}\) has the most significant effects on biohydrogen production. It can be seen that high hydrogen production (up to 20,000 mL H\({}_{2}\)/L) was facilitated using waste glycerol in the range of 5-10 g/L and Na\({}_{2}\)HPO\({}_{4}\) in the range of 6-8 g/L. The descriptive statistical analysis of the dataset used for training the model is summarized in Table 2. The analysis revealed that waste glycerol is in the range of 0.23-36.81 g/L, with a mean value estimated at 21.39 g/L. The standard deviation and variance of waste glycerol datasets were calculated as 7.43 g/L and 55.27 g/L, respectively. The amount of urea used for the experiment varied in the range of 0.05 to 0.25 g/L, with a mean value of 0.15 g/L. The standard deviation and the variance were obtained as 0.05 g/L and 0 g/L,  
Figure 3: Contour plots showing the interaction between (**a**) urea and waste glycerol (**b**) Endo-nutrient and urea (**c**) Na\({}_{2}\)HPO\({}_{4}\) and urea (**d**) Na\({}_{2}\)HPO\({}_{4}\) and waste glycerol. respectively.