## 3 Results and Discussion
### Sensitivity Analysis  
After the validation stage of a developed ANN model, it is necessary to gain an idea about the uncertainties associated with the relationships between the output (weight left percent) and the input parameters. In general, sensitivity analysis aims to check the effect of a small change in an input parameter on the output variable. In this work, sensitivity analysis, which is a statistical tool to qualitatively obtain the relationship between input and output parameters [33; 34; 35; 36; 37; 38; 39], was performed using Pearson correlation (Equation (5)). In addition, the Pearson correlation indicates whether the relationship is positive or negative. For this purpose, 534 data points were used. The sensitivity analysis index (SAI), bounded by \(-1\) and \(+1\), was obtained using Pearson correlation. While \(+1\) denotes the maximum proportional effect of an input variable on the output parameter, \(-1\) indicates the maximum inverse impact, and \(0\) signifies the absence of any correlation between the output and the input [40]. Based on the SAI value, the relationship between an input parameter and the output parameter can be classified as follows: negligible (\(0\leq\text{SAI}\leq\pm 0.2\)), weak (\(\pm 0.21\leq\text{SAI}\leq\pm 0.35\)), moderate (\(\pm 0.36\leq\text{SAI}\leq\pm 0.67\)), strong (\(\pm 0.68\leq\text{SAI}\leq\pm 0.90\)), and very strong (\(\pm 0.91\leq\text{SAI}\leq\pm 1.00\)) [41]. The results of the analysis are presented in Table 8.  
As per the classification mentioned above, the results presented in Table 8 prove a strong negative correlation between temperature and weight left percent of the mixed plastic during its pyrolysis, which makes sense. As temperature increases, pyrolysis will take place, and thus weight left percent will decrease. This finding is aligned with the thermogravimetric curves, presented elsewhere [17], which have an inverted S-shape. However, since the SAI values of other parameters are close to zero, the correlation between the composition of the mixed plastic and weight left percent is negligible (no clear correlation), and this can be attributed to the size of the pyrolytic samples (10 mg) and the nature of the thermal decomposition of the polymer samples (fast process). The statistical significance of the input parameters was then checked with a confirmatory data analysis based on the probability or \(p\)-values, which were calculated using the \"Regression\" function available in the \"Data Analysis\" toolbox in Excel. The \(p\)-values provide a qualitative idea about the most significant parameters. A significance level of 5% (i.e., \(\alpha=0.05\)) was considered for the analysis. This means a parameter was considered statistically significant if the corresponding \(p\)-value was less than 0.05 [42; 43; 44; 45]. The results of the analysis are summarized in Table 9.  
The results presented in Tables 8 and 9 imply that temperature is the most sensitive parameter, which is in a good agreement with the results reported by [PERSON] et al. [46]. Temperature is a key parameter that contributes to the energy needed to transform a unit mass of solids to volatiles [47]. Figure 7 presents the variation in the pyrolysis output parameter (weight left percent) with input parameters. Although the temperature has a clear inverted sigmoid function relationship with weight left percent (Figure 7a), other parameters have no clear correlation with the output parameter (Figure 7b-e). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input Parameter** & **PP (wt \%)** & **PS (wt \%)** & **HDPE (wt \%)** & **LDPE (wt \%)** & **T(K)** \\ \hline SAI & 0.038 & \(-0.088\) & 0.051 & 0.001 & \(-0.732\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Relative importance of the input parameters.