## 3 Results and Discussion
### Thermal Conductivity Results  
Figure 4 depicts the impact of CPP and HSO quantities on the thermal conductivity coefficient of polyurethane-based composites. An increase in the amount of HSO has been observed to enhance the thermal conductivity coefficient. The thermal conductivity values depicted in the graph range between approximately 0.02 W/m\(\cdot\)K and 0.03 W/m\(\cdot\)K, with the lowest conductivity values observed when CPP levels are high. It is evident that the thermal conductivity coefficient of the composites can be optimized by controlling the CPP and HSO components. These findings underscore the necessity of employing both components at optimal ratios in industrial applications that necessitate energy efficiency and heat management. Equation (3) presents the quadratic model proposed by the RSM design for thermal conductivity. \begin{table}
\begin{tabular}{c c c c c} \hline
**Source** & **Sum of Squares** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline
**Model** & 172.22 & 28.70 & 281.92 & \(<\)0.0001 \\ \hline
**A—CPP (g)** & 2.12 & 2.12 & 20.87 & 0.0010 \\ \hline
**B—HSO (g)** & 159.28 & 159.28 & 1564.44 & \(<\)0.0001 \\ \hline
**C—MDI (g)** & 6.63 & 6.63 & 65.13 & \(<\)0.0001 \\ \hline
**AB** & 1.36 & 1.36 & 13.37 & 0.0044 \\ \hline
**AC** & 2.76 & 2.76 & 27.12 & 0.0004 \\ \hline
**BC** & 0.0613 & 0.0613 & 0.6016 & 0.4559 \\ \hline
**Residual** & 1.02 & 0.1018 & & \\ \hline
**Lack of Fit** & 1.02 & 0.1273 & & \\ \hline
**Pure Error** & 0.0000 & 0.0000 & & \\ \hline
**Cor Total** & 173.24 & & & \\ \hline \end{tabular}
\end{table} 
Table 3: ANOVA analysis for Shore A hardness of biocomposite. Table 4 illustrates that the model is highly significant overall (\(p\) < 0.0001) and effectively explains a considerable proportion of the variance (model SS = 0.0000; residual SS = 1.071 \(\times\) 10\({}^{-7}\)). Upon examination of the individual effects of the factors, it was found that the CPP (A), HSO (B), and MDI (C) variables exerted a markedly strong and significant influence on the model (\(p\) < 0.0001). It is noteworthy that C (MDI) (F = 1045.51) and B (HSO) (F = 878.53) emerge as the most dominant factors with the highest F-values, thereby underscoring their critical role in biocomposite production. Among the binary interactions, the AC interaction is found to be significant (\(p\) < 0.0001), whereas the AB (\(p\) = 0.0561) and BC (\(p\) = 0.1301) interactions are observed to be insignificant. This suggests that specific factors exert a considerable influence on the model when they are combined, whereas other interactions do not result in significant alterations to the model's behavior. Moreover, the quadratic effects (A\({}^{2}\), B\({}^{2}\), C\({}^{2}\)) are found to be highly significant (\(p\) < 0.0001), emphasizing the importance of nonlinear effects in the model. \[ \begin{array}{c}\text{Thermal conductivity}=-0.642249+0.020431\times\text{CPP }+0.004527\times\text{HSO }+\\ 0.010259\times\text{MDI}-0.000044\times\text{CPP }\times\text{HSO}-0.000117\times\text{CPP }\times\text{MDI}-0.000025\\ \times\text{HSO}\times\text{MDI}-0.000183\times(\text{CPP})^{2}-0.000175\times( \text{HSO})^{2}-0.000054\times(\text{MDI})^{2}\end{array}  \tag{3}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline
**Model** & 0.0000 & 4.126 \(\times\) 10\({}^{-6}\) & 269.61 & \textless{}0.0001 \\ \hline
**A—CPP (g)** & 3.895 \(\times\) 10\({}^{-6}\) & 3.895 \(\times\) 10\({}^{-6}\) & 254.54 & \textless{}0.0001 \\ \hline
**B—HSO (g)** & 0.0000 & 0.0000 & 878.53 & \textless{}0.0001 \\ \hline
**C—MDI (g)** & 0.0000 & 0.0000 & 1045.51 & \textless{}0.0001 \\ \hline
**AB** & 8.000 \(\times\) 10\({}^{-8}\) & 8.000 \(\times\) 10\({}^{-8}\) & 5.23 & 0.0561 \\ \hline
**AC** & 9.800 \(\times\) 10\({}^{-7}\) & 9.800 \(\times\) 10\({}^{-7}\) & 64.04 & \textless{}0.0001 \\ \hline
**BC** & 4.500 \(\times\) 10\({}^{-8}\) & 4.500 \(\times\) 10\({}^{-8}\) & 2.94 & 0.1301 \\ \hline \hline \end{tabular}
\end{table}