# Optimization of Screen-Hole-Clearing Devices for Mechanized Residual Film-Impurity Separation
## 3 Results and Discussion  
### Regression Model Building and Testing  
The experimental results were analyzed using Design-Expert software [23]. In the results, \(p<0.01\) indicated that the parameter had notable effects on the model, and \(p<0.05\) indicated that the parameter had notable effects on the model. The results of the analysis of variance and the significance test of the regression coefficients of the secondary regression model are summarized in Table 3.  
As displayed in Table 3, \(P_{1}\) and \(P_{2}\) were less than 0.01 in the quadratic regression models based on the influencing factors and the ratios of impurities in the residual film and of blockages of the screen holes were highly significant. The outlying values of \(P_{1}\) and \(P_{2}\) were greater than 0.05, which were not significant. The \(R^{2}\) values of the coefficients of determination were 0.9591 and 0.9489, indicating that the regression models could fit more than 94% of the sample data [24]. The quadratic regression equation fitted by the model was consistent with the actual situation and correctly represented the relationships between the ratio of impurities in the residual film \(Y_{1}\), the ratio of the blockage of the screen holes \(Y_{2}\), and \(X_{1}\), \(X_{2}\), and \(X_{3}\), indicating that the model can better predict the results of various tests with real machines.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Levels**} & \multicolumn{3}{c}{**Test Factors**} \\ \cline{2-4}  & \multicolumn{1}{c}{**Distance from the Nozzle to**} & \multirow{2}{*}{**Fan Wind Speed \(X_{2}\) (m\(\cdot\)s\({}^{-1}\))**} & \multirow{2}{*}{**Number of Airflow Pipes \(X_{3}\)**} \\  & & & & \\ \hline –1 & 95 & 23 & 1 \\
0 & 105 & 24 & 3 \\
1 & 115 & 25 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test factor levels and coding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Serial**} & \multicolumn{3}{c}{**Test Factors**} & \multicolumn{2}{c}{**Evaluation Indexes**} \\ \cline{2-5}  & \(X_{1}\) & \(X_{2}\) & \(X_{3}\) & \
\begin{tabular}{c} **Impurity Rate in the** \\ **Residual Film Y\({}_{1}\)** \\ \end{tabular} &
\begin{tabular}{c} **Blockage Rate of the** \\ **Screen Holes Y\({}_{2}\)** \\ \end{tabular} \\ \hline  
1 & 1 & 10.77 & 15.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of response surface analysis.  
For the ratio of impurities in the residual film, \(X_{2}\), \(X_{3}\), \(X_{1}X_{3}\), and \(X_{3}\)\({}^{2}\) had significantly strong effects on the model (\(p<0.01\)), and \(X_{1}X_{2}\), \(X_{1}\)\({}^{2}\) had significant effects on the model (\(p<0.05\)). For the ratio of blockages of the screen holes, \(X_{2}\)\({}^{2}\), \(X_{3}\)\({}^{2}\) had a highly significant effect on the model (\(p<0.01\)), and \(X_{2}\), \(X_{3}\) had significant effects on the model (\(p<0.05\)). The quadratic regression models of the ratio of impurities \(Y_{1}\) in the residual film and the ratio of blockage \(Y_{2}\) of the screen holes were expressed using coded values obtained by eliminating the nonsignificant terms, as depicted in Equations (14) and (15).  
\[Y_{1}=7.98-1.19X_{2}-1.99X_{3}-1.21X_{1}X_{2}+2.05X_{1}X_{3}+1.28X_{1}^{2}+3.1 6X_{3}^{2}, \tag{14}\]  
\[Y_{2}=6.86-1.22X_{2}-0.92X_{3}+2.4X_{2}^{2}+4.51X_{3}^{2}. \tag{15}\]  
The data in Table 3 and the \(p\) value of each factor indicated that the factors influencing the ratio of impurities in the residual film, in the descending order of influence, were as follows: the number of airflow pipes, fan air speed, and distance from the nozzle to the screen's surface. The factors affecting the ratio of blockage of the screen holes, in the descending order of influence, were as follows: the fan air speed, number of airflow pipes, and distance from the nozzle to the screen's surface [25].