## 3 Results
### Analysis of Response Surface Test Results  
#### 3.4.1 Establishment and Test of Regression Model  
Design Expert 8.06 software [42; 43] was used to analyze the test results and establish a quadratic regression model. The variance analysis of the quadratic regression model was shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & \multicolumn{5}{c}{**Factor**} & \multirow{2}{*}{**Comprehensive**} \\ \cline{2-2} \cline{4-6}  & **A Scraper Shaft** & **B Scraper Space** & **C Spiral Conveyor** & **D Spiral Conveyor** & **Qualified Rate (\%)** \\  & **Speed (r/min)** & **(mm)** & **Speed (r/min)** & **Pitch (mm)** & **Pitch (mm)** & **Pitch (mm)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 6: Design scheme and result of the test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{5}{c}{**Level**} \\ \cline{2-5}  & \(-2\) & \(-1\) & \(0\) & \(1\) & \(2\) \\ \hline A & \(30\) & \(35\) & \(40\) & \(45\) & \(50\) \\ B & \(200\) & \(300\) & \(400\) & \(500\) & \(600\) \\ C & \(137.5\) & \(150\) & \(162.5\) & \(175\) & \(187.5\) \\ D & \(150\) & \(200\) & \(250\) & \(300\) & \(350\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Factor levels in the test.  
The variance analysis results of the quadratic regression model for the Central Composite Design response surface test are shown in Table 7. According to Table 7, the P-value of the fitting model was lower than 0.0001, showing a good fitting degree; the P values of scraper shaft speed (\(A\)), spiral conveyor pitch (\(D\)), scraper space-scraper space (\(B^{2}\)) and spiral conveyor pitch (\(D^{2}\)) were all lower than 0.01; the P values of scraper space (\(B\)) and scraper shaft speed-scraper space (\(AB\)) were all lower than 0.05. The results showed that all factors had significant impacts on the comprehensive qualified rate of fertilization, which indicates the validity of the regression model. The lack of fit P = 0.9281 \(>\) 0.05 indicates that the proportion of abnormal error when fitting the regression equation with the actual results was small, showing a good fitting degree without lack of fit. The coefficient of variation was \(CV\) = 1.29%, indicating the high reliability of the test. The determination coefficient was 0.9655, and the adjusted determination coefficient was 0.9334, both of which were greater than 0.9, indicating that the model can truly reflect the actual situation. The test precision \"Adept precision = 21.172 \(>\) 4\" indicates the high precision of the model.  
The second-order regression Equation (12) of the comprehensive qualified rate and significance parameter of fertilization was obtained after the deletion of the insignificant items.  
\[Y=90.27-4.41A+0.56B+1.09D+0.70 AB+1.04B^{2}+1.28D^{2} \tag{12}\]  
According to Formula (12), when factor A reached the optimal value, if it continued to increase, it would bring negative effects. Factor A was the main factor affecting the fertilization amount. With the increase of factor A, the qualified rate of fertilization amount will decrease and the qualified rate of fertilization amount occupied 70% in the evaluation index. Therefore, a too large value of factor A will bring negative effects.