# Model Study on the Combination of Operating Parameters of Corn Kernel Harvesters
## 4 Results and Discussion
### Optimization of Operating Parameters
#### 4.3.3 Operating Parameter Optimization  
MATLAB was used to optimize and calculate the regression equation. Combined with the actual operating parameter indicators, the crushing rate was less than 5%, the impurity rate was less than 2%, the kernel loss was less than 5%, and the critical indicators for engine speed and cylinder speed were 10% and 20% respectively. That is, if the speed variation rate exceeded 10%, there would be a tendency toward blockage, and processing adjustment \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Source**} & \multicolumn{2}{c}{**Sum of Squares**} & \multicolumn{2}{c}{**Degrees of Freedom**} & \multicolumn{2}{c}{**Mean Square**} & \multicolumn{1}{c}{**\(F\)-Value**} & \multicolumn{1}{c}{**\(p\)-Value**} \\ \hline \multirow{4}{*}{\(y_{1}\)} & Regression & 18,078.18 & 9 & 2008.69 & \(F_{2}=3.519\) & 0.036 \\  & Remainder & 5708.35 & 10 & 570.84 & — & — \\  & Lack of fit & 5602.13 & 5 & 1120.43 & \(F_{1}=52.74\) & 0.0001 \\  & Pure error & 106.23 & 5 & 21.25 & — & — \\  & Total & 23,786.53 & 19 & — & — & — \\ \hline \multirow{4}{*}{\(y_{2}\)} & Regression & 8.22 & 9 & 0.91 & \(F_{2}=0.74\) & 0.6812 \\  & Remainder & 12.40 & 10 & 1.24 & — & — \\  & Lack of fit & 10.86 & 5 & 2.17 & \(F_{1}=7.00\) & 0.0047 \\  & Pure error & 1.55 & 5 & 0.31 & — & — \\  & Total & 20.62 & 19 & — & — & — \\ \hline \multirow{4}{*}{\(y_{3}\)} & Regression & 4.40 & 9 & 0.35 & \(F_{2}=1.309\) & 0.35 \\  & Remainder & 3.74 & 10 & — & — & — \\  & Lack of fit & — & 5 & — & \(F_{1}=0.219\) & 0.95 \\  & Pure error & — & 5 & — & — & — \\  & Total & — & 19 & — & — & — \\ \hline \multirow{4}{*}{\(y_{5}\)} & Regression & 498.31 & 9 & 55.37 & \(F_{2}=0.528\) & 0.83 \\  & Remainder & 1049.31 & 10 & 104.93 & — & — \\  & Lack of fit & 74.40 & 5 & 14.88 & \(F_{1}=0.076\) & 0.99 \\  & Pure error & 974.91 & 5 & 194.98 & — & — \\  & Total & 1547.62 & 19 & — & — & — \\ \hline \multirow{4}{*}{\(y_{6}\)} & Regression & 17,318 & 9 & 1924.22 & \(F_{2}=3.473\) & 0.0373 \\  & Remainder & 5539.78 & 10 & 553.978 & — & — \\ \cline{1-1}  & Lack of fit & 5417.28 & 5 & 1083.46 & \(F_{1}=44.223\) & 0.0001 \\ \cline{1-1}  & Pure error & 122.499 & 5 & 24.4999 & — & — \\ \cline{1-1}  & Total & 22,857.8 & 19 & — & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of variance. would be required; if the speed variation rate exceeded 20%, the harvester experienced severe blockage. The range of the individual operating parameters was obtained after the optimization calculations. To analyze the relationships among the influencing factors, including the feed rate, concave clearance, and cylinder speed, and the target functions (crushing rate, impurity rate, and corn kernel loss rate), and to optimize the combination of operating parameters, scatter plots and the box plots of the feed rate, concave clearance and cylinder speed were obtained with the aid of Origin software. In the box diagram, the horizontal lines above and below the boxes are the 25% quantile position and the 75% quantile position of the sample, respectively; the boxes contain 50% of the data in the sample. Figure 7 shows the scatter plots and box plots of the values of the feed rate \(x_{1}\) obtained by optimizing the regression model with the actual operating indicators. In Figure 7a, it can be seen that the range of values of the solution for the feed rate \(x_{1}\) calculated by the regression model optimization was concentrated between 12 kg/s and 13 kg/s. In Figure 7b, it can be seen that the optimal feed rate range after the optimized calculation of the rate of change of engine speed \(y_{1}\) and cylinder speed \(y_{5}\) was mainly distributed around 12 kg/s; that is, the forward speed of the harvester was about 5 km/h. From the scatter distribution outside the box line, the lower the system feed rate, the lower the system speed variation, which suggested that the system's operation was more stable.