# Investigation of a Precise Control Scheme for Rice Quality
## 3 Results and Discussion
### Process Reference Chart for Quality  
The desirability function approach is a commonly employed technique in response surface methodology (RSM) for optimizing each response variable and determining the optimal drying conditions [29]. However, the process of drying grains differs from that of fruits and vegetables, and there is no fixed optimal drying method. The initial moisture content of rice is greatly influenced by factors such as harvest season, region, and variety. Due to the diversity of rice dryers and their varying structural types, tempering ratios may differ and operating environmental air humidity can fluctuate, thus necessitating real-time adjustments to dryer parameters [30]. In order to mitigate the impact of sensitive factors on drying quality and achieve precise control, for early indica rice with minimal varietal differences, process reference charts have been developed. This expedites the drying process while ensuring milling quality. These charts depict the projected values of one or multiple rice indices during drying under varying conditions. Additionally, the corresponding parameters of the drying process can be extracted from the charts to establish target values for one or more indices. Based on the experimental results and considering the sensitivity to factors and factor adjustability, the drying temperature was identified as the primary query process parameter in the process reference charts for different initial moisture contents and relative humidities. Figures 11 and 12 depict the process reference charts for tempering ratios of two and three, respectively, that are applicable to dryers with varying tempering ratios. These charts can be utilized for other grain types, such as corn or different varieties of rice, and integrated into intelligent drying control systems in the future. The contours of the net drying time and ACP are presented in Figure 11, where the top left region exhibits longer net drying times and smaller ACPs. As the initial moisture content decreases and drying temperature increases, a decrease in net drying time is observed alongside an increase in ACP. Furthermore, under the influence of initial moisture content and drying temperature, opposite responses are noted for both drying time and ACP with varying trends. For a given ACP, the contour lines of varying relative humidities converge at a singular point. Above this point, the relative humidity exhibits a positive correlation with the ACP, while below this point, it shows a negative correlation. This further elucidates the interplay between RH and MC. To simultaneously satisfy both indicators' requirements, we divided the process area and estab \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Parametric Setting**} & \multicolumn{4}{c}{**Net Drying Time (min)**} & \multicolumn{4}{c}{**Total Drying Time (min)**} \\ \cline{2-10}
**Run** & **T (°C)** & **RH (\%)** & **MC (\%)** & **V (m/s)** & **TR** & **Predicted** & **Measured** & **Discrepancy** & **Predicted** & **Measured** & **Discrepancy** \\ \hline
1 & 37 & 45 & 27 & 0.7 & 2.4 & 283.7 & 301.4 & 6.23\% & 965.3 & 938.4 & 2.87\% \\
2 & 52 & 45 & 23 & 0.8 & 1.5 & 172.5 & 168.5 & \(-\)2.30\% & 465.8 & 480.1 & 2.98\% \\
3 & 42 & 35 & 27 & 0.75 & 2 & 190.2 & 210.3 & 10.57\% & 562.2 & 545.3 & 3.1\% \\
4 & 44 & 48 & 22 & 0.6 & 1.3 & 234.6 & 220.9 & \(-\)5.84\% & 571.6 & 560.8 & 1.92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Predicted and measured values of drying time.