## 1 Introduction  
The pressure drop of the grain bed layer is the key parameter for designing and optimizing the structure of the drying chamber, and it is also one of the most important references for selecting the power of a fan [1, 2]. During the drying process, owing to the bed layer porosity as well as the air viscosity, there must be a pressure drop when the air flows through the bed layer. A high-pressure drop may make it difficult for the air to flow through the bed layer and further lead to the increase of non-uniformity of the drying process, while a low-pressure drop may lead to wasting the power of the fan [3]. Accordingly, it is necessary to find out a reasonable pressure drop under the premise of ensuring product quality and maintaining a reasonable power demand of the fan. In the last few decades, researchers have done many studies on the ventilation resistance of the grain bed layer by applying the traditional pressure drop models [4, 5, 6, 7]. For example, [PERSON] and [PERSON] [8] conducted an experiment on the flow resistance of sorghum bed layer by applying the Ergun model, the resistance of sorghum moisture content of 16.5, 18.5, and 23 %w.b. were determined under the grain bed depth of 150 to 1200 mm with the airflow ranges of 0.05 to 0.3 m/s. The results showed that the resistance of airflow increases with the increase of air velocity and bed layer depth, while resistance increases with the decrease of moisture content. [PERSON] et al. [9] applied the Shedd model to simulate the flow resistance of the hot air flow through the paddy grain bed layer; they found that the flow resistance of the hot air increases with the increase of hot air velocities. [PERSON] et al. [10] investigated the pressure drop of the air (with velocity ranges of 0.1 to 0.6 m/s) flow through the bed layer with depth ranges of 100 to 1000 mm, and found that the simulation accuracy of the pressure drop using Ergun model is reliable only when the air velocity is under 0.2 m/s. It can be summarized from the above analyses that the flow resistance of the grain bed layer are mainly determined by the traditional models and the characteristics of the air flow resistance are investigated by using single factor test. Though the works provided some valuable references for guiding the practical production, the air flow resistance prediction accuracy based on traditional models should be further improved and the interaction of the parameters on the flow resistance should be further investigated. Respond surface methodology (RSM) is one of the most effective modeling tools to investigate the interactions of two or more variables on the responses, which is widely adopted to optimize the process parameters in the agriculture field [11; 12]. [PERSON] et al. [13] conducted an experimental study on the vacuum drying extraction technology of konjac glucomanan using RSM, and an artificial neuron network (ANN) was also adopted and then compared with the RSM. The results showed that the model established by RSM is slightly better than that of an ANN. In order to improve the dynamic performance of orchard ditching fertilizer and to avoid resonance, [PERSON] et al. [14] adopted the RSM to optimize the structure of the rack of orchard ditching-fertilizer machine. The results showed that the optimized structure has a better first-order modal frequency of 38.31 Hz than that of the traditional structure, and the optimized frequency is far away from the input frequency (35 Hz) of a tractor, indicating that the frequency of the structure optimized by RSM can avoid the resonance. Moreover, [PERSON] et al. [15] adopted the RSM to optimize the production process of a micro-elemental feed additive for laying hens, the most favorable enrichment conditions of the process were determined by the model established by the RSM. It can be summarized that the RSM is widely used in the agricultural field, and it can be also adopted to model the ventilation resistance of paddy grain layer.