## 1 Introduction  
Drying is the process of removing the moisture from natural products (e.g., agricultural products, wood, and fruits) or industrial materials (e.g., lignite, ceramics, and medical materials) down to a specific moisture content, while ensuring at the same time prime product quality, high throughput, and minimal operational costs [1; 2]. Industrial drying is an effective approach to achieve the efficient and economic production of the agricultural product, while the real-time detecting and controlling technology plays a very important role in the production. However, the drying is a typical nonlinear complex process and there are various variables coupled with each other during the drying process. The real-time measurement of the drying process has hysteresis and is interfered by many factors [3]. Therefore, it is very difficult to establish a mathematical model that is in line with the actual drying process in industrial production. In the last few decades, researchers have done a lot of works on the mathematical modeling of grain drying process, which mainly focus on the modeling of single particle drying [4; 5; 6], thin layer drying [7; 8], and deep bed drying [9; 10; 11], of which the deep bed drying model is generally used in industrial drying. The typical deep bed drying model is partial differential equation (PDE), which is based on the principle of heat and mass transfer. The kind of model are mainly adopted to simulate the drying process, so as to understand the variation law of various parameters in the drying process, and provide theoretical basis for simplifying the model. However, owing to the fact that this kind of model is complex and difficult to solve, it cannot be used for real-time control in drying process [12]. [PERSON], [PERSON] et al. [13] established a predictive and control model by combining the prediction model, rolling optimization algorithm and feedback correction algorithm, the problems of hysteresis and multi disturbance in the control process of grain dryer were solved. Though the mathematical model is considered to be one of the best control methods for industrial-scale grain dryers when the hot air temperature is in the range of 85-120 \({}^{\circ}\)C, and the moisture content of 21-32% w.b., the control and predicting effects are still affected by the analytical accuracy of the deep drying process as well as the accuracy of moisture on-line detection. Based on the analysis above, a simple and sensitive prediction model is necessary to realize the automatic control of drying process and achieve the goal of high efficiency, high quality, and energy saving drying. Artificial neural network (ANN) methodology has great application potential in the classification and prediction of complex systems [14; 15]. Generally, the methodology identifies the system by collecting certain input and output data to get the model without revealing the mathematical equation in advance [16]. Owing to the advantages of the self-learning, self-adaptation, and flexibility, ANN methodology was also widely applied in identification and modeling of grain drying system. [PERSON] et al. established a 8-10-1 back propagation (BP) neural network prediction model for the drying process of the combined side-heat infrared radiation and convection grain dryer. The input of the model is eight variables of the grain dryer, and the output of the model is the moisture ratio (or drying rate) of grain material. The results showed that the model has an excellent prediction performance (coefficient of determination \(R^{2}\) = 0.9989), and the feasibility of the application of ANN methodology in drying the drying process are also verified [17]. [PERSON], [PERSON] et al. applied the ANN methodology to an agricultural fixed-bed dryer, the relationship between the moisture distribution of the dried grain and the physical parameters of the drying air temperature, humidity, and air flow rate were determined; the results showed that ANN methodology can be effective for modelling of the grain drying process [18].