In this paper, a model for controlling paddy drying by incorporating accumulated temperature is introduced and defined. Drying experiments using freshly harvested paddy were conducted at different levels of drying air parameters including temperature (T = 27°C, 31°C, 35°C, 39°C, and 43°C), relative humidity (RH = 45%, 50%, 55%, 60%, and 65%), initial moisture content (M₀ = 17%, 19%, 21%, 23%, and 25%) and airspeed (V = 0.4, 0.5, 0.6, 0.7, and 0.8 m s⁻¹). When T = 31°C, RH = 60%, M₀ = 19%, and V = 0.5 ms⁻¹, the crack additional percentage reached a minimum value of 0.508%, with the average precipitation rate at 0.719%·h. Further, an accumulated temperature and quality chart was provided in this paper. The x-coordinate of this chart is temperature, and the initial moisture content represents the y-coordinate. It covers the drying conditions of the actual dryer well and has a wide range of applicability for real-world environment. The model developed in this study not only provides a scientific reference for precise drying and intelligent control of paddy but also guide the actual drying operation.

Due to the nonlinearity, strong coupling and hysteresis of the parameters used in measuring the grain drying process, it is always a challenge to perform accurate control of the drying system. The purpose of this paper is to describe the design of a suitable neural network model to control the grain dryer more effectively. First, the operating mechanism of a continuous grain dryer and the principle of the heat and mass transfer that can be used in the process of grain drying was analyzed before an intelligent control system was designed accordingly. Second, an intelligent control system based on the Back Propagation Neural Network (BPNN) was developed. The BPNN was the optimal model selected based on a series of comparative test results. According to the BPNN prediction of the moisture content of dried rice, the system could adjust the rate of grain discharge of the dryer, and then control the drying process accurately. Finally, the neural network control model was simulated using computer simulation technology, and was optimized by comparing analysis results with experimental results. The results showed that the optimized intelligent control system using BPNN has the advantage of strong stability and good noise handling, and could have great potential for future implementation studies.

Rice drying is a complex and nonlinear process, with product quality being easily influenced by numerous factors. This study aimed to investigate the characteristics causing variation in rice quality and provide novel insights for regulating and controlling rice drying operations. To this end, response surface methodology was employed to examine the hot air drying of rice. The study was centered on investigating the impact of different levels of drying temperature (35–55 °C), relative humidity (30–50%), initial moisture content (20–28%), air velocity (0.36–0.84 m/s), and tempering ratio (1–4) on the process. The measured parameters included the net drying time, total drying time, additional crack percentage, and head rice yield. The experimental data were analyzed using Design–Expert, and the results indicated that all the response quadratic polynomial models were statistically significant. All the linear terms had a significant impact on the response variables except for the impact of air velocity on head rice yield. Finally, process reference charts of actual drying operating conditions were established based on the regression models to provide a scientific reference for guiding the control of rice drying quality and integrating into intelligent grain drying control systems in the future.

The grain-drying process plays a critical role in grain storage and quality assurance. In recent years, with the advancement of low-carbon and intelligent technologies, the control technology of grain dryers has significantly improved. This paper systematically reviews the development status of grain dryer control technology from the perspective of low-carbon and intelligentization, analyzing the technological differences in control systems between domestic and international approaches. Current research challenges include the insufficient integration of control technologies with the drying process, limited control variables, the inadequate application of intelligent control strategies, and unstable sensor accuracy. To enhance the performance of grain-drying systems, this paper suggests optimizing control mechanisms, adopting efficient and environmentally friendly energy sources, improving sensor performance, introducing advanced intelligent control algorithms, and strengthening system monitoring capabilities. Looking ahead, with the further integration of AI, IoT, and green energy, grain-drying control systems are expected to evolve towards greater intelligence, remote operation, and low carbonization, providing technical support for enhancing drying efficiency and environmental performance.

As the pre-process of storage and processing, rice drying is a key link to ensuring grain quality. A scientific and reasonable drying process can maintain grain quality and realize grain loss reduction, energy savings, and emission reduction. By establishing the response surface (RSM) regression model, the relationship between various experimental factors and quality indicators was analyzed, and the reasons for the results were explained. The optimized parameter combination was a hot air temperature of 43.14 °C, hot air humidity of 48.00%, initial moisture content of 23.80%, hot air velocity of 0.70 m/s, and hysteresis ratio of 3.55. Under this parameter combination, the drying characteristics (net drying time = 157.61 min, germination rate = 94.15%), processing quality (burst waist increase rate = 3.48%, whole rice rate = 70.458%), and nutritional quality (fatty acid value = 20.93 mg, resistant starch concentration = 195.26 μg/g, protein content change = 8.53 g/100 g, fat content change = 2.37 g/100 g) of rice after drying improved. The relative error between the validation results and the optimized results was 4.68%, indicating that the optimized process parameters can improve the efficiency and quality of rice drying. According to the regression model, the process reference chart with the process retrieval and prediction function was drawn, and the corresponding high-quality control scheme was given, providing a reference for the parameter settings of actual drying operations. The optimized drying process parameter combination effectively reduced the moisture gradient inside rice grains, drying stress, and the occurrence of cracks, thereby improving the appearance, quality and nutritional value of rice after drying. Moreover, the process reference chart can provide a reference for the actual preliminary processing technology of rice after production and a theoretical basis for an in-depth exploration of the mechanism of changes in rice quality.

Previous research has shown that the accumulated temperature can describe drying processes as well as crop growth. To describe the mass and heat transfer processes in the rice drying process more accurately, a mathematical model of rice drying was proposed based on the drying accumulated temperature, and the optimal tempering ratio for conventional hot air drying was obtained through data comparison and analysis. First, it was proven that there was an exponential relationship between the moisture ratio and the drying accumulated temperature of rice. Second, by comparing and analyzing the fitting results of seven different drying mathematical models, the model with the highest fitting degree was selected and reconstructed to obtain the drying accumulated temperature–moisture ratio model. Finally, the new model was used to fit the results of two drying experiments without and with tempering, and the tempering characteristics of rice drying were proved by comparing and analyzing the coefficient difference between the two models. The results showed that the optimal tempering ratio was 3. This study thus provides a reference for rice drying process parameters.