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.

The physicochemical components of rice such as starch, protein, fat and water have significant influence on its nutritional value, and the drying process can easily cause changes in these components. In this paper, the effect of technical parameters on the nutritional quality of rice during hot-air drying was studied, and a control method of the rice-drying process based on effective accumulated temperature was proposed to ensure the drying quality and improve the drying efficiency. Through thin-layer drying experiments, hot-air temperature (T), humidity (RH), initial moisture content (MC), wind speed (V) and tempering ratio (TR) were selected as control factors, and the central composite design was adopted to optimize the experimental scheme. The relationship between each factor and nutrient quality was revealed through response surface analysis, and the regression model and process optimization parameters were established. The results show that the optimization parameters are as follows: hot-air temperature, 48.87 °C; humidity, 30.12%; initial moisture content, 21.31%; wind speed, 0.62 m/s; tempering ratio, 2.87; the optimized total drying time is 4.23 h; the effective accumulated temperature is 214.44 °C·h. The contents of protein, fat, amylose and amylopectin were 8.47 g/100 g, 1.97 g/100 g, 15.33 g/100 g and 60.50 g/100 g, respectively. The relative error of the verification test was 4.17%. The optimized process can effectively maintain the nutritional quality of rice and improve drying efficiency. This paper provides a new way to deeply explore the mechanism of rice quality change, and the established process reference chart provides a scientific basis for actual drying operations and the development of an intelligent control system.

To optimize the microwave drying process of paddy rice and improve its quality, the effects of hot air temperature, microwave power, and grain layer thickness on the post-drying potassium content, calcium content, vitamin B₁ content, and free fatty acid content of rice were investigated. Using response surface methodology, an experimental scheme based on the Box-Behnken design was constructed to analyze the influence of these three factors on the four nutritional and biochemical indicators. A multi-index optimization model was established and validated. The results showed that all response indicator models were statistically significant. The optimal process parameters were determined as follows: hot air temperature of 52.47°C, microwave power of 20 kW, and grain layer thickness of 2.78 cm. The corresponding predicted values were potassium content of 3724 mg/kg, calcium content of 113.7 mg/kg, vitamin B₁ content of 0.290 mg/100g, and free fatty acid content of 21.5 mg/100g. Validation experiments demonstrated that the dried rice under these conditions exhibited excellent quality, with relative errors between predicted and measured values below 3%, indicating the reliability of the model and the significant improvement in rice drying quality achieved by the optimized process. Furthermore, visual process reference charts were developed to provide a theoretical basis for adjusting process parameters in practical production.