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In this study, we designed a 5HLN‐R‐50 electric grain dryer with internal circulation of the drying medium based on the analysis of drying medium enthalpy and humidity status variation diagrams. We aimed to reduce the dryer energy consumption and exhaust gas emission. Using clean electric energy instead of a conventional dryer heat source, the drying system can cyclically dry high‐moisture grain with zero emissions. The experimental results indicate that the condensation effect is suitable when the ratio of the cool and hot air flows is 6:1 at a heat transfer coefficient of the condenser of 79.5 W/(m2.K) and a specific heat consumption of 3,916 kJ/kgH2O when compared with the Chinese standard of 7,500 kJ/kgH2O, that is, a maximum energy savings of 48%. This paper proposes a novel technological method and idea, studies the differences in energy efficiency, emissions reduction and clean production, and provides a reference for replacing the conventional grain dryer heat source of coal with electricity. Practical applications This drying system is applied in circular grain drying; a corn drying experiment indicates that compared with the specific energy consumption stipulated by the national standards of China, the highest energy savings is 48%, and zero release nonpollution is shown. The state parameters of the dried material and drying medium are displayed graphically in real time, and the operating parameters of the drying process are intuitively tracked in real time and automatically adjusted.

In our study, we developed a system to reduce both energy consumption and pollutant discharge during the drying process. We present a new technology, a stationary bed grain-drying test device based on the internal circulation of the drying medium (ICODM). A rice-drying experiment was carried out inside of it, and the influences of air temperature (AT) and air velocity (AV) on the energy and exergy efficiencies (EEE) as well as the improvement potential rate (IPR) and the sustainability index (SI) of the rice-drying process were studied. The following conclusions were obtained: when the rice was dried at a temperature of below 55 °C and an AV across the grain layer of 0.5 m/s, the average EEE during the drying process was 48.27–72.17% and 40.27–71.07%, respectively, demonstrating an increasing trend as the drying medium temperature increased. When the rice was dried using an AV across the grain layer in the range of 0.33–0.5 m/s and a temperature of 40 °C, the two values were 39.79–73.9% and 49.66–71.04%, respectively, demonstrating a decreasing trend as the drying medium flow velocity increased. IPR and SI were 4.1–8.5 J/s and 1.9–2.7, respectively, at a drying temperature of 30–55 °C and an AV of 0.33–0.5 m/s. These conclusions can provide helpful guidance for the optimization and control of the rice-drying process in terms of saving energy.

Grain drying is a complex heat and mass transfer process, which has the characteristics of a significant delay, multidisturbance, nonlinearity, strong coupling, and parameter uncertainty. Artificial intelligence (AI) control technology is suitable for solving such complex control problems. In this paper, the mechanism and data dual-drive with equivalent accumulated temperature (EAT) mutual-window AI-control method for continuous grain drying were proposed, and a control system was established. The experimental verification was carried out on the test platform of continuous grain drying. The results show that the method has the ability of implicit prediction, high accuracy, strong stability and self-adaptive ability, and the maximum control deviation of moisture at the outlet of the dryer is −0.58–0.3%.