The present work proposes a novel industrial multi-field synergistic dryer with a drying capacity of 3.45 t/h. The energy, exergy, and quality aspects of the drying process were studied. An energy–exergy methodology was employed to estimate the energetic and exergetic performance, heat loss characteristics and heat recovery behavior of the dryer. Additionally, the quality of the dried paddy seeds was evaluated by its crackle ratio, generation potential, and generation rate. The results showed that the overall energy and exergy efficiency ranged from 13.26% to 56.63% and 39.03% to 60.23%, respectively. The improvement potential rates of the whole system varied from the lowest 8.49 kW to the highest 15.83 kW and respectively accounted for 15.81–29.48% of the total exergy input, indicating that the performance of the dryer is acceptable. The total recovered radiant energy and radiant exergy recover rate were respectively ascertained to be 237.64 MJ and 0.26 kW. As for the quality aspect, the generation potential and generation rate of the dried paddy seeds respectively ranged from 75% to 90% and 69% to 88% while the crackle ratio of the paddy seeds was 1%, which indicated that the quality performance of the dried seed is of economic viability.

Online detection of impurities content in the corn deep-bed drying process is the key technology to ensure stable operation and to provide data support for self-adapting control of drying equipment. In this study, an automatic approach to corn image acquisition, impurity classification and recognition, and impurities content detection based on machine vision technology are proposed. The multi-scale retinex with colour restore (MSRCR) algorithm is utilized to enhance the original image for eliminating the influence of noise. HSV (Hue, saturation, value) colour space parameter threshold is set for image segmentation, and the classification and recognition results are obtained combined with the morphological operation. The comprehensive evaluation index is adopted to quantitatively evaluate the test results. Online detection results show that the comprehensive evaluation index of broken corncobs, broken bracts, and crushed stones are 83.05%, 83.87%, and 87.43%, respectively. The proposed algorithm can quickly and effectively identify the impurities in corn images, providing technical support and a theoretical basis for monitoring impurities content in the corn deep-bed drying process.

The shell explosion by hot air drying is a critical step in the processing of Camellia oleifera fruit (COF), which directly affects the degree of the shell explosion, and the separation effect of Camellia oleifera seed and Camellia oleifera shell after the shell explosion of COF. To reveal the characteristics of the COF shell explosion, a hot air drying device was designed based on mass conservation and drying principles. The physical characteristics of COF and the evolution of drying parameters were thoroughly analyzed with a combination method of drying analysis and experimental. Moreover, under the conditions of air temperature 50–70 °C, relative humidity 20–50%, and air velocity 1.3–1.9 m/s, the internal relationship between COF shell explosion formation through hot air drying and the hot air drying medium was systematically investigated by response surface methodology, and a prediction model for the shell explosion rate of COF by hot air drying was constructed using statistical methods. Results demonstrated that decreasing the relative humidity and increasing the temperature and air velocity of the drying medium could reduce the dehydration time of COF. The moisture content of Camellia oleifera shell was found to be 177.45% d.b. (dry basis) at the initial cracking stage of COF. Furthermore, at temperatures ranging from 50 to 70 °C D_eff values of COF were estimated to be within the range of 0.915 × 10⁻⁹ to 1.782 × 10⁻⁹ m²/s. Similarly, at relative humidity levels of 20 to 50%, D_eff values ranged from 1.226 × 10⁻⁹ to 1.501 × 10⁻⁹ m²/s. At an air velocity of 1.3 to 1.9 m/s, D_eff values ranged from 0.956 × 10⁻⁹ to 1.501 × 10⁻⁹ m²/s. The measured values of the shell explosion rate were in close agreement with that calculated using the fitted model, with a correlation coefficient of 0.997 and a root mean square error of 0.9743. This study will provide a theoretical basis for optimizing the shell explosion process and improving shell explosion rate of COF by hot air drying.