Predictive modeling of forced air pre-cooling: A digital twin approach

The use of digital twin technology to achieve real-time perception and accurate prediction of the temperature of fruits and vegetables during forced air pre-cooling is an effective measure to ensure their quality to the greatest extent. However, current methods such as theoretical analysis and sensor testing have drawbacks including poor accuracy and potential damage to the structure of the produce. To address these issues, a digital twin modeling approach for forced air pre-cooling of spherical fruits and vegetables based on "theoretical analysis computation + real boundary correction" was proposed. First, a mathematical model for the forced air pre-cooling was established and validated experimentally. Then, a sample parameter database was built based on the mathematical model. Then, using the similarity principle, a general function expression for the forced air pre-cooling of spherical fruits and vegetables was proposed. Finally, a universal digital twin model was obtained by the least squares method. The results showed that the dimensionless temperature in forced air pre-cooling had an exponential function relationship with time. Based on the intermediate coefficient method, equations for the cooling coefficient ( C ) and lag factor (J) were obtained. The proposed theoretical equation showed excellent performance, with an absolute error of less than 1 °C and a prediction accuracy of more than 93 %. Predictability analysis showed that the equation could be accurately corrected using more than 80 sets of data, with a relative prediction deviation of less than 20 %. Overall, the digital twin modeling method proposed in this paper provides reliable theoretical support and practical application value for the forced air pre-cooling of spherical fruits and vegetables.