Analysis and prediction of grain temperature from air temperature to ensure the safety of grain storage

• Main Authors: Qiyang Wang, Jiachang Feng, Feng Han, Wenfu Wu, Shucheng Gao
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2020-01-01
• Series: International Journal of Food Properties
• Subjects: grain storage; temperature prediction; food security; fourier analysis; time lag; akaike information criterion (aic)
• Online Access: http://dx.doi.org/10.1080/10942912.2020.1792922

Food security is an influential factor for the development and future of humanity. Stored grain temperature is a key physical variable for grain quality control and management in stored-grain ecosystem. In this study, a prediction model using Fourier series based on least-squares method is introduced to describe the average temperature movement of grain pile and the daily air temperature, naming hysteresis cycle model (HCM). Then the HCM based on the Fourier analysis is proposed that incorporating with the least-squares method, the Fourier series model is applied to forecast the grain pile temperature from the daily air temperature. The HCM is built by finding relationship called conversion coefficients between the air and grain pile temperature that is constructed on the basis of Fourier analysis, and the optimal order of the model is determined by Akaike information criteria. Our method can reflect the time lag of the grain pile temperature changes with the change of air temperature and requires only a single variable (i.e. air temperature). Experiments including model fitting, temperature prediction, and results comparison for a period of 425 days from a real-world granary, yield satisfied results agreeing well with actual observation values. As a result of model validation, the proposed method has the root mean square error (RMSE) values ranging from 1.6°C to 2.3°C with average RMSE of 1.9°C. In other words, the proposed model can predict grain pile temperature with a high accuracy once an acceptable relationship between air temperature and grain pile temperature has been constructed from previously temperature data. Result analysis indicates effectiveness and workability of the proposed model for stored grain temperature prediction.