Modeling and Application of Temporal Correlation of Grain Temperature during Grain Storage

Hongwei Cui, Qu Zhang, Wenfu Wu, Haolei Zhang, Jiangtao Ji and Hao Ma ()
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Hongwei Cui: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Qu Zhang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Wenfu Wu: College of Biology and Agricultural Engineering, Jilin University, Changchun 130022, China
Haolei Zhang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Jiangtao Ji: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Hao Ma: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China

Agriculture, 2022, vol. 12, issue 11, 1-16

Abstract: Temperature measurement system malfunction and sensor failure in grain storage warehouses can lead to missing grain temperature data on some days. Missing data is not conducive to the monitoring of grain storage conditions. This paper establishes mathematical models of temporal correlation coefficients of grain temperature and storage time in different planes, and analyzes the influence of storage state change on grain temperature correlation. The historical grain situation data for about one year were selected from 27 flat grain storage warehouses distributed in the second to seventh grain storage ecological zones in China. In addition, correlation coefficients of grain temperature were then calculated on the XOY , XOZ and YOZ planes of each warehouse. During this process, the time interval included 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days, meaning that the correlation coefficients between the grain temperature on the day and the grain temperature after storage for 1, 7, 14, 21, 28, 35, 42, 49, 56, 63 and 70 days were calculated. Next, the correlation coefficients from the same time intervals and planes in each warehouse were sequentially connected to form arrays of correlation coefficients. Then, the 3σ-threshold setting methods and DBSCAN (density-based spatial clustering of applications with noise) method were used to analyze the correlation coefficients those arrays. According to the results, we set the correlation coefficient thresholds for each plane ( XOY , XOZ and YOZ planes) at each time interval. The models were then established regarding the correlation coefficient thresholds and storage time intervals. Subsequently, the sum of squares for error (SSE), coefficient of determination ( R 2 ), and root mean square error (RMSE) were chosen to evaluate the models, with the results showing that the effect of the model established by the threshold set by the 3σ-setting method, with SSE, R 2 and RMSE of 0.056, 0.9771 and 0.0748, respectively, was better than the model established using the DBSCAN method. Finally, the correlation coefficients of grain temperatures with empty warehouse, new grain addition, aeration and self-heating were analyzed. The results show that the four modes in a certain time interval (e.g., 30 days) does not meet the correlation coefficient threshold during normal storage. The result can provide a theoretical basis for grain storage condition detection when grain temperature data is intermittently missing.

Keywords: grain storage; grain storage condition; temperature data; correlation; clustering (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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