Abstract
Objective: Solve the problems of difficulties of Daqu fermentation detection, judge the fermentation state judgement, and controlling. Methods: The Tent-SSA optimized BP neural network algorithm Daqu fermentation humidity prediction model and dynamic threshold control algorithm were proposed to realize real-time judgment of Daqu state and Daqu fermentation control during Daqu fermentation process. Results: The error predicted by The simulation prediction model for humidity prediction had low error (0.596%), good robust performance and fast convergence speed. Conclusion: The Daqu monitoring system based on this model is accurate and reliable.
Publication Date
10-16-2022
First Page
93
Last Page
97
DOI
10.13652/j.spjx.1003.5788.2022.90164
Recommended Citation
Jun-jie, LIAO; Guang-zhong, HU; Qiu, XIA; Wen-chang, ZHU; and Qiu-yue, BIN
(2022)
"Monitoring system of Daqu fermentation humidity based on improved SSA optimization prediction model,"
Food and Machinery: Vol. 38:
Iss.
9, Article 17.
DOI: 10.13652/j.spjx.1003.5788.2022.90164
Available at:
https://www.ifoodmm.cn/journal/vol38/iss9/17
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