LSTM-based continuous real-time prediction method for moisture of environmental temperature and humidity in tobacco shred process

Abstract

The invention discloses an LSTM-based continuous real-time prediction method for the moisture of ambient temperature and humidity in a tobacco shred process. In order to guarantee the stability of theoutlet moisture of perfumed tobacco shreds in a multi-batch and continuous production environment, the LSTM-based deep learning iterative prediction method is realized, the influence of moisture analysis and the prediction of environmental temperature and humidity in the tobacco shred process on the outlet moisture of the perfumed tobacco shreds is analyzed, and a real-time prediction model of the outlet moisture of the perfumed tobacco shreds is established; the influence trend of the predicted environmental temperature and humidity on the water yield of the perfumed tobacco shreds is obtained through model solving, and finally, the inspection is performed by adopting a method of comparing a model prediction value with an actual measurement value. According to the method, the predictionof the cut tobacco moisture can be realized, so that the prediction readiness and efficiency are improved.

Description

technical field [0001] The invention relates to the field of real-time prediction of intelligent manufacturing, and is specifically aimed at an LSTM-based real-time prediction method of ambient temperature and humidity in a continuous, multi-batch, feedback hysteresis environment in the cut tobacco production process. Background technique [0002] With the modernization of industry and the progress of science and technology, as an important part of my country's economic income, my country's tobacco industry has also entered a new stage of development. Cigarette production is a relatively complicated process, and each link has a significant impact on the quality of cigarettes and the consumption of materials. As an important link in the tobacco processing process, tobacco shred processing can effectively ensure the stability of cigarette quality during the processing process due to its strong continuity and correlation, as well as a variety of process and equipment factors. ...

AI Technical Summary

Problems solved by technology

However, in the tobacco shredded production process, multiple batches of tobacco leaves need to be predicted in real time in a continuous production environment. The control parameters of each batch are changing in real time. At the same time, research on the control of moisture content in shredded tobacco after flavoring Mainly focus on intelligent control, PID control and the combination of the two. Because the control effect has a certain hysteresis, the potential influence of ambient temperature and humidity cannot be considered, which will easily lead to a decrease in the stability of the moisture content of the tobacco after flavoring.

Therefore, the traditional LSTM algorithm is difficult to achieve real-time and accurate prediction under such hysteresis, continuous, and multi-batch conditions.

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