Cut tobacco drying process moisture prediction control method and system based on recurrent neural network

Abstract

The invention, which relates to the technical field of cut tobacco drying process moisture control, discloses a cut tobacco drying process moisture prediction control method and system based on a recurrent neural network. The method comprises the steps: step A, collecting related data of a cut tobacco drying process; step B, automatically identifying acquired trademark information to obtain control parameters; step C, judging the related data, and establishing a nonlinear prediction control model; step D, converting a nonlinear prediction model into a nonlinear prediction control model based on a recurrent neural network, and updating the weight of the recurrent neural network to obtain an outlet moisture content prediction value; and step E, constructing a performance index J, and acquiring an optimal moisture removal air door opening degree of the performance index J. According to the method, the nonlinear prediction control model is improved, the neural network training speed and stability are improved, and the stability of the outlet moisture content is improved.

Description

technical field [0001] The invention relates to the technical field of moisture control in the silk drying process, in particular to a method and system for predicting and controlling moisture in the silk drying process based on a recursive neural network. Background technique [0002] The drying process is an important process for moisture control of silk thread materials in the tobacco industry. The outlet moisture content is an important technological index of the silk drying process, and its process stability has a direct impact on the technological indexes of subsequent processes. At present, there are many problems in the control of outlet moisture in the drying process, and the most prominent are three points: First, due to the time delay in the drying process itself, the control system for silk drying cannot adjust the value of the manipulated variable according to the outlet moisture content measured in real time , so that the stability of the outlet moisture conte...

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Problems solved by technology

[0005] The present invention aims to solve the problem of poor stability of outlet moisture content in the drying process, and provides a method and system for predicting and controlling moisture in the drying process based on a recursive neural network. The predictive control model is transformed into a nonlinear predictive control model based on recurrent neural network, using recurrent neural network (RNN) to replace the recursive least squares method with forgetting factor in generalized predictive control, and predicting in real time according to the process variables obtained by real-time measurement The model is adjusted, and the optimal tide damper opening is obtained by predicting the moisture content of the outlet to increase the stability of the moisture content of the outlet

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