The added water control system based on neural network model and double parameter corrected for loosening and conditioning cylinder

Abstract

In order to improve the stability of outlet moisture in the of loosening and conditioning cylinder, we concentrate on loosening and conditioning cylinder, and design the prediction model of the added water volume based on neural network technology and the double parameter corrected control system of material balance and moisture deviation for loosening and conditioning cylinder by using historical production data. Taking the set value of inlet and outlet moisture of the loosening and conditioning cylinder as the input factor and the added water volume as the output factor, the prediction model of the added water volume was to predict the total volume of the added water. When there was a big deviation between the actual value and the set value of outlet moisture, the double parameter corrected control system of material balance and moisture deviation was used to correct the deviation, so as to improve the outlet moisture stability and the control accuracy of the added water volume. Using Cigarette brand “diamond (hard-case Yingbin)” produced by Zhangjiakou cigarette factory to built model and analysis, the results showed that after the improvement, the standard deviation of output moisture of loosening and conditioning cylinder reduced by 0.27%, and the control stability and the control level of production process were improved.