Modeling of vertical mill raw meal grinding process and optimal setting of operating parameters based on wavelet neural network

Abstract

The stability of vertical mill raw meal grinding process affect the yield and quality of cement clinker. Due to the nonlinear of grinding process, random variation of working conditions, and large lag of the offline index test, it is difficult to establish an accurate mathematics model, thus cannot collect the optimizing operating parameters of vertical mill in time. In this paper, based on the principal component analysis (PCA) for the related variables, a production index prediction model of vertical mill raw meal grinding process was established using wavelet neural network (WNN) and compared with the BP network model, and the validity of the novel model was verified. Then, based on the prediction model and related constraint conditions, the parametric optimization model was established, wherein, the optimal operating setting value under typical working conditions was obtained by using particle swarm optimization algorithm, and an optimal case base was established; through the case inquiry and revision, the optimal set points under the current conditions was obtained. The simulation results showed that, the novel wavelet neural network model and the parameter optimizing setting method could adapt to the changing of process indicators, and could provide optimal parameter value to make the production performance meet expectations, meanwhile achieved the optimizing goal.