Modeling of operating parameters for wet ball mill by modified GA-KPLS

Load of the ball mill affects the productivity, quality and energy consumption of the grinding process. But sensors are not available for the direct measurement of the key parameters for mill load (ML). A new soft sensor approach based on the shell vibration signals to measure the operating parameters is proposed in this paper. Vibration signal is first transformed into power spectral density (PSD) via fast Fourier transform (FFT), such that the relative amplitudes of different frequencies could contain information about operating parameters. As the spectral curve consists of a set of small peaks, the masses and the central frequencies of the peaks are extracted as the spectral features, then the kernel partial least square (KPLS) is used to built the soft sensor model. The kernel parameters, the input variables of the models including the masses and the central frequencies of the peaks are selected by Genetic algorithm (GA). At last, a new approach for the updating of the KPLS model is proposed. Experimental results show that proposed method has higher accuracy and better predictive performance than the other approaches.