Correspondence analysis and establishment of evaluation model of classification performance indices for a turbo air classifier

The operational parameters of a turbo air classifier including feeding speed, rotor cage's rotary speed and air inlet velocity affect its classification performance directly, such as cut size, classification precision, classification efficiency, fine powder yield, particle fineness and degree of dispersion. Current methods of optimizing operational parameters and improving the classification performance of a turbo air classifier are almost single objective decision only for one of the classification performance indices. In this paper, the multi‐objective programming (MOP) model on classification performance for a turbo air classifier is established to evaluate these performance indices comprehensively and achieve optimal classification performance. To minimize the effect of repeatability within these classification performance indices, correspondence analysis is applied to determine the evaluation indices of this MOP model. According to correspondence analysis on the fine talc classification experimental data as well as the calcium carbonate classification experimental data, there is a very strong correlation between cut size and D90; there is also a very strong correlation between cut size and fine powder yield. Thus D90 and fine powder yield are filtered out and they aren't discussed in the evaluation model. The variation coefficient method is introduced to calculate weights of the evaluation function, and the dimensionless transformation method is used to eliminate the effects of different dimension. Thus, the optimal solution among the experimental data is obtained through solving the evaluation function. For the talc classification experiments, the optimal operational parameter combinations are: the feeding speed is 40 kg · h^–1, the air inlet velocity is 5 m · s^–1 and the rotor cage's rotary speed is 1200 ? min^–1. The classification performance indices are: cut size is 16.5 μm, classification precision index is 0.59, Newton classification efficiency is 57% and degree of dispersion is 2.13. For the calcium carbonate classification experiments, the optimal operational parameter combinations are: the feeding speed is 92 kg · h^–1, the air inlet velocity is 12 m · s^–1 and the rotor cage's rotary speed is 1200 ? min^–1. The classification performance indices are: cut size is 31.4 μm, classification precision index is 0.74, Newton classification efficiency is 74% and degree of dispersion is 1.27. This evaluation model avoids the limitation of evaluation for the single classification performance index and incomplete information got by the means of single factor experiment of operational parameters. It also provides the quantitative evaluating criteria for classification performance of a turbo air classifier, which offers a theoretical basis for effective production. This multi‐objective programming optimizing method and evaluation model on classification performance can be applied to other dynamic air classifiers as well.