Abstract
Modeling for the ball mill grinding process is still an imperative but difficult problem for the optimal control of mineral processing industry. Due to the integrated complexities of grinding process (strong nonlinearity, unknown mechanisms, multivariable, time varying parameters, etc.), a hybrid intelligent dynamic model is presented in this paper, which includes a phenomenological ball mill grinding model with a neurofuzzy network to describe the selection function of different operating conditions, a populace balance based sump model, a phenomenological hydrocyclone model with some reasoning rules for its parameters correction and a radius basis function network (RBFN) for fine particle size error compensation. With the production data from a ball mill grinding circuit of an iron concentration plant, the experiments and simulation results show the proposed hybrid intelligent modeling approach effective.
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