It is difficult to measure the process parameters online in the bauxite froth flotation process because the slurry deposits quickly. Especially

frequent change of the characteristics of the ore makes the process parameters change from time to time. So that

the static soft sensing models

such as the neural network model

which was obtained by a fixed set of training samples

may not track the dynamic characteristics of the process caused by change of the ore resource. And

thus

model mismatch problem occurs. In this paper

for model mismatch problem under various ore sources

dynamic RBF neural network modeling method based on the hidden layer node dynamic allocation and model parameters dynamic correction strategy is proposed. And the model is used for online measurement of the pH of the slurry in the flotation process

simulation results show that the dynamic model can solve the model mismatch problem well.