Abstract:

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.

Key words: froth flotation process, dynamic RBF neural network, model mismatch, migration of working condition

摘要：

铝土矿泡沫浮选过程中,因矿浆的快速沉淀等原因工艺参数在线检测困难,且入矿性质变化频繁,造成浮选过程参数随入矿的变化而不断改变。而通常建立的静态软测量模型利用固定样本集训练得到,当矿源变化时容易发生模型失配现象,使模型不能跟踪当前对象。针对变矿源下的模型失配问题,本文提出基于隐层节点动态分配和模型参数动态修正策略的RBF神经网络建模方法,用于铝土矿浮选过程酸碱度的在线检测建模。实际生产数据仿真结果表明该方法能够有效解决模型失配的问题。

关键词: 泡沫浮选过程, 动态RBF神经网络, 模型失配, 工况迁移