Study on the application of a new prediction model based on penalty constraint to flotation process

The flotation process is a complicated multi-input and multi-output process with the characteristic of strong non-linearity, heavy coupling and large delay. Due to the difficulty of measuring the concentrate grade and tailing grade index online, and its dynamics varying with the process conditions, such a control objective by far is difficult to achieve by the existing control methods to control the product quality indices into their technical targeted ranges and even cause fault work-condition. This paper presents a clustering algorithm based on punishing constraint of swarm intelligence (PCSI). Directed by the nature of PSO, PCSI could randomly search the centers of clusters and obtain the number of clusters. The process prior knowledge and PCA method are used to reduce dimension of the input data and select auxiliary variables. And then a new hybrid recursive algorithm of RBFNN based on simplified rival penalized competitive learning method (SRPCL) to make an adaptive clustering is developed. The method proposed has successfully been applied to two production lines of a mineral processing plant of Anshan Iron and Steel Group Corporation, and its effectiveness is proved evidently.