Abstract
In the bauxite flotation process, concentrate grade and tailings grade are key production indicators; however, they are difficult to measure online. It is also difficult to develop an effective mathematical model for the process because of the complex non-linear and uncertain relationship among the feed parameters (feed grade, pulp density, slurry particle size, etc.), froth features and production indicators. Therefore, an online hybrid modeling method is proposed by analyzing the multiple parameters that affect the production indicators. First, according to the correlation and redundancy in the feed and froth feature parameters, the kernel principle component analysis (KPCA) is used to reduce the number of the parameters. Then, a neutral network model of the regular extreme learning machine (RELM), which is based on wavelet function, is presented to predict these two indicators. To improve generalization capability and prediction accuracy, information entropy is used to distribute the weight of the two models based on their predicting error. At last, an on-line updating strategy of the hybrid model is constructed in order to investigate the influence of the working conditions. The proposed method is tested on the diasporic-bauxite flotation process and shows high predictive accuracy and generalization capability. It lays the foundation for optimal control of the operation parameters based on mineral grade in the flotation process.
Highlights
Online modeling is a useful tool for operating complex industrial processes
In the bauxite flotation process, the concentrate grade and the tailings grade, which is measured by the mass ratio of Al2O3 and SiO2 (m (Al2O3)/m (SiO2) =A/S),are the main production indicators, but they are hard to achieve by online measurements (Morar et al, 2012, CAO et al, 2013, Moolman et al, 1996) and mainly depend on human laboratory analysis
For the high-dimensional nonlinear characteristics of the feed parameters, the method of using kernel principal component analysis (KPCA) to extract the principal feature is proposed in Schölkopf et al(1998) and LI et al (2012); the magnetite grade prediction model is established, which demonstrates that the kernel principle component analysis (KPCA) is capable of reducing the data dimension, eliminating redundancy among data, and further improving the model accuracy
Summary
Online modeling is a useful tool for operating complex industrial processes. Updated models are needed for early reaction to disturbances that affect the process production indicators and the end product quality. For the high-dimensional nonlinear characteristics of the feed parameters, the method of using kernel principal component analysis (KPCA) to extract the principal feature is proposed in Schölkopf et al(1998) and LI et al (2012); the magnetite grade prediction model is established, which demonstrates that the KPCA is capable of reducing the data dimension, eliminating redundancy among data, and further improving the model accuracy. This will result in an unsatisfactory dynamic tracking ability for the predictive model.
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