Dynamic prediction model for mixed concentrate grade of mineral processing plant

Author

Ding, Jinliang ; Chai, Tianyou ; Wang, Hong

Author_Institution

Key Lab. of Integrated Autom. of Process Ind., Northeastern Univ., Shenyang, China

fYear

2010

fDate

15-17 Dec. 2010

Firstpage

6773

Lastpage

6778

Abstract

A non-linear modelling approach of dynamic prediction model for mixed concentrate grade consisting of a linear part and a nonlinear part is developed. The nonlinear part is implemented using the least squares support vector machine (LS-SVM), where the problem of selecting model parameters is transformed into the probability distribution function (PDF) control of the modelling error. Both the PDF control based and minimum entropy based model parameter selection approaches are proposed. The experiment results show the effectiveness of the proposed approaches.

Keywords

entropy; least squares approximations; mineral processing; nonlinear control systems; prediction theory; process control; statistical distributions; support vector machines; dynamic prediction model; least square support vector machine; mineral processing plant; mixed concentrate grade; model parameter selection approach; modelling error; nonlinear modelling; probability distribution function; Data models; Entropy; Magnetic separation; Ores; Predictive models; Production;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control (CDC), 2010 49th IEEE Conference on

Conference_Location

Atlanta, GA

ISSN

0743-1546

Print_ISBN

978-1-4244-7745-6

Type

conf

DOI

10.1109/CDC.2010.5717944

Filename

5717944