Combined fuzzy based feedforward and bubble size distribution based feedback control for reagent dosage in copper roughing process. (March 2016)

• Record Type: Journal Article
• Authors: Zhu, Jianyong; Gui, Weihua; Liu, Jinpin; Xu, Honglei; Yang, Chunhua
• Is Part Of: Journal of process control. Volume 39(2016:Mar.)
• Page Start: 50
• Page End: 63
• Publication Date: 2016-03
• Subjects: Copper roughing -- Reagent dosage -- PDF -- Fuzzy -- MLS-SVM; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.12.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Bubble size is believed to be closely related to flotation performance and responds to changes in the reagent dosage, probability density function of bubble size based feedback predictive control for reagent dosage is proposed. Moreover, in order to eliminate the change of condition of given ore such as ore grade and handling capacity during flotation running, a fuzzy theory based feedforward compensator will be used to calculated the reagent dosage in advance according to ore grade and handling capacity. So, a combined fuzzy based feedforward (FBF) and bubble size distribution (BSD) based feedback reagent dosage control strategy is proposed to implement the product indices in copper roughing process. Abstract: A combined fuzzy based feedforward (FBF) and bubble size distribution (BSD) based feedback reagent dosage control strategy is proposed to implement the product indices in copper roughing process. A fuzzy theory based feedforward compensator will be used to calculated the reagent dosage in advance to eliminate the influence of large disturbances according to ore grade and handling capacity. Since the bubble size is believed to be closely related to flotation performance and responds to changes in the reagent dosage, using BSD based feedback predictive control calculates the reagent dosage to stabilize the flotation running. Instead of simple statistic feature, the bubble size with non-Gaussian feature is characterized to be probability density function (PDF) by using B-spline. A multi-output least square support vector machine (MLS-SVM) based is then applied to establish a dynamical relationship between the weights of B-spline and the reagent dosage since the weights are interrelated and related to the reagent dosage. A multiple step based optimization algorithm is finally proposed to determine the reagent dosage. Experimental results can show the effectiveness of the proposed method.