Coordinated optimization setting of reagent dosages in roughing-scavenging process of antimony flotation

Bin-fang Cao , Yong-fang Xie , Wei-hua Gui , Chun-hua Yang , Jian-qi Li

Journal of Central South University ›› 2018, Vol. 25 ›› Issue (1) : 95 -106.

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Journal of Central South University ›› 2018, Vol. 25 ›› Issue (1) : 95 -106. DOI: 10.1007/s11771-018-3720-0
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Coordinated optimization setting of reagent dosages in roughing-scavenging process of antimony flotation

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Abstract

Considering the influence of reagent adjustment in different flotation bank on the final production index and the difficulty of establishing an effective mathematical model, a coordinated optimization method for dosage reagent based on key characteristics variation tendency and case-based reasoning is proposed. On the basis of the expert reagent regulation method in antimony flotation process, the reagent dosage pre-setting model of the roughing–scavenging bank is constructed based on case-based reasoning. Then, the sensitivity index is used to calculate the key features of reagent dosage. The reagent dosage compensation model is constructed based on the variation tendency of the key features in the roughing and scavenging process. At last, the prediction model is used to finish the classification and discriminant analysis. The simulation results and industrial experiment in antimony flotation process show that the proposed method reduces fluctuation of the tailings indicators and the cost of reagent dosage. It can lay a foundation for optimizing the whole process of flotation.

Keywords

froth flotation / image features / optimization setting / coordinated optimization

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Bin-fang Cao, Yong-fang Xie, Wei-hua Gui, Chun-hua Yang, Jian-qi Li. Coordinated optimization setting of reagent dosages in roughing-scavenging process of antimony flotation. Journal of Central South University, 2018, 25(1): 95-106 DOI:10.1007/s11771-018-3720-0

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References

[1]

JovanovicI, MiljanovicI. Contemporary advanced control techniques for flotation plants with mechanical flotation cells–A review [J]. Minerals Engineering, 2015, 70: 228-249

[2]

MorarS H, HarrisM C, BradshawD J. The use of machine vision to predict flotation performance [J]. Minerals Engineering, 2012, 36(10): 31-36

[3]

ZhouK-j, YangC-h, GuiW-hua. Clustering-driven watershed adaptive segmentation of bubble image [J]. Journal of Central South University of Technology, 2010, 17(5): 1049-1057

[4]

Perez-CorreaR, GonaalezG, CasaliA. Dynamic modeling and advanced multivariable control of conventional flotation circuits [J]. Minerals Engineering, 1998, 11(4): 333-346

[5]

HodouinD, BazinC, GagnonE. Feed forwardfeedback predictive control of a simulated flotation bank [J]. Power Technology, 2000, 108(2): 173-179

[6]

NaikP K, ReddyP, MisraV N. Interpretation of interaction effects and optimization of reagent dosages for fine coal flotation [J]. International Journal of Mineral Processing, 2005, 75(1): 83-90

[7]

ZhuJ-y, GuiW-h, YangC-hua. Probability density function of bubble size based reagent dosage control for flotation process [J]. Asian Journal of Control, 2014, 16(3): 765-777

[8]

LiuJ J, MacgregorJ F. Froth-based modeling and control of flotation processes [J]. Minerals Engineering, 2008, 21(6): 642-651

[9]

GengZ-x, ChaiT-you. Intelligently optimal index setting for flotation process by CBR [J]. Journal of Northeastern University: Natural Science, 2008, 29(6): 761-764

[10]

ZhouP, ChaiT-y, WangHong. Intelligent optimal-setting control for grinding circuits of mineral processing process [J]. IEEE Transactions on Automation Science & Engineering, 2009, 6(4): 730-743

[11]

LiH-b, ZhengX-p, ChaiT-you. Hybrid intelligent optimal control in flotation processes [J]. Journal of Northeastern University: Natural Science, 2012, 33(1): 1-5

[12]

CaoB-f, XieY-f, GuiW-hua. Integrated prediction model of bauxite concentrate grade based on distributed machine vision [J]. Minerals Engineering, 2013, 53: 31-38

[13]

KaaritinenJ, HatonenJ, Hyotyniemih, MiettunenJ. Machine-vision-based control of zinc flotation—A case study [J]. Control Engineering Practice, 2006, 14: 1455-1466

[14]

GianniB, PatrickP J, JaysonT J. Application of numerical image analysis to process diagnosis and physical parameter measurement in mineral processes-Part I: Flotation control based on froth textural characteristics [J]. Minerals Engineering, 2006, 19(6–8): 734-747

[15]

ZhuJ-y, GuiW-h, YangC-hua. Probability density function of bubble size based reagent dosage predictive control for copper roughing flotation [J]. Control Engineering Practice, 2014, 29: 1-12

[16]

ZhaoH-w, XieY-f, JiangZ-h, XuD-g, YangC-hua. An intelligent optimal setting approach based on froth features for level of flotation cells [J]. Acta Automatica Sinica, 2014, 40(6): 1086-1097

[17]

WangY-l, ChenX-f, ZhouX-j. Hybrid intelligence model based on image features for the prediction of flotation concentrate grade [J]. Abstract and Applied Analysis, 2014

[18]

WuJ, XieY-f, YangC-hua. An unsupervised reduction method for the selection of flotation froth image characters and its application [J]. Information and Control, 2014, 43(3): 314-317

[19]

KaartiinenJImage analysis in mineral flotation [D], 1999, Helsinki, Helsinki University of Technology Control Engineering Laboratory

[20]

CaoB-f, XieY-f, YangC-h, GuiW-h, WangX-li. Integrated modeling for production index of bauxite flotation based on multi-source data [J]. Control Theory & Applications, 2014, 31(9): 1252-1261

[21]

LiY-g, GuiW-hua. Optimal control for zin solution purification based on interacting CSTR models [J]. Journal of Process Control, 2012, 22(10): 1878-1889

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