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Engineering    2017, Vol. 3 Issue (2) : 183 -187
Research |
Recent Progress on Data-Based Optimization for Mineral Processing Plants
Jinliang Ding(),Cuie Yang,Tianyou Chai
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China

In the globalized market environment, increasingly significant economic and environmental factors within complex industrial plants impose importance on the optimization of global production indices; such optimization includes improvements in production efficiency, product quality, and yield, along with reductions of energy and resource usage. This paper briefly overviews recent progress in data-driven hybrid intelligence optimization methods and technologies in improving the performance of global production indices in mineral processing. First, we provide the problem description. Next, we summarize recent progress in data-based optimization for mineral processing plants. This optimization consists of four layers: optimization of the target values for monthly global production indices, optimization of the target values for daily global production indices, optimization of the target values for operational indices, and automation systems for unit processes. We briefly overview recent progress in each of the different layers. Finally, we point out opportunities for future works in data-based optimization for mineral processing plants.

Keywords Data-based optimization      Plant-wide global optimization      Mineral processing      Survey     
Corresponding Authors: Jinliang Ding   
Just Accepted Date: 21 March 2017   Online First Date: 13 April 2017    Issue Date: 27 April 2017
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Jinliang Ding
Cuie Yang
Tianyou Chai
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Jinliang Ding,Cuie Yang,Tianyou Chai. Recent Progress on Data-Based Optimization for Mineral Processing Plants[J]. Engineering, 2017, 3(2): 183 -187 .
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1   Ding J, Chai T, Wang H. Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization. IEEE Trans Neural Netw 2011;22(3):408–19
doi: 10.1109/TNN.2010.2102362
2   Jäschke J, Skogestad S. NCO tracking and self-optimizing control in the context of real-time optimization. J Process Contr 2011;21(10):1407–16
doi: 10.1016/j.jprocont.2011.07.001
3   Würth L, Hannemann R, Marquardt W. A two-layer architecture for economically optimal process control and operation. J Process Contr 2011;21(3):311–21
doi: 10.1016/j.jprocont.2010.12.008
4   Engell S. Feedback control for optimal process operation. IFAC Proc Vol 2006; 39(2):13–26
doi: 10.3182/20060402-4-BR-2902.00013
5   Mercangöz M, Doyle FJ III. Real-time optimization of the pulp mill benchmark problem. Comput Chem Eng 2008;32(4–5):789–804
doi: 10.1016/j.compchemeng.2007.03.004
6   Adetola V, Guay M. Integration of real-time optimization and model predictive control. J Process Contr 2010;20(2):125–33
doi: 10.1016/j.jprocont.2009.09.001
7   []Qin SJ, Cherry G, Good R, Wang J, Harrison CA. Semiconductor manufacturing process control and monitoring: A fab-wide framework. J Process Contr 2006;16(3):179–91
doi: 10.1016/j.jprocont.2005.06.002
8   Bartusiak RD. NLMPC: A platform for optimal control of feed- or product-flexible manufacturing. In: Findeisen R, Allgöwer F, Biegler LT, editors Assessment and future directions of nonlinear model predictive control. Berlin: Springer; 2007. p. 367–81
doi: 10.1007/978-3-540-72699-9_30
9   Yu G, Chai T, Luo X. Multiobjective production planning optimization using hybrid evolutionary algorithms for mineral processing. IEEE Trans Evol Comput 2011;15(4):487–514
doi: 10.1109/TEVC.2010.2073472
10   Kong W, Ding J, Chai T, Zheng X, Yang S. A multiobjective particle swarm optimization algorithm for load scheduling in electric smelting furnaces. In: Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES); 2013Apr 16–19; Piscataway: IEEE; 2013. p. 188–95
doi: 10.1109/cies.2013.6611748
11   Yu G, Chai T, Luo X. Two-level production plan decomposition based on a hybrid MOEA for mineral processing. IEEE Trans Autom Sci Eng 2013;10(4):1050–71
doi: 10.1109/TASE.2012.2221458
12   Chai T, Ding J, Yu G, Wang H. Integrated optimization for the automation systems of mineral processing. IEEE Trans Autom Sci Eng 2014;11(4):965–82
doi: 10.1109/TASE.2014.2308576
13   Marchetti AG, Ferramosca A, González AH. Steady-state target optimization designs for integrating real-time optimization and model predictive control. J Process Contr 2014;24(1):129–45
doi: 10.1016/j.jprocont.2013.11.004
14   Chachuat B, Srinivasan B, Bonvin D. Adaptation strategies for real-time optimization. Comput Chem Eng 2009;33(10):1557–67
doi: 10.1016/j.compchemeng.2009.04.014
15   Chen VYX. A 0–1 goal programming model for scheduling multiple maintenance projects at a copper mine. Eur J Oper Res 1994;76(1):176–91
doi: 10.1016/0377-2217(94)90015-9
16   Bevilacqua M, Ciarapica FE, Giacchetta G. Critical chain and risk analysis applied to high-risk industry maintenance: A case study. Int J Proj Manag 2009;27(4):419–32
doi: 10.1016/j.ijproman.2008.06.006
17   Kumral M. Genetic algorithms for optimization of a mine system under uncertainty. Prod Plann Contr 2004;15(1):34–41
doi: 10.1080/09537280310001654844
18   Cisternas LA, Gálvez ED, Zavala MF, Magna J. A MILP model for the design of mineral flotation circuits. Int J Miner Process 2004;74(1–4):121–31
doi: 10.1016/j.minpro.2003.10.001
19   Li Z, Ierapetritou M. Process scheduling under uncertainty: Review and challenges. Comput Chem Eng 2008;32(4–5):715–27
doi: 10.1016/j.compchemeng.2007.03.001
20   Pinto JM, Grossmann IE. Assignment and sequencing models for the scheduling of process systems. Ann Oper Res 1998;81:433–66
doi: 10.1023/A:1018929829086
21   Chai T, Ding J, Wang H. Multi-objective hybrid intelligent optimization of operational indices for industrial processes and application. IFAC Proc Vol 2011; 44(1):10517–22
doi: 10.3182/20110828-6-IT-1002.01753
22   Ding J, Chai T, Wang H, Wang J, Zheng X. An intelligent factory-wide optimal operation system for continuous production process. Enterprise Inf Syst 2016;10(3):286–302
doi: 10.1080/17517575.2015.1065346
23   Ding J, Wang H, Liu C, Chai T. A multiobjective operational optimization approach for iron ore beneficiation process. In: Proceedings of the 2013 International Conference on Advanced Mechatronic Systems; 2013 Sep 25–27; Luoyang, China.Piscataway: IEEE; 2013. p. 582–7
doi: 10.1109/ICAMechS.2013.6681710
24   Ding J, Modares H, Chai T, Lewis FL. Data-based multiobjective plant-wide performance optimization of industrial processes under dynamic environments. IEEE Trans Industr Inform 2016;12(2):454–65
doi: 10.1109/TII.2016.2516973
25   Yang C, Ding J. Constraint dynamic multi-objective evolutionary optimization for operational indices of beneficiation process. J Intell Manuf. In press.
26   Ma Y, Sun Z, Gao H. Incremental associate data mining in real time database. J Comput Res Develop 2000;37(12):1446–51. Chinese.
27   Ding J, Chai T, Cheng W, Zheng X. Data-based multiple-model prediction of the production rate for hematite ore beneficiation process. Control Eng Pract 2015;45:219–29
doi: 10.1016/j.conengprac.2015.08.015
28   Liu C, Ding J, Toprac AJ, Chai T. Data-based adaptive online prediction model for plant-wide production indices. Knowl Inf Syst 2014;41(2):401–21
doi: 10.1007/s10115-014-0757-8
29   Liu C, Ding J, Chai T. Robust prediction for quality of industrial processes. In: Proceedings of the 2014 IEEE International Conference on Information and Automation (ICIA); 2014 Jul 28–30; Hailar, China.Piscataway: IEEE; 2014. p. 1172–5
doi: 10.1109/icinfa.2014.6932826
30   Ding J, Chai T, Wang H, Chen X. Knowledge-based global operation of mineral processing under uncertainty. IEEE Trans Industr Inform 2012;8(4):849–59
doi: 10.1109/TII.2012.2205394
31   Chai T, Qin SJ, Wang H. Optimal operational control for complex industrial processes. Annu Rev Contr 2014;38(1):81–92
doi: 10.1016/j.arcontrol.2014.03.005
32   Chai T, Ding J, Wu F. Hybrid intelligent control for optimal operation of shaft furnace process. Cont Eng Pract 2011;19(3):264–75
doi: 10.1016/j.conengprac.2010.05.002
33   Zhou P, Chai T, Sun J. Intelligence-based supervisory control for optimal operation of a DCS-controlled grinding system. IEEE Trans Contr Syst Technol 2013;21(1):162–75
doi: 10.1109/TCST.2012.2182996
34   Zhou P, Lu S, Yuan M, Chai T. Survey on higher-level advanced control for grinding circuits operation. Powder Technol 2016;288:324–38
doi: 10.1016/j.powtec.2015.11.010
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