[ 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 link1 link2

[ 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 link1 link2

[ 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 link1 link2

[ 4 ] Engell S. Feedback control for optimal process operation. IFAC Proc Vol 2006; 39(2):13–26 link1 link2

[ 5 ] Mercangöz M, Doyle FJ III. Real-time optimization of the pulp mill benchmark problem. Comput Chem Eng 2008;32(4–5):789–804 link1 link2

[ 6 ] Adetola V, Guay M. Integration of real-time optimization and model predictive control. J Process Contr 2010;20(2):125–33 link1 link2

[ 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 link1 link2

[ 8 ] Bartusiak RD. NLMPC: A platform for optimal control of feed- or product-flexible manufacturing. In: Findeisen R, Allg?wer F, Biegler LT, editors Assessment and future directions of nonlinear model predictive control. Berlin: Springer; 2007. p. 367–81 link1 link2

[ 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 link1 link2

[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 link1 link2

[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 link1 link2

[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 link1 link2

[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 link1 link2

[14] Chachuat B, Srinivasan B, Bonvin D. Adaptation strategies for real-time optimization. Comput Chem Eng 2009;33(10):1557–67 link1 link2

[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 link1 link2

[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 link1 link2

[17] Kumral M. Genetic algorithms for optimization of a mine system under uncertainty. Prod Plann Contr 2004;15(1):34–41 link1 link2

[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 link1 link2

[19] Li Z, Ierapetritou M. Process scheduling under uncertainty: Review and challenges. Comput Chem Eng 2008;32(4–5):715–27 link1 link2

[20] Pinto JM, Grossmann IE. Assignment and sequencing models for the scheduling of process systems. Ann Oper Res 1998;81:433–66 link1 link2

[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 link1 link2

[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 link1 link2

[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 link1 link2

[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 link1 link2

[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 link1 link2

[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 link1 link2

[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 link1 link2

[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 link1 link2

[31] Chai T, Qin SJ, Wang H. Optimal operational control for complex industrial processes. Annu Rev Contr 2014;38(1):81–92 link1 link2

[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 link1 link2

[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 link1 link2

[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 link1 link2