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Volume 29 Issue 4
Apr.  2022
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文章导航 >  矿物冶金与材料学报（英文）  > 2022  >  29(4): 611-625
Bei Sun, Juntao Dai, Keke Huang, Chunhua Yang, and Weihua Gui, Smart manufacturing of nonferrous metallurgical processes: Review and perspectives, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 611-625. https://doi.org/10.1007/s12613-022-2448-x
Cite this article as:
Bei Sun, Juntao Dai, Keke Huang, Chunhua Yang, and Weihua Gui, Smart manufacturing of nonferrous metallurgical processes: Review and perspectives, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 611-625. https://doi.org/10.1007/s12613-022-2448-x
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特约综述

有色冶金过程智能制造：综述与展望

  • 1)

    中南大学自动化学院, 长沙 410083, 中国

  • 2)

    鹏城实验室, 深圳 518000, 中国

  • 通讯作者:

    黄科科    E-mail: huangkeke@csu.edu.cn

    桂卫华    E-mail: gwh@csu.edu.cn

文章亮点

  • (1) 介绍了有色冶金建模、过程监测、优化、控制等方面的研究进展。
  • (2) 阐述了有色冶金行业智能优化制造的前景。
  • (3) 分析了有色冶金智能优化制造需应对的挑战。
  • 有色冶金工业是一个国家经济的基石产业。随着人工技术的发展，对环境保护、产品质量、生产效率的要求越来越高，应用智能制造技术全面感知生产状态、智能优化工艺操作的重要性正得到业界的广泛认可。本文首先对有色冶金行业的智能优化制造进行了简要的总结，综述了有色冶金过程运行优化关键技术的研究进展，包括生产管理、配料优化、建模、过程监控、优化和控制。然后，阐述了有色冶金行业智能优化制造的前景。最后，讨论了有色冶金行业在智能优化制造方面潜在的主要研究方向和挑战。
    • Invited Review

    Smart manufacturing of nonferrous metallurgical processes: Review and perspectives

    + Author Affiliations
      Abstract
    • The nonferrous metallurgical (NFM) industry is a cornerstone industry for a nation’s economy. With the development of artificial technologies and high requirements on environment protection, product quality, and production efficiency, the importance of applying smart manufacturing technologies to comprehensively percept production states and intelligently optimize process operations is becoming widely recognized by the industry. As a brief summary of the smart and optimal manufacturing of the NFM industry, this paper first reviews the research progress on some key facets of the operational optimization of NFM processes, including production and management, blending optimization, modeling, process monitoring, optimization, and control. Then, it illustrates the perspectives of smart and optimal manufacturing of the NFM industry. Finally, it discusses the major research directions and challenges of smart and optimal manufacturing for the NFM industry. This paper will lay a foundation for the realization of smart and optimal manufacturing in nonferrous metallurgy in the future.
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    • [1]
      W.H. Gui, X.F. Chen, C.H. Yang, and Y.F. Xie, Knowledge automation and its industrial application, Sci. Sin.-Inf., 46(2016), No. 8, p. 1016. doi: 10.1360/N112016-00065
      [2]
      Z.M. Lü, T.R. Jiang, and Z.W. Li, Multiproduct and multistage integrated production planning model and algorithm based on an available production capacity network, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1343. doi: 10.1007/s12613-021-2310-6
      [3]
      M. Siemon, M. Schiffer, and G. Walther, Integrated purchasing and production planning for a non-ferrous metal production network, Omega, 98(2021), art. No. 102136. doi: 10.1016/j.omega.2019.102136
      [4]
      U.S. Sakalli and B. Birgoren, A spreadsheet-based decision support tool for blending problems in brass casting industry, Comput. Ind. Eng., 56(2009), No. 2, p. 724. doi: 10.1016/j.cie.2008.05.009
      [5]
      R. Caldentey and S. Mondschein, Policy model for pollution control in the copper industry, including a model for the sulfuric acid market, Oper. Res., 51(2003), No. 1, p. 1. doi: 10.1287/opre.51.1.1.12797
      [6]
      G.J. Hahn and H. Kuhn, Value-based performance and risk management in supply chains: A robust optimization approach, Int. J. Prod. Econ., 139(2012), No. 1, p. 135. doi: 10.1016/j.ijpe.2011.04.002
      [7]
      G. Guillén, M. Badell, and L. Puigjaner, A holistic framework for short-term supply chain management integrating production and corporate financial planning, Int. J. Prod. Econ., 106(2007), No. 1, p. 288. doi: 10.1016/j.ijpe.2006.06.008
      [8]
      R. Sousa, N. Shah, and L.G. Papageorgiou, Supply chain design and multilevel planning—An industrial case, Comput. Chem. Eng., 32(2008), No. 11, p. 2643. doi: 10.1016/j.compchemeng.2007.09.005
      [9]
      Y.S. Liu, C.H. Yang, K.K. Huang, and W.H. Gui, Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network, Knowledge Based Syst., 188(2020), art. No. 105006. doi: 10.1016/j.knosys.2019.105006
      [10]
      O.Q. Wu and H. Chen, Optimal control and equilibrium behavior of production-inventory systems, Manag. Sci., 56(2010), No. 8, p. 1362. doi: 10.1287/mnsc.1100.1186
      [11]
      Ü.S. Sakallı, Ö.F. Baykoç, and B. Birgören, Stochastic optimization for blending problem in brass casting industry, Ann. Oper. Res., 186(2011), No. 1, p. 141. doi: 10.1007/s10479-011-0851-1
      [12]
      Y. Chen, Y.G. Li, B. Sun, Y.D. Li, H.Q. Zhu, and Z.S. Chen, A chance-constrained programming approach for a zinc hydrometallurgy blending problem under uncertainty, Comput. Chem. Eng., 140(2020), art. No. 106893. doi: 10.1016/j.compchemeng.2020.106893
      [13]
      C.H. Yang, W.H. Gui, L.S. Kong, and Y.L. Wang, Modeling and optimal-setting control of blending process in a metallurgical industry, Comput. Chem. Eng., 33(2009), No. 7, p. 1289. doi: 10.1016/j.compchemeng.2009.01.005
      [14]
      B. Sun, C.H. Yang, H.Q. Zhu, Y.G. Li, and W.H. Gui, Modeling, optimization, and control of solution purification process in zinc hydrometallurgy, IEEE/CAA J. Autom. Sin., 5(2018), No. 2, p. 564. doi: 10.1109/JAS.2017.7510844
      [15]
      B. Sun, C.H. Yang, Y.L. Wang, W.H. Gui, I. Craig, and L. Olivier, A comprehensive hybrid first principles/machine learning modeling framework for complex industrial processes, J. Process. Control, 86(2020), p. 30. doi: 10.1016/j.jprocont.2019.11.012
      [16]
      O.B. Riquelme, Modelling and Computer Control of a Flotation Cell [Dissertation], University of Utah, Salt Lake City, 1982.
      [17]
      A.J. Niemi, Role of kinetics in modelling and control of flotation plants, Powder Technol., 82(1995), No. 1, p. 69. doi: 10.1016/0032-5910(94)02893-S
      [18]
      A. Casali, G. Gonzalez, H. Agusto, and G. Vallebuona, Dynamic simulator of a rougher flotation circuit for a copper sulphide ore, Miner. Eng., 15(2002), No. 4, p. 253. doi: 10.1016/S0892-6875(02)00016-X
      [19]
      S.J. Neethling, H.T. Lee, and J.J. Cilliers, Simple relationships for predicting the recovery of liquid from flowing foams and froths, Miner. Eng., 16(2003), No. 11, p. 1123. doi: 10.1016/j.mineng.2003.06.014
      [20]
      O.N. Savassi, A compartment model for the mass transfer inside a conventional flotation cell, Int. J. Miner. Process., 77(2005), No. 2, p. 65. doi: 10.1016/j.minpro.2005.02.003
      [21]
      S.A.C. Stadler, J.J. Eksteen, and C. Aldrich, Physical modelling of slag foaming in two-phase and three-phase systems in the churn-flow regime, Miner. Eng., 19(2006), No. 3, p. 237. doi: 10.1016/j.mineng.2005.05.018
      [22]
      K. Hadler, M. Greyling, N. Plint, and J.J. Cilliers, The effect of froth depth on air recovery and flotation performance, Miner. Eng., 36-38(2012), p. 248. doi: 10.1016/j.mineng.2012.04.003
      [23]
      I. Jovanović and I. Miljanović, Modelling of flotation processes by classical mathematical methods–A review, Arch. Min. Sci., 60(2015), No. 4, p. 905. doi: 10.1515/amsc-2015-0059
      [24]
      A. Norori-McCormac, P.R. Brito-Parada, K. Hadler, K. Cole, and J.J. Cilliers, The effect of particle size distribution on froth stability in flotation, Sep. Purif. Technol., 184(2017), p. 240. doi: 10.1016/j.seppur.2017.04.022
      [25]
      B. Shean, K. Hadler, S. Neethling, and J.J. Cilliers, A dynamic model for level prediction in aerated tanks, Miner. Eng., 125(2018), p. 140. doi: 10.1016/j.mineng.2018.05.030
      [26]
      S.J. Neethling and P.R. Brito-Parada, Predicting flotation behaviour—The interaction between froth stability and performance, Miner. Eng., 120(2018), p. 60. doi: 10.1016/j.mineng.2018.02.002
      [27]
      J. Yianatos, P. Vallejos, R. Grau, and A. Yañez, New approach for flotation process modelling and simulation, Miner. Eng., 156(2020), art. No. 106482. doi: 10.1016/j.mineng.2020.106482
      [28]
      P. Quintanilla, S.J. Neethling, and P.R. Brito-Parada, Modelling for froth flotation control: A review, Miner. Eng., 162(2021), art. No. 106718. doi: 10.1016/j.mineng.2020.106718
      [29]
      D.J. Oosthuizen, J.D. le Roux, and I.K. Craig, A dynamic flotation model to infer process characteristics from online measurements, Miner. Eng., 167(2021), art. No. 106878. doi: 10.1016/j.mineng.2021.106878
      [30]
      J.A. Herbst and D.W. Fuerstenau, Scale-up procedure for continuous grinding mill design using population balance models, Int. J. Miner. Process., 7(1980), No. 1, p. 1. doi: 10.1016/0301-7516(80)90034-4
      [31]
      R.K. Rajamani and J.A. Herbst, Optimal control of a ball mill grinding circuit—II. Feedback and optimal control, Chem. Eng. Sci., 46(1991), No. 3, p. 871. doi: 10.1016/0009-2509(91)80194-4
      [32]
      A. Casali, G. Gonzalez, F. Torres, G. Vallebuona, L. Castelli, and P. Gimenez, Particle size distribution soft-sensor for a grinding circuit, Powder Technol., 99(1998), No. 1, p. 15. doi: 10.1016/S0032-5910(98)00084-9
      [33]
      Y. Liu and S. Spencer, Dynamic simulation of grinding circuits, Miner. Eng., 17(2004), No. 11-12, p. 1189. doi: 10.1016/j.mineng.2004.05.018
      [34]
      P. Zhou, T.Y. Chai, and H. Wang, Intelligent optimal-setting control for grinding circuits of mineral processing process, IEEE Trans. Autom. Sci. Eng., 6(2009), No. 4, p. 730. doi: 10.1109/TASE.2008.2011562
      [35]
      J. Yang, S.H. Li, X.S. Chen, and Q. Li, Disturbance rejection of ball mill grinding circuits using DOB and MPC, Powder Technol., 198(2010), No. 2, p. 219. doi: 10.1016/j.powtec.2009.11.010
      [36]
      J. Tang, L.J. Zhao, J.W. Zhou, H. Yue, and T.Y. Chai, Experimental analysis of wet mill load based on vibration signals of laboratory-scale ball mill shell, Miner. Eng., 23(2010), No. 9, p. 720. doi: 10.1016/j.mineng.2010.05.001
      [37]
      X.L. Wang, W.H. Gui, C.H. Yang, and Y.L. Wang, Wet grindability of an industrial ore and its breakage parameters estimation using population balances, Int. J. Miner. Process., 98(2011), No. 1-2, p. 113. doi: 10.1016/j.minpro.2010.11.008
      [38]
      P.W. Cleary and R.D. Morrison, Understanding fine ore breakage in a laboratory scale ball mill using DEM, Miner. Eng., 24(2011), No. 3-4, p. 352. doi: 10.1016/j.mineng.2010.12.013
      [39]
      A. Ebadnejad, G.R. Karimi, and H. Dehghani, Application of response surface methodology for modeling of ball mills in copper sulphide ore grinding, Powder Technol., 245(2013), p. 292. doi: 10.1016/j.powtec.2013.04.021
      [40]
      J.D. le Roux, I.K. Craig, D.G. Hulbert, and A.L. Hinde, Analysis and validation of a run-of-mine ore grinding mill circuit model for process control, Miner. Eng., 43-44(2013), p. 121. doi: 10.1016/j.mineng.2012.10.009
      [41]
      S.W. Lu, P. Zhou, T.Y. Chai, and W. Dai, Modeling and simulation of whole ball mill grinding plant for integrated control, IEEE Trans. Autom. Sci. Eng., 11(2014), No. 4, p. 1004. doi: 10.1109/TASE.2013.2296309
      [42]
      X.L. Wang, Y.L. Wang, C.H. Yang, D.G. Xu, and W.H. Gui, Hybrid modeling of an industrial grinding-classification process, Powder Technol., 279(2015), p. 75. doi: 10.1016/j.powtec.2015.03.031
      [43]
      G. Danha, D. Hildebrandt, D. Glasser, and C. Bhondayi, Application of basic process modeling in investigating the breakage behavior of UG2 ore in wet milling, Powder Technol., 279(2015), p. 42. doi: 10.1016/j.powtec.2015.03.034
      [44]
      J. Kwon, J. Jeong, and H. Cho, Simulation and optimization of a two-stage ball mill grinding circuit of molybdenum ore, Adv. Powder Technol., 27(2016), No. 4, p. 1073. doi: 10.1016/j.apt.2016.03.016
      [45]
      S.W. Lu, Acceleration of kinetic Monte Carlo simulation of particle breakage process during grinding with controlled accuracy, Powder Technol., 301(2016), p. 186. doi: 10.1016/j.powtec.2016.05.059
      [46]
      J.D. le Roux, A. Steinboeck, A. Kugi, and I.K. Craig, Steady-state and dynamic simulation of a grinding mill using grind curves, Miner. Eng., 152(2020), art. No. 106208. doi: 10.1016/j.mineng.2020.106208
      [47]
      S.W. Lu and T.Y. Chai, Mesoscale particle size predictive model for operational optimal control of bauxite ore grinding process, IEEE Trans. Ind. Inf., 16(2020), No. 12, p. 7714. doi: 10.1109/TII.2020.2967067
      [48]
      T. Komulainen, P. Pekkala, A. Rantala, and S.L. Jämsä-Jounela, Dynamic modelling of an industrial copper solvent extraction process, Hydrometallurgy, 81(2006), No. 1, p. 52. doi: 10.1016/j.hydromet.2005.11.001
      [49]
      B. Verbaan and F.K. Crundwell, An electrochemical model for the leaching of a sphalerite concentrate, Hydrometallurgy, 16(1986), No. 3, p. 345. doi: 10.1016/0304-386X(86)90009-5
      [50]
      F.K. Crundwell, Progress in the mathematical modelling of leaching reactors, Hydrometallurgy, 39(1995), No. 1-3, p. 321. doi: 10.1016/0304-386X(95)00039-J
      [51]
      F.K. Crundwell and S.A. Godorr, A mathematical model of the leaching of gold in cyanide solutions, Hydrometallurgy, 44(1997), No. 1-2, p. 147. doi: 10.1016/S0304-386X(96)00039-4
      [52]
      F.K. Crundwell, N.d. Preez, and J.M. Lloyd, Dynamics of particle-size distributions in continuous leaching reactors and autoclaves, Hydrometallurgy, 133(2013), p. 44. doi: 10.1016/j.hydromet.2012.11.016
      [53]
      F.K. Crundwell, The dissolution and leaching of minerals: Mechanisms, myths and misunderstandings, Hydrometallurgy, 139(2013), p. 132. doi: 10.1016/j.hydromet.2013.08.003
      [54]
      M. Lampinen, A. Laari, and I. Turunen, Kinetic model for direct leaching of zinc sulfide concentrates at high slurry and solute concentration, Hydrometallurgy, 153(2015), p. 160. doi: 10.1016/j.hydromet.2015.02.012
      [55]
      F.E.B. Coelho, J.C. Balarini, E.M.R. Araújo, T.L.S. Miranda, A.E.C. Peres, A.H. Martins, and A. Salum, Roasted zinc concentrate leaching: Population balance modeling and validation, Hydrometallurgy, 175(2018), p. 208. doi: 10.1016/j.hydromet.2017.11.013
      [56]
      B. Zhang, C.H. Yang, H.Q. Zhu, Y.G. Li, and W.H. Gui, Kinetic modeling and parameter estimation for competing reactions in copper removal process from zinc sulfate solution, Ind. Eng. Chem. Res., 52(2013), No. 48, p. 17074. doi: 10.1021/ie401619h
      [57]
      B. Sun, W.H. Gui, T.B. Wu, Y.L. Wang, and C.H. Yang, An integrated prediction model of cobalt ion concentration based on oxidation-reduction potential, Hydrometallurgy, 140(2013), p. 102. doi: 10.1016/j.hydromet.2013.09.015
      [58]
      M. Mokmeli, D. Dreisinger, and B. Wassink, Modeling of selenium and tellurium removal from copper electrowinning solution, Hydrometallurgy, 153(2015), p. 12. doi: 10.1016/j.hydromet.2015.01.007
      [59]
      M. Mahon, L. Wasik, and A. Alfantazi, Development and implementation of a zinc electrowinning process simulation, J. Electrochem. Soc., 159(2012), No. 8, p. D486. doi: 10.1149/2.084208jes
      [60]
      S.J. Deng, C.H. Yang, Y.G. Li, H.Q. Zhu, and T.B. Wu, Spatiotemporal distribution model for zinc electrowinning process and its parameter estimation, J. Cent. South Univ., 24(2017), No. 9, p. 1968. doi: 10.1007/s11771-017-3605-7
      [61]
      J.H. Qiao and T.Y. Chai, Soft measurement model and its application in raw meal calcination process, J. Process Control, 22(2012), No. 1, p. 344. doi: 10.1016/j.jprocont.2011.08.005
      [62]
      J.T. McCoy and L. Auret, Machine learning applications in minerals processing: A review, Miner. Eng., 132(2019), p. 95. doi: 10.1016/j.mineng.2018.12.004
      [63]
      K. Mitra and M. Ghivari, Modeling of an industrial wet grinding operation using data-driven techniques, Comput. Chem. Eng., 30(2006), No. 3, p. 508. doi: 10.1016/j.compchemeng.2005.10.007
      [64]
      J. Tang, T.Y. Chai, W. Yu, and L.J. Zhao, Feature extraction and selection based on vibration spectrum with application to estimating the load parameters of ball mill in grinding process, Control Eng. Pract., 20(2012), No. 10, p. 991. doi: 10.1016/j.conengprac.2012.03.020
      [65]
      J. Tang, T.Y. Chai, W. Yu, and L.J. Zhao, Modeling load parameters of ball mill in grinding process based on selective ensemble multisensor information, IEEE Trans. Autom. Sci. Eng., 10(2013), No. 3, p. 726. doi: 10.1109/TASE.2012.2225142
      [66]
      J.L. Ding, T.Y. Chai, W.J. Cheng, and X.P. Zheng, Data-based multiple-model prediction of the production rate for hematite ore beneficiation process, Control Eng. Pract., 45(2015), p. 219. doi: 10.1016/j.conengprac.2015.08.015
      [67]
      Z. Lv, Y. Liu, J. Zhao, and W. Wang, Soft computing for overflow particle size in grinding process based on hybrid case based reasoning, Appl. Soft Comput., 27(2015), p. 533. doi: 10.1016/j.asoc.2014.09.035
      [68]
      R.K. Inapakurthi, S.S. Miriyala, and K. Mitra, Recurrent neural networks based modelling of industrial grinding operation, Chem. Eng. Sci., 219(2020), art. No. 115585. doi: 10.1016/j.ces.2020.115585
      [69]
      S. Avalos, W. Kracht, and J.M. Ortiz, Machine learning and deep learning methods in mining operations: A data-driven SAG mill energy consumption prediction application, Min. Metall. Explor., 37(2020), p. 1197. doi: 10.1007/s42461-020-00238-1
      [70]
      S.S. Miriyala and K. Mitra, Deep learning based system identification of industrial integrated grinding circuits, Powder Technol., 360(2020), p. 921. doi: 10.1016/j.powtec.2019.10.065
      [71]
      J. Zhang, Z.H. Tang, Y.F. Xie, M.X. Ai, G.Y. Zhang, and W.H. Gui, Data-driven adaptive modeling method for industrial processes and its application in flotation reagent control, ISA Trans., 108(2021), p. 305. doi: 10.1016/j.isatra.2020.08.024
      [72]
      E.J.Y. Koh, E. Amini, G.J. McLachlan, and N. Beaton, Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations, Miner. Eng., 170(2021), art. No. 107026. doi: 10.1016/j.mineng.2021.107026
      [73]
      J.Y. Zhou, X.L. Wang, C.H. Yang, and W. Xiong, A novel soft sensor modeling approach based on difference-LSTM for complex industrial process, IEEE Trans. Ind. Inf., 18(2022), No. 5, p. 2955. doi: 10.1109/TII.2021.3110507
      [74]
      I. Jovanović, I. Miljanović, and T. Jovanović, Soft computing-based modeling of flotation processes—A review, Miner. Eng., 84(2015), p. 34. doi: 10.1016/j.mineng.2015.09.020
      [75]
      C. Marais and C. Aldrich, Estimation of platinum flotation grades from froth image data, Miner. Eng., 24(2011), No. 5, p. 433. doi: 10.1016/j.mineng.2010.12.006
      [76]
      L. Auret and C. Aldrich, Interpretation of nonlinear relationships between process variables by use of random forests, Miner. Eng., 35(2012), p. 27. doi: 10.1016/j.mineng.2012.05.008
      [77]
      D.G. Xu, Y.W. Chen, X. Chen, Y.F. Xie, C.H. Yang and W.H. Gui, Multi-model soft measurement method of the froth layer thickness based on visual features, Chemom. Intell. Lab. Syst., 154(2016), p. 112. doi: 10.1016/j.chemolab.2016.03.029
      [78]
      Y.H. Fu and C. Aldrich, Froth image analysis by use of transfer learning and convolutional neural networks, Miner. Eng., 115(2018), p. 68. doi: 10.1016/j.mineng.2017.10.005
      [79]
      L. Zhao, T. Peng, Y.F. Xie, W.H. Gui, and Y.H. Zhao, Froth stereo visual feature extraction for the industrial flotation process, Ind. Eng. Chem. Res., 58(2019), No. 31, p. 14510. doi: 10.1021/acs.iecr.9b00426
      [80]
      Y.F. Fu, B. Yang, Y.Q. Ma, Q.Y. Sun, J. Yao, W.B. Fu, and W.Z. Yin, Effect of particle size on magnesite flotation based on kinetic studies and machine learning simulation, Powder Technol., 376(2020), p. 486. doi: 10.1016/j.powtec.2020.08.054
      [81]
      H. Zhang, Z.H. Tang, Y.F. Xie, J. Luo, Q. Chen, and W.H. Gui, Grade prediction of zinc tailings using an encoder-decoder model in froth flotation, Miner. Eng., 172(2021), art. No. 107173. doi: 10.1016/j.mineng.2021.107173
      [82]
      H. Zhang, Z.H. Tang, Y.F. Xie, X.L. Gao, Q. Chen, and W.H. Gui, Long short-term memory-based grade monitoring in froth flotation using a froth video sequence, Miner. Eng., 160(2021), art. No. 106677. doi: 10.1016/j.mineng.2020.106677
      [83]
      X. Yang, Y. Zhang, Y.A.W. Shardt, X.L. Li, J.R. Cui, and C.N. Tong, A KPI-based soft sensor development approach incorporating infrequent, variable time delayed measurements, IEEE Trans. Control Syst. Technol., 28(2020), No. 6, p. 2523. doi: 10.1109/TCST.2019.2929478
      [84]
      S. Yang, P. Navarathna, S. Ghosh, and B.W. Bequette, Hybrid modeling in the era of smart manufacturing, Comput. Chem. Eng., 140(2020), art. No. 106874. doi: 10.1016/j.compchemeng.2020.106874
      [85]
      R.D. Jia, Z.Z. Mao, Y.Q. Chang, and L.P. Zhao, Soft-sensor for copper extraction process in cobalt hydrometallurgy based on adaptive hybrid model, Chem. Eng. Res. Des., 89(2011), No. 6, p. 722. doi: 10.1016/j.cherd.2010.09.015
      [86]
      S.N. Zhang, F.L. Wang, D.K. He, and F. Chu, Soft sensor for cobalt oxalate synthesis process in cobalt hydrometallurgy based on hybrid model, Neural Comput. Appl., 23(2013), No. 5, p. 1465. doi: 10.1007/s00521-012-1096-x
      [87]
      Y.F. Xie, S.W. Xie, X.F. Chen, W.H. Gui, C.H. Yang, and L. Caccetta, An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy, Hydrometallurgy, 151(2015), p. 62. doi: 10.1016/j.hydromet.2014.11.004
      [88]
      J. Yang, T.Y. Chai, C.M. Luo, and W. Yu, Intelligent demand forecasting of smelting process using data-driven and mechanism model, IEEE Trans. Ind. Electron., 66(2019), No. 12, p. 9745. doi: 10.1109/TIE.2018.2883262
      [89]
      T. Xu, G. Song, Y. Yang, P.X. Ge, and L.X. Tang, Visualization and simulation of steel metallurgy processes, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1387. doi: 10.1007/s12613-021-2283-5
      [90]
      B. Sun, W. Yang, M.F. He, and X.L. Wang, An integrated multi-mode model of froth flotation cell based on fusion of flotation kinetics and froth image features, Miner. Eng., 172(2021), art. No. 107169. doi: 10.1016/j.mineng.2021.107169
      [91]
      S.L. Jämsä-Jounela, R. Poikonen, N. Vatanski, and A. Rantala, Evaluation of control performance: Methods, monitoring tool and applications in a flotation plant, Miner. Eng., 16(2003), No. 11, p. 1069. doi: 10.1016/j.mineng.2003.06.005
      [92]
      X. Yang, J.J. Gao, L.L. Li, H. Luo, S.X. Ding, and K.X. Peng, Data-driven design of fault-tolerant control systems based on recursive stable image representation, Automatica, 122(2020), art. No. 109246. doi: 10.1016/j.automatica.2020.109246
      [93]
      Y. Liu, F.L. Wang, Y.Q. Chang, and R.C. Ma, Operating optimality assessment and nonoptimal cause identification for non-Gaussian multimode processes with transitions, Chem. Eng. Sci., 137(2015), p. 106. doi: 10.1016/j.ces.2015.06.016
      [94]
      B. Zhang, C.H. Yang, H.Q. Zhu, Y.G. Li, and W.H. Gui, Evaluation strategy for the control of the copper removal process based on oxidation-reduction potential, Chem. Eng. J., 284(2016), p. 294. doi: 10.1016/j.cej.2015.07.094
      [95]
      X.Y. Zou, F.L. Wang, and Y.Q. Chang, Assessment of operating performance using cross-domain feature transfer learning, Control Eng. Pract., 89(2019), p. 143. doi: 10.1016/j.conengprac.2019.05.007
      [96]
      Z.H. Zeng, W.H. Gui, X.F. Chen, Y.F. Xie, and R.C. Wu, A mechanism knowledge-driven method for identifying the pseudo dissolution hysteresis coefficient in the industrial aluminium electrolysis process, Control Eng. Pract., 102(2020), art. No. 104533. doi: 10.1016/j.conengprac.2020.104533
      [97]
      H. Zhang, Z.H. Tang, Y.F. Xie, Q. Chen, X.L. Gao, and W.H. Gui, Feature reconstruction-regression network: A light-weight deep neural network for performance monitoring in the froth flotation, IEEE Trans. Ind. Inf., 17(2021), No. 12, p. 8406. doi: 10.1109/TII.2020.3046278
      [98]
      L.S. Zhong, Y.Q. Chang, F.L. Wang, and S.H. Gao, Distributed operating performance assessment of the plant-wide process based on data-driven hybrid characteristics decomposition, Ind. Eng. Chem. Res., 59(2020), No. 35, p. 15682. doi: 10.1021/acs.iecr.0c02565
      [99]
      H.P. Liang, C.H. Yang, K.K. Huang, Y.G. Li, and W.H. Gui, A hybrid first principles and data-driven process monitoring method for zinc smelting roasting process, IEEE Trans. Instrum. Meas., 70(2021), p. 1. doi: 10.1109/TIM.2021.3126390
      [100]
      C. Aldrich, D.W. Moolman, F.S. Gouws, and G.P.J. Schmitz, Machine learning strategies for control of flotation plants, Control Eng. Pract., 5(1997), No. 2, p. 263. doi: 10.1016/S0967-0661(97)00235-9
      [101]
      C. Aldrich, C. Marais, B.J. Shean, and J.J. Cilliers, Online monitoring and control of froth flotation systems with machine vision: A review, Int. J. Miner. Process., 96(2010), No. 1-4, p. 1. doi: 10.1016/j.minpro.2010.04.005
      [102]
      H. Hyötyniemi and R. Ylinen, Modeling of visual flotation froth data, Control Eng. Pract., 8(2000), No. 3, p. 313. doi: 10.1016/S0967-0661(99)00187-2
      [103]
      J.J. Liu, J.F. MacGregor, C. Duchesne, and G. Bartolacci, Flotation froth monitoring using multiresolutional multivariate image analysis, Miner. Eng., 18(2005), No. 1, p. 65. doi: 10.1016/j.mineng.2004.05.010
      [104]
      M.F. He, C.H. Yang, X.L. Wang, W.H. Gui, and L.J. Wei, Nonparametric density estimation of froth colour texture distribution for monitoring sulphur flotation process, Miner. Eng., 53(2013), p. 203. doi: 10.1016/j.mineng.2013.08.011
      [105]
      L. Zhao, T. Peng, Y.F. Xie, C.H. Yang, and W.H. Gui, Recognition of flooding and sinking conditions in flotation process using soft measurement of froth surface level and QTA, Chemom. Intell. Lab. Syst., 169(2017), p. 45. doi: 10.1016/j.chemolab.2017.07.005
      [106]
      X.L. Wang, C. Song, C.H. Yang, and Y.F. Xie, Process working condition recognition based on the fusion of morphological and pixel set features of froth for froth flotation, Miner. Eng., 128(2018), p. 17. doi: 10.1016/j.mineng.2018.08.017
      [107]
      M. Lu, D.H. Xie, W.H. Gui, L.H. Wu, C.Y. Chen, and C.H. Yang, A cascaded recognition method for copper rougher flotation working conditions, Chem. Eng. Sci., 175(2018), p. 220. doi: 10.1016/j.ces.2017.09.048
      [108]
      Y. Fu and C. Aldrich, Flotation froth image recognition with convolutional neural networks, Miner. Eng., 132(2019), p. 183. doi: 10.1016/j.mineng.2018.12.011
      [109]
      X.L. Gao, Z.H. Tang, Y.F. Xie, H. Zhang, and W.H. Gui, A layered working condition perception integrating handcrafted with deep features for froth flotation, Miner. Eng., 170(2021), art. No. 107059. doi: 10.1016/j.mineng.2021.107059
      [110]
      S.L. Jämsä-Jounela, M. Vermasvuori, P. Endén, and S. Haavisto, A process monitoring system based on the Kohonen self-organizing maps, Control Eng. Pract., 11(2003), No. 1, p. 83. doi: 10.1016/S0967-0661(02)00141-7
      [111]
      M.J.J. van Vuuren, C. Aldrich, and L. Auret, Detecting changes in the operational states of hydrocyclones, Miner. Eng., 24(2011), No. 14, p. 1532. doi: 10.1016/j.mineng.2011.08.002
      [112]
      Y.W. Zhang, T.Y. Chai, Z.M. Li, and C.Y. Yang, Modeling and monitoring of dynamic processes, IEEE Trans. Neural Networks Learn. Syst.,, 23(2012), No. 2, p. 277. doi: 10.1109/TNNLS.2011.2179669
      [113]
      G.C. Wu, Q. Liu, T.Y. Chai, and S.J. Qin, Abnormal condition diagnosis through deep learning of image sequences for fused magnesium furnaces, Acta Autom. Sin., 45(2019), No. 8, p. 1475.
      [114]
      Z.W. Wu, Y.J. Wu, T.Y. Chai, and J. Sun, Data-driven abnormal condition identification and self-healing control system for fused magnesium furnace, IEEE Trans. Ind. Electron., 62(2015), No. 3, p. 1703. doi: 10.1109/TIE.2014.2349479
      [115]
      H. Li, F.L. Wang, and H.R. Li, Abnormal condition identification and self-healing control scheme for the electro-fused magnesia smelting process, Acta Autom. Sin., 46(2020), No. 7, p. 1411.
      [116]
      K.K. Huang, Z. Tao, B. Sun, C.H. Yang, and W.H. Gui, Industrial process modeling and monitoring based on jointly specific and shared dictionary learning, IEEE Trans. Instrum. Meas., 71(2022), p. 1. doi: 10.1109/TIM.2021.3125969
      [117]
      H. Ren, Z.W. Chen, Z.H. Jiang, C.H. Yang, and W.H. Gui, An industrial multilevel knowledge graph-based local–global monitoring for plant-wide processes, IEEE Trans. Instrum. Meas., 70(2021), p. 1. doi: 10.1109/TIM.2021.3125110
      [118]
      A.J. Yan, F.H. Wu, and T.Y. Chai, Fault diagnosis expert system using neural networks for roasting process, [in] 16th Triennial World Congress, Prague, 2005, p. 115.
      [119]
      G.T. Jemwa and C. Aldrich, Kernel-based fault diagnosis on mineral processing plants, Miner. Eng., 19(2006), No. 11, p. 1149. doi: 10.1016/j.mineng.2006.05.006
      [120]
      C.H. Xu, W.H. Gui, C.H. Yang, H.Q. Zhu, Y.Q. Lin, and C. Shi, Flotation process fault detection using output PDF of bubble size distribution, Miner. Eng., 26(2012), p. 5. doi: 10.1016/j.mineng.2011.09.012
      [121]
      Z.M. Li, W.H. Gui, and J.Y. Zhu, Fault detection in flotation processes based on deep learning and support vector machine, J. Cent. South Univ., 26(2019), No. 9, p. 2504. doi: 10.1007/s11771-019-4190-8
      [122]
      B. Lindner, L. Auret, M. Bauer, and J.W.D. Groenewald, Comparative analysis of Granger causality and transfer entropy to present a decision flow for the application of oscillation diagnosis, J. Process Control, 79(2019), p. 72. doi: 10.1016/j.jprocont.2019.04.005
      [123]
      M. Järvensivu, K. Saari, and S.L. Jämsä-Jounela, Intelligent control system of an industrial lime kiln process, Control Eng. Pract., 9(2001), No. 6, p. 589. doi: 10.1016/S0967-0661(01)00017-X
      [124]
      T.Y. Chai, S.J. Qin, and H. Wang, Optimal operational control for complex industrial processes, Annu. Rev. Control, 38(2014), No. 1, p. 81. doi: 10.1016/j.arcontrol.2014.03.005
      [125]
      Z.W. Wu, T.F. Liu, Z.P. Jiang, T.Y. Chai, and L.N. Zhang, Nonlinear control tools for fused magnesium furnaces: Design and implementation, IEEE Trans. Ind. Electron., 65(2018), No. 9, p. 7248. doi: 10.1109/TIE.2017.2767545
      [126]
      J.L. Ding, C.E. Yang, and T.Y. Chai, Recent progress on data-based optimization for mineral processing plants, Engineering, 3(2017), No. 2, p. 183. doi: 10.1016/J.ENG.2017.02.015
      [127]
      J. Han, C.H. Yang, C.C. Lim, X.J. Zhou, and P. Shi, Stackelberg–Nash game approach for constrained robust optimization with fuzzy variables, IEEE Trans. Fuzzy Syst., 29(2021), No. 11, p. 3519. doi: 10.1109/TFUZZ.2020.3025697
      [128]
      G. Asbjörnsson, L.M. Tavares, A. Mainza, and M. Yahyaei, Different perspectives of dynamics in comminution processes, Miner. Eng., 176(2022), art. No. 107326. doi: 10.1016/j.mineng.2021.107326
      [129]
      A. Niemi and U. Paakkinen, Simulation and control of flotation circuits, Automatica, 5(1969), No. 5, p. 551. doi: 10.1016/0005-1098(69)90023-5
      [130]
      D. Hodouin, Methods for automatic control, observation, and optimization in mineral processing plants, J. Process Control, 21(2011), No. 2, p. 211. doi: 10.1016/j.jprocont.2010.10.016
      [131]
      L.G. Bergh and J.B. Yianatos, The long way toward multivariate predictive control of flotation processes, J. Process Control, 21(2011), No. 2, p. 226. doi: 10.1016/j.jprocont.2010.11.001
      [132]
      B.J. Shean and J.J. Cilliers, A review of froth flotation control, Int. J. Miner. Process., 100(2011), No. 3-4, p. 57. doi: 10.1016/j.minpro.2011.05.002
      [133]
      W.H. Gui, C.H. Yang, X.F. Chen, and Y.L. Wang, Modeling and optimization problems and challenges arising in nonferrous metallurgical processes, Acta Autom. Sin., 39(2013), No. 3, p. 197.
      [134]
      P. Zhou, S.W. Lu, M. Yuan, and T.Y. Chai, Survey on higher-level advanced control for grinding circuits operation, Powder Technol., 288(2016), p. 324. doi: 10.1016/j.powtec.2015.11.010
      [135]
      D. Ali and S. Frimpong, Artificial intelligence, machine learning and process automation: Existing knowledge frontier and way forward for mining sector, Artif. Intell. Rev., 53(2020), No. 8, p. 6025. doi: 10.1007/s10462-020-09841-6
      [136]
      R.Y. Yin, Review on the study of metallurgical process engineering, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1253. doi: 10.1007/s12613-020-2220-z
      [137]
      E.N. Pistikopoulos, A. Barbosa-Povoa, J.H. Lee, R. Misener, A. Mitsos, G.V. Reklaitis, V. Venkatasubramanian, F.Q. You, and R. Gani, Process systems engineering—The generation next? Comput. Chem. Eng., 147(2021), art. No. 107252.
      [138]
      Z.J. Xu, Z. Zheng, and X.Q. Gao, Operation optimization of the steel manufacturing process: A brief review, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1274. doi: 10.1007/s12613-021-2273-7
      [139]
      E. Nolasco, V.S. Vassiliadis, W. Kähm, S.D. Adloor, R.A. Ismaili, R. Conejeros, T. Espaas, N. Gangadharan, V. Mappas, F. Scott, and Q.Y. Zhang, Optimal control in chemical engineering: Past, present and future, Comput. Chem. Eng., 155(2021), art. No. 107528. doi: 10.1016/j.compchemeng.2021.107528
      [140]
      L. Lin and J.Q. Zeng, Consideration of green intelligent steel processes and narrow window stability control technology on steel quality, Int. J. Miner. Metall. Mater., 28(2021), No. 8, p. 1264. doi: 10.1007/s12613-020-2246-2
      [141]
      T. Yang, X.L. Yi, S.W. Lu, K.H. Johansson, and T.Y. Chai, Intelligent manufacturing for the process industry driven by industrial artificial intelligence, Engineering, 7(2021), No. 9, p. 1224. doi: 10.1016/j.eng.2021.04.023
      [142]
      A. Desbiens, A. Pomerleau, and K. Najim, Adaptive predictive control of a grinding circuit, Int. J. Miner. Process., 41(1994), No. 1-2, p. 17. doi: 10.1016/0301-7516(94)90003-5
      [143]
      V.R. Radhakrishnan, Model based supervisory control of a ball mill grinding circuit, J. Process Control, 9(1999), No. 3, p. 195. doi: 10.1016/S0959-1524(98)00048-1
      [144]
      P. Kämpjärvi and S.L. Jämsä-Jounela, Level control strategies for flotation cells, Miner. Eng., 16(2003), No. 11, p. 1061. doi: 10.1016/j.mineng.2003.06.004
      [145]
      X.S. Chen, J. Yang, S.H. Li, and Q. Li, Disturbance observer based multi-variable control of ball mill grinding circuits, J. Process Control, 19(2009), No. 7, p. 1205. doi: 10.1016/j.jprocont.2009.02.004
      [146]
      J.Y. Zhu, W.H. Gui, J.P. Liu, H.L. Xu, and C.H. Yang, Combined fuzzy based feedforward and bubble size distribution based feedback control for reagent dosage in copper roughing process, J. Process Control, 39(2016), p. 50. doi: 10.1016/j.jprocont.2015.12.003
      [147]
      B. Shean, K. Hadler, and J.J. Cilliers, A flotation control system to optimise performance using peak air recovery, Chem. Eng. Res. Des., 117(2017), p. 57. doi: 10.1016/j.cherd.2016.10.021
      [148]
      H. Khodadadi and H. Ghadiri, Fuzzy logic self-tuning PID controller design for ball mill grinding circuits using an improved disturbance observer, Min. Metall. Explor., 36(2019), No. 6, p. 1075. doi: 10.1007/s42461-019-0098-y
      [149]
      M. Ramasamy, S.S. Narayanan, and C.D.P. Rao, Control of ball mill grinding circuit using model predictive control scheme, J. Process Control, 15(2005), No. 3, p. 273. doi: 10.1016/j.jprocont.2004.06.006
      [150]
      X.S. Chen, S.H. Li, J.Y. Zhai, and Q. Li, Expert system based adaptive dynamic matrix control for ball mill grinding circuit, Expert Syst. Appl., 36(2009), No. 1, p. 716. doi: 10.1016/j.eswa.2007.10.008
      [151]
      A. Remes, J. Aaltonen, and H. Koivo, Grinding circuit modeling and simulation of particle size control at Siilinjärvi concentrator, Int. J. Miner. Process., 96(2010), No. 1-4, p. 70. doi: 10.1016/j.minpro.2010.05.001
      [152]
      J.D. le Roux, R. Padhi, and I.K. Craig, Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm, J. Process Control, 24(2014), No. 12, p. 29. doi: 10.1016/j.jprocont.2014.10.007
      [153]
      T.Y. Chai, L.Y. Zhang, C.Y. Su, and H. Wang, An intelligent mill load switching control of the pulverizing system for an alumina sintering process, IEEE Trans. Control Syst. Technol., 20(2012), No. 3, p. 677. doi: 10.1109/TCST.2011.2140319
      [154]
      B. Zhang, C.H. Yang, H.Q. Zhu, P. Shi, and W.H. Gui, Controllable-domain-based fuzzy rule extraction for copper removal process control, IEEE Trans. Fuzzy Syst., 26(2018), No. 3, p. 1744. doi: 10.1109/TFUZZ.2017.2751000
      [155]
      H. Li, F.L. Wang, H.R. Li, and Q.K. Wang, Safety control modeling method based on Bayesian network transfer learning for the thickening process of gold hydrometallurgy, Knowl. Based Syst., 192(2020), art. No. 105297. doi: 10.1016/j.knosys.2019.105297
      [156]
      Z.X. Feng, Y.G. Li, B. Sun, C.H. Yang, H.Q. Zhu, and Z.S. Chen, A trend-based event-triggering fuzzy controller for the stabilizing control of a large-scale zinc roaster, J. Process. Control, 97(2021), p. 59. doi: 10.1016/j.jprocont.2020.11.009
      [157]
      J. Valenzuela, K. Najim, R. del Villar, and M. Bourassa, Learning control of an autogenous grinding circuit, Int. J. Miner. Process., 40(1993), No. 1-2, p. 45. doi: 10.1016/0301-7516(93)90039-D
      [158]
      A.V.E. Conradie and C. Aldrich, Neurocontrol of a ball mill grinding circuit using evolutionary reinforcement learning, Miner. Eng., 14(2001), No. 10, p. 1277. doi: 10.1016/S0892-6875(01)00144-3
      [159]
      B. Sun, M.F. He, Y.L. Wang, W.H. Gui, C.H. Yang, and Q.M. Zhu, A data-driven optimal control approach for solution purification process, J. Process Control, 68(2018), p. 171. doi: 10.1016/j.jprocont.2018.06.005
      [160]
      Y. Jiang, J.L. Fan, T.Y. Chai, J.N. Li, and F.L. Lewis, Data-driven flotation industrial process operational optimal control based on reinforcement learning, IEEE Trans. Ind. Inf., 14(2018), No. 5, p. 1974. doi: 10.1109/TII.2017.2761852
      [161]
      X.L. Lu, B. Kiumarsi, T.Y. Chai, Y. Jiang, and F.L. Lewis, Operational control of mineral grinding processes using adaptive dynamic programming and reference governor, IEEE Trans. Ind. Inf., 15(2019), No. 4, p. 2210. doi: 10.1109/TII.2018.2868473
      [162]
      D.W. Moolman, C. Aldrich, J.S.J. van Deventer, and W.W. Stange, Digital image processing as a tool for on-line monitoring of froth in flotation plants, Miner. Eng., 7(1994), No. 9, p. 1149. doi: 10.1016/0892-6875(94)00058-1
      [163]
      D.W. Moolman, J.J. Eksteen, C. Aldrich, and J.S.J. van Deventer, The significance of flotation froth appearance for machine vision control, Int. J. Miner. Process., 48(1996), No. 3-4, p. 135. doi: 10.1016/S0301-7516(96)00022-1
      [164]
      J.J. Liu and J.F. MacGregor, Froth-based modeling and control of flotation processes, Miner. Eng., 21(2008), No. 9, p. 642. doi: 10.1016/j.mineng.2007.12.011
      [165]
      F. Núñez and A. Cipriano, Visual information model based predictor for froth speed control in flotation process, Miner. Eng., 22(2009), No. 4, p. 366. doi: 10.1016/j.mineng.2008.10.005
      [166]
      W.H. Gui, C.H. Yang, D.G. Xu, M. Lu, and Y.F. Xie, Machine-vision-based online measuring and controlling technologies for mineral flotation—A review, Acta Autom. Sin., 39(2013), No. 11, art. No. 1879. doi: 10.3724/SP.J.1004.2013.01879
      [167]
      J.Y. Zhu, W.H. Gui, C.H. Yang, H.L. Xu, and X.L. Wang, Probability density function of bubble size based reagent dosage predictive control for copper roughing flotation, Control Eng. Pract., 29(2014), p. 1. doi: 10.1016/j.conengprac.2014.02.021
      [168]
      M. Maldonado, A. Desbiens, É. Poulin, R.d. Villar, and A. Riquelme, Automatic control of bubble size in a laboratory flotation column, Int. J. Miner. Process., 141(2015), p. 27. doi: 10.1016/j.minpro.2015.06.003
      [169]
      A. Jahedsaravani, M.H. Marhaban, M. Massinaei, M.I. Saripan, and S.B.M. Noor, Froth-based modeling and control of a batch flotation process, Int. J. Miner. Process., 146(2016), p. 90. doi: 10.1016/j.minpro.2015.12.002
      [170]
      Y.F. Xie, J. Wu, D.G. Xu, C.H. Yang, and W.H. Gui, Reagent addition control for stibium rougher flotation based on sensitive froth image features, IEEE Trans. Ind. Electron., 64(2017), No. 5, p. 4199. doi: 10.1109/TIE.2016.2613499
      [171]
      J. Zhang, Z.H. Tang, Y.F. Xie, Q. Chen, M.X. Ai, and W.H. Gui, Timed key-value memory network for flotation reagent control, Control Eng. Pract., 98(2020), art. No. 104360. doi: 10.1016/j.conengprac.2020.104360
      [172]
      Y.C. Lo, A.E. Oblad, and J.A. Herbst, Cost reduction in grinding plants through process optimization and control, Min. Metall. Explor., 13(1996), No. 1, p. 19. doi: 10.1007/BF03402711
      [173]
      K. Mitra and R. Gopinath, Multiobjective optimization of an industrial grinding operation using elitist nondominated sorting genetic algorithm, Chem. Eng. Sci., 59(2004), No. 2, p. 385. doi: 10.1016/j.ces.2003.09.036
      [174]
      K. Mitra, Multiobjective optimization of an industrial grinding operation under uncertainty, Chem. Eng. Sci., 64(2009), No. 23, p. 5043. doi: 10.1016/j.ces.2009.08.012
      [175]
      S. Sharma, P.D. Pantula, S.S. Miriyala, and K. Mitra, A novel data-driven sampling strategy for optimizing industrial grinding operation under uncertainty using chance constrained programming, Powder Technol., 377(2021), p. 913. doi: 10.1016/j.powtec.2020.09.024
      [176]
      M. Maldonado, D. Sbarbaro, and E. Lizama, Optimal control of a rougher flotation process based on dynamic programming, Miner. Eng., 20(2007), No. 3, p. 221. doi: 10.1016/j.mineng.2006.08.015
      [177]
      X.S. Chen, Q. Li, and S.M. Fei, Supervisory expert control for ball mill grinding circuits, Expert Syst. Appl., 34(2008), No. 3, p. 1877. doi: 10.1016/j.eswa.2007.02.013
      [178]
      T.Y. Chai, L. Zhao, J.B. Qiu, F.Z. Liu, and J.L. Fan, Integrated network-based model predictive control for setpoints compensation in industrial processes, IEEE Trans. Ind. Inf., 9(2013), No. 1, p. 417. doi: 10.1109/TII.2012.2217750
      [179]
      B. Sun, W.H. Gui, Y.L. Wang, and C.H. Yang, Intelligent optimal setting control of a cobalt removal process, J. Process Control, 24(2014), No. 5, p. 586. doi: 10.1016/j.jprocont.2014.03.002
      [180]
      B. Sun, W.H. Gui, Y.L. Wang, C.H. Yang, and M.F. He, A gradient optimization scheme for solution purification process, Control Eng. Pract., 44(2015), p. 89. doi: 10.1016/j.conengprac.2015.07.008
      [181]
      Z.K. Hu, W.H. Gui, X.Q. Peng, J.F. Yao, and W.H. Zhang, Intelligent optimization of optimal operational pattern in the process of copper converting furnace, Control Theory Appl., 22(2005), No. 2, p. 243.
      [182]
      W.H. Gui, C.H. Yang, Y.G. Li, J.J. He, and L.Z. Yin, Data-driven operational-pattern optimization for copper flash smelting process, Acta Autom. Sin., 35(2009), No. 6, p. 717. doi: 10.3724/SP.J.1004.2009.00717
      [183]
      T.B. Wu, C.H. Yang, Y.G. Li, H.Q. Zhu, and W.H. Gui, Fuzzy operational-pattern based operating parameters collaborative optimization of cobalt removal process with arsenic salt, Acta Autom. Sin., 40(2014), No. 8, p. 1690.
      [184]
      B.F. Cao, Y.F. Xie, W.H. Gui, C.H. Yang, and J.Q. Li, Coordinated optimization setting of reagent dosages in roughing-scavenging process of antimony flotation, J. Cent. South Univ., 25(2018), No. 1, p. 95. doi: 10.1007/s11771-018-3720-0
      [185]
      J.D. le Roux and I.K. Craig, Plant-wide control framework for a grinding mill circuit, Ind. Eng. Chem. Res., 58(2019), No. 26, p. 11585. doi: 10.1021/acs.iecr.8b06031
      [186]
      O.A. Bascur and A. Soudek, Grinding and flotation optimization using operational intelligence, Min. Metall. Explor., 36(2019), No. 1, p. 139. doi: 10.1007/s42461-018-0036-4
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