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doi: 10.3934/jimo.2021159
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Parameter optimal identification and dynamic behavior analysis of nonlinear model for the solution purification process of zinc hydrometallurgy

1. 

College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

2. 

College of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

3. 

College of Electrical and Information Engineering, Key Laboratory of Gansu Advanced Control for Industrial Processes, National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou, 730050, China

* Corresponding author: Aimin An, Ph.D Professor; Email: anaiminll@163.com; ORCID:0000-0003-3607-6536

Received  March 2021 Revised  June 2021 Early access September 2021

Impurity removal is a momentous part of zinc hydrometallurgy process, and the quality of products and the stability of the whole process are affected directly by its control effect. The application of dynamic model is of great significance to the prediction of key indexes and the optimization of process control. In this paper, considering the complex coupling relationship of stage II purification process, a hybrid modeling method of mechanism modeling and parameter identification modeling was proposed on the basis of not changing the actual production process of lead-zinc smeltery. Firstly, the overall nonlinear dynamic mechanism model was established, and then the deviation between the theoretical value and the actual detected outlet ion concentration was taken as the objective function to establish the parameter identification optimization model. Since the built model is nonlinear, it may pose implementation problems. On the premise of deriving the gradient vector and Hessian matrix of the objective function with respect to the parameter vector, an optimization algorithm based on the steepest descent method and Newton method is proposed. Finally, using the historical production data of a lead-zinc smeltery in China, the model parameters were accurately inversed. An intensive simulation validation and analysis of the dynamic characteristics about the whole model shows the accuracy and the potential of the model, also in the perspective of practical implementation, which provides the basis for the optimal control of system output and the guidance for the optimal control of zinc powder addition.

Citation: Qianqian Wang, Minan Tang, Aimin An, Jiawei Lu, Yingying Zhao. Parameter optimal identification and dynamic behavior analysis of nonlinear model for the solution purification process of zinc hydrometallurgy. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2021159
References:
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N. Chen, J. Q. Zhou, W. H. Gui, C. H. Yang and J. Y. Dai, Two-layer optimal control for goethite iron precipitation process, Control Theory & Applications, 37 (2020), 222–228. Google Scholar

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W. H. GuiC. H. YangX. F. Chen and Y. L. Wang, Modeling and optimization problems and challenges arising in nonferrous metallurgical processes, Acta Automatica Sinica, 39 (2013), 197-207.   Google Scholar

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J. HanC. H. YangX. J. Zhou and W. H. Gui, Dynamic multi-objective optimization arising in iron precipitation of zinc hydrometallurgy, Hydrometallurgy, 173 (2017), 134-148.  doi: 10.1016/j.hydromet.2017.08.007.  Google Scholar

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B. SunW. H. GuiY. L. WangC. H. Yang and M. F. He, A gradient optimization scheme for solution purification process, Control Engineering Practice, 44 (2015), 89-103.  doi: 10.1016/j.conengprac.2015.07.008.  Google Scholar

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B. SunW. H. GuiC. H. YangY. L. Wang and M. F. He, Online estimation of impurity ion concentration in solution purification process, IFAC-PapersOnLine, 49 (2016), 178-183.   Google Scholar

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B. SunB. ZhangC. H. Yang and W. H. Gui, Discussion on modeling and optimal control of nonferrous metallurgical purification process, Acta Automatica Sinica, 43 (2017), 880-892.   Google Scholar

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M. S. Sang, Y. Ding, M. L. Bao, Y. T. Fang and B. B. Lu, Propagation dynamics model considering the characteristics of 2019-nCoV and prevention-control measurements, System Engineering-Theory & Practice, 41 (2021), 124–133. doi: 10.12011/SETP2020-0911.  Google Scholar

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B. SunM. F. HeY. L. WangW. H. GuiC. H. Yang and Q. M. Zhu, A data-driven optimal control approach for solution purification process, Journal of Process Control, 68 (2018), 171-185.  doi: 10.1016/j.jprocont.2018.06.005.  Google Scholar

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L. Y. Wang, Modelling and Optimization Method Based on Control Parameterization in Purification Process of Zinc Hydrometallurgy, Ph.D thesis, Central South University in Changsha, 2009. Google Scholar

[13]

L. Y. WangW. H. GuiK. L. TeoR. Loxton and C. H. Yang, Time delayed optimal control problems with multiple characteristic time points: Computation and industrial applications, J. Ind. Manag. Optim., 5 (2009), 705-718.  doi: 10.3934/jimo.2009.5.705.  Google Scholar

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T. B. WuC. H. YangY. G. LiH. Q. Zhu and W. H. Gui, Fuzzy operational-pattern based operating parameters collaborative optimization of cobalt removal process with arsenic salt, Acta Automatica Sinica, 40 (2014), 1690-1698.   Google Scholar

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X. Y. WangX. J. Zhou and C. H. Yang, Chance constrained optimization for copper removal process under uncertainty in zinc hydrometallurgy, CIESC Journal, 71 (2020), 1226-1233.   Google Scholar

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S. W. Xie, Y. F. Xie and F. B. Li, Optimal setting and control for iron removal process based on adaptive neural network soft-sensort, IEEE Transactions on Systems Man & Cybernetics Systems, 99 (2018), 1–13. Google Scholar

[17]

S. W. XieY. F. Xie and H. Ying, A hybrid control strategy for real-time control of the iron removal process of the zinc hydrometallurgy plants, IEEE Transactions on Industrial Informatics, 14 (2018), 5278-5288.  doi: 10.1109/TII.2018.2815659.  Google Scholar

[18]

S. W. XieY. F. XieW. H. Gui and C. H. Yang, Weighted-coupling CSTR modeling and model predictive control with parameter adaptive correction for the goethite process, Journal of Process Control, 68 (2018), 254-267.  doi: 10.1016/j.jprocont.2018.05.006.  Google Scholar

[19]

F. B. ZhouC. G. LiH. Q. Zhu and Y. G. Li, Determination of trace ions of cobalt and copper by UV-vis spectrometry in purification process of zinc hydrometallurgy, Optik, 184 (2019), 227-233.  doi: 10.1016/j.ijleo.2019.03.056.  Google Scholar

[20]

H. Q. ZhuC. H. Yang and W. H. Gui, Soft sensing method of ionic concentration in cobalt removal purification process based on WA and SVM, Computer Engineering and Applications, 47 (2011), 212-214.   Google Scholar

[21]

Z. G. Zhang, Study on Industrial Application Hydrometallurgy of Zinc Purification System to High Impurity Raw Materials, Master thesis, Lanzhou University of Technology in Lanzhou, 2020. Google Scholar

show all references

References:
[1]

N. Chen, J. Q. Zhou, W. H. Gui, C. H. Yang and J. Y. Dai, Two-layer optimal control for goethite iron precipitation process, Control Theory & Applications, 37 (2020), 222–228. Google Scholar

[2] W. H. Gui and C. H. Yang, Intelligent Modeling, Control and Optimization of Complex Non-Ferrous Metallurgy Production Process, Science Press, Beijing, 2010.   Google Scholar
[3]

W. H. GuiC. H. YangX. F. Chen and Y. L. Wang, Modeling and optimization problems and challenges arising in nonferrous metallurgical processes, Acta Automatica Sinica, 39 (2013), 197-207.   Google Scholar

[4]

J. HanC. H. YangX. J. Zhou and W. H. Gui, Dynamic multi-objective optimization arising in iron precipitation of zinc hydrometallurgy, Hydrometallurgy, 173 (2017), 134-148.  doi: 10.1016/j.hydromet.2017.08.007.  Google Scholar

[5]

X. F. LiuB. Z. ZhouB. Y. Xiao and G. P. Cai, Inertia parameter identification of an unknown captured space target, Aircraft Engineering and Aerospace Technology, 91 (2019), 1147-1155.   Google Scholar

[6]

B. SunW. H. GuiT. B. WuY. L. Wang and C. H. Yang, An integrated prediction model of cobalt ion concentration based on oxidation-reduction potential, Hydrometallurgy, 140 (2013), 102-110.  doi: 10.1016/j.hydromet.2013.09.015.  Google Scholar

[7]

B. SunW. H. GuiY. L. WangC. H. Yang and M. F. He, A gradient optimization scheme for solution purification process, Control Engineering Practice, 44 (2015), 89-103.  doi: 10.1016/j.conengprac.2015.07.008.  Google Scholar

[8]

B. SunW. H. GuiC. H. YangY. L. Wang and M. F. He, Online estimation of impurity ion concentration in solution purification process, IFAC-PapersOnLine, 49 (2016), 178-183.   Google Scholar

[9]

B. SunB. ZhangC. H. Yang and W. H. Gui, Discussion on modeling and optimal control of nonferrous metallurgical purification process, Acta Automatica Sinica, 43 (2017), 880-892.   Google Scholar

[10]

M. S. Sang, Y. Ding, M. L. Bao, Y. T. Fang and B. B. Lu, Propagation dynamics model considering the characteristics of 2019-nCoV and prevention-control measurements, System Engineering-Theory & Practice, 41 (2021), 124–133. doi: 10.12011/SETP2020-0911.  Google Scholar

[11]

B. SunM. F. HeY. L. WangW. H. GuiC. H. Yang and Q. M. Zhu, A data-driven optimal control approach for solution purification process, Journal of Process Control, 68 (2018), 171-185.  doi: 10.1016/j.jprocont.2018.06.005.  Google Scholar

[12]

L. Y. Wang, Modelling and Optimization Method Based on Control Parameterization in Purification Process of Zinc Hydrometallurgy, Ph.D thesis, Central South University in Changsha, 2009. Google Scholar

[13]

L. Y. WangW. H. GuiK. L. TeoR. Loxton and C. H. Yang, Time delayed optimal control problems with multiple characteristic time points: Computation and industrial applications, J. Ind. Manag. Optim., 5 (2009), 705-718.  doi: 10.3934/jimo.2009.5.705.  Google Scholar

[14]

T. B. WuC. H. YangY. G. LiH. Q. Zhu and W. H. Gui, Fuzzy operational-pattern based operating parameters collaborative optimization of cobalt removal process with arsenic salt, Acta Automatica Sinica, 40 (2014), 1690-1698.   Google Scholar

[15]

X. Y. WangX. J. Zhou and C. H. Yang, Chance constrained optimization for copper removal process under uncertainty in zinc hydrometallurgy, CIESC Journal, 71 (2020), 1226-1233.   Google Scholar

[16]

S. W. Xie, Y. F. Xie and F. B. Li, Optimal setting and control for iron removal process based on adaptive neural network soft-sensort, IEEE Transactions on Systems Man & Cybernetics Systems, 99 (2018), 1–13. Google Scholar

[17]

S. W. XieY. F. Xie and H. Ying, A hybrid control strategy for real-time control of the iron removal process of the zinc hydrometallurgy plants, IEEE Transactions on Industrial Informatics, 14 (2018), 5278-5288.  doi: 10.1109/TII.2018.2815659.  Google Scholar

[18]

S. W. XieY. F. XieW. H. Gui and C. H. Yang, Weighted-coupling CSTR modeling and model predictive control with parameter adaptive correction for the goethite process, Journal of Process Control, 68 (2018), 254-267.  doi: 10.1016/j.jprocont.2018.05.006.  Google Scholar

[19]

F. B. ZhouC. G. LiH. Q. Zhu and Y. G. Li, Determination of trace ions of cobalt and copper by UV-vis spectrometry in purification process of zinc hydrometallurgy, Optik, 184 (2019), 227-233.  doi: 10.1016/j.ijleo.2019.03.056.  Google Scholar

[20]

H. Q. ZhuC. H. Yang and W. H. Gui, Soft sensing method of ionic concentration in cobalt removal purification process based on WA and SVM, Computer Engineering and Applications, 47 (2011), 212-214.   Google Scholar

[21]

Z. G. Zhang, Study on Industrial Application Hydrometallurgy of Zinc Purification System to High Impurity Raw Materials, Master thesis, Lanzhou University of Technology in Lanzhou, 2020. Google Scholar

Figure 1.  Flow chart of roasting, leaching and purification process of a lead-zinc smeltery
Figure 2.  Temperature profiles of stage II solution purification in a lead-zinc smeltery
Figure 3.  The equivalent CSTR model
Figure 4.  Flow chart of solution algorithm for parameter identification model
Figure 5.  Stage II purification reaction tank of zinc hydrometallurgy in a lead-zinc smeltery
Figure 6.  Deviation function profile and parameter increment profile in solving process.(a)Deviation function profile; (b)Parameter increment profile
Figure 7.  Nonlinear dynamic system model of the stage II purification process
Figure 8.  Response profiles of outlet ion concentration.(a)Response profile of cobalt ion concentration; (b)Response profile of cadmium ion concentration
Figure 9.  Variation profiles of outlet ion concentration when $ {u_{\rm{b}}} $ is constant and $ {u_{\rm{a}}} $ is variable.(a)Cobalt ion concentration at the outlet; (b)Cadmium ion concentration at the outlet
Figure 10.  Variation profiles of outlet ion concentration when $ {u_{\rm{a}}} $ is constant and $ {u_{\rm{b}}} $ is variable.(a)Cobalt ion concentration at the outlet; (b)Cadmium ion concentration at the outlet
Figure 11.  Variation profiles of outlet ion concentration when $ {u_{\rm{a}}} $ and $ {u_{\rm{b}}} $ change
Figure 12.  The influence of inlet flow $ Q $ on impurity ions concentration.(a) Cobalt ion concentration at the outlet; (b)Cadmium ion concentration at the outlet
Figure 13.  Model test results.(a)Comparison of cobalt ion concentration at the outlet; (b) Comparison of cadmium ion concentration at the outlet
Table 1.  Values of relevant parameters in the reaction process
ParameterSymbolValue
Solution flow rate/(${{\rm{m}}^3}/{\rm{h}}$)$Q$160
Volume of single reaction tank/${{\rm{m}}^3}$${V_p}$108
Volume utilization of reaction tank/$\% $$\backslash$80
Area coefficient/(${{\rm{m}}^2}/{\rm{kg}}$)$p$174
ParameterSymbolValue
Solution flow rate/(${{\rm{m}}^3}/{\rm{h}}$)$Q$160
Volume of single reaction tank/${{\rm{m}}^3}$${V_p}$108
Volume utilization of reaction tank/$\% $$\backslash$80
Area coefficient/(${{\rm{m}}^2}/{\rm{kg}}$)$p$174
Table 2.  Characteristics of sample data
Data typeSymbolAverage valueMaximum valueMinimum value
Inlet cobalt ion concentration/(${\rm{mg/L}}$)${x_{{\rm{a0}}}}$35.25151.19013.382
Inlet cadmium ion concentration/(${\rm{mg/L}}$)${x_{{\rm{b0}}}}$298.278433.148113.229
Outlet cobalt ion concentration/(${\rm{mg/L}}$)${\bar x_{\rm{a}}}$0.4390.9450.257
Outlet cadmium ion concentration/(${\rm{mg/L}}$)${\bar x_b}$15.77258.9431.449
Data typeSymbolAverage valueMaximum valueMinimum value
Inlet cobalt ion concentration/(${\rm{mg/L}}$)${x_{{\rm{a0}}}}$35.25151.19013.382
Inlet cadmium ion concentration/(${\rm{mg/L}}$)${x_{{\rm{b0}}}}$298.278433.148113.229
Outlet cobalt ion concentration/(${\rm{mg/L}}$)${\bar x_{\rm{a}}}$0.4390.9450.257
Outlet cadmium ion concentration/(${\rm{mg/L}}$)${\bar x_b}$15.77258.9431.449
Table 3.  Inlet ion concentration values
Inlet ionSymbolValue
Cobalt ion/(${\rm{g/L}}$)${x_{{\rm{a0}}}}$0.035
Cadmium ion/(${\rm{g/L}}$)$ {x_{{\rm{b0}}}} $0.298
Inlet ionSymbolValue
Cobalt ion/(${\rm{g/L}}$)${x_{{\rm{a0}}}}$0.035
Cadmium ion/(${\rm{g/L}}$)$ {x_{{\rm{b0}}}} $0.298
Table 4.  Model error results
ErrorModel in this paperOriginal model [8]Reformulated model [8]
Maximum error (Cobalt ion) /${\rm{\% }}$31.1249.1032.27
Maximum error (Cadmium ion)/${\rm{\% }}$24.40$\backslash$$\backslash$
Average error (Cobalt ion)/${\rm{\% }}$11.0513.8111.90
Average error (Cadmium ion)/${\rm{\% }}$10.85$\backslash$$\backslash$
ErrorModel in this paperOriginal model [8]Reformulated model [8]
Maximum error (Cobalt ion) /${\rm{\% }}$31.1249.1032.27
Maximum error (Cadmium ion)/${\rm{\% }}$24.40$\backslash$$\backslash$
Average error (Cobalt ion)/${\rm{\% }}$11.0513.8111.90
Average error (Cadmium ion)/${\rm{\% }}$10.85$\backslash$$\backslash$
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