湿法炼锌沉铁过程氧化速率优化控制

谢世文; 谢永芳; 李勇刚; 阳春华; 桂卫华

doi:10.16383/j.aas.2015.c150192

湿法炼锌沉铁过程氧化速率优化控制

doi: 10.16383/j.aas.2015.c150192

1.
中南大学信息科学与工程学院, 长沙 410083

基金项目:

国家自然科学基金创新研究群体科学基金(61321003),国家高技术研究发展计划(863计划)(2014AA041803),国家自然科学基金(61273186,61503416),中南大学创新驱动计划项目(2015cx007),中南大学中央高校基本科研业务费(2015zzts049)资助

详细信息

作者简介:
谢世文中南大学信息科学与工程学院博士研究生. 主要研究方向为工业过程建模与优化控制研究, 智能控制系统.E-mail: mathking@csu.edu.cn

通讯作者:
谢永芳博士, 中南大学教授.主要研究方向为复杂工业过程建模与控制、分散鲁棒控制.本文通信作者.

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出版历程
- 收稿日期: 2015-04-21
- 修回日期: 2015-08-18
- 刊出日期: 2015-12-20

Optimal Control of Oxidizing Rate for Iron Precipitation Process in Zinc Hydrometallurgy

1.
School of Information Science and Engineering, Central South University, Changsha 410083

Funds:

Supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (61321003), National High Technology Research and Development Program of China (863 Program) (2014AA041803), National Natural Science Foundation of China (61273186, 61503416), Innovation-driven Plan in Central South University (2015cx007), the Fundamental Research Funds for the Central Universities of Central South University (2015zzts049)

摘要

摘要: 湿法炼锌沉铁过程针铁矿沉淀形成的条件要求苛刻, 亚铁离子的氧化速率必须控制在合理的范围内才能保证溶液中的铁离子以针铁矿形式除去. 本文在沉铁过程动态模型的基础上, 根据针铁矿沉淀形成的条件和结合流程工艺要求, 优化设定每个反应器出口的亚铁离子浓度, 进而建立针铁矿法沉铁过程氧化速率优化控制模型. 采用控制参数化方法将最优控制求解问题转化为非线性规划, 通过状态转移优化算法求取最优的氧气和氧化锌控制率, 以合理控制沉铁过程亚铁离子的氧化速率. 仿真结果表明, 优化控制模型计算所得的控制量不仅可以保证反应过程的氧化速率符合生成针铁矿沉淀的条件, 而且可以稳定生产流程.
- 优化设定 /
- 优化控制 /
- 控制参数化 /
- 状态转移优化算法 /
- 针铁矿法沉铁
Abstract: In the iron removal process in zinc hydrometallurgy, the reaction conditions to form goethite precipitate are difficult to achieve, in which the oxidizing rate of ferrous ion has to be strictly controlled to remove the iron ion from leach solution by goethite. On the basis of dynamic model for iron removal process and according to the forming conditions of goethite precipitate and procedure requirements, the optimal setting model of reactor outlet ferrous ion concentration is investigated in this paper. An optimal control model of oxidizing rate for iron precipitation process is established. The optimal control problem is transformed to a nonlinear mathematical programming problem by control parameterization method. The mathematical programming problem is then solved utilizing state transition optimization algorithm to obtain the optimal control of oxygen and zinc oxide to make the oxidizing rate in the best conditions of forming goethite precipitate. Numerical simulations validate that the optimal control of oxidizing rate can not only satisfy the forming conditions of goethite precipitate but also stabilize the production process.
- Optimal setting /
- optimal control /
- control parameterization /
- state transition optimization algorithm /
- iron removal process by goethite

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参考文献(28)

[1]	Loan M, Newman O M G, Cooper R M G, Farrow J B, Parkinson G M. Defining the Paragoethite process for iron removal in zinc hydrometallurgy. Hydrometallurgy, 2006, 81(2): 104-129
[2]	Ismael M R C, Carvalho J M R. Iron recovery from sulphate leach liquors in zinc hydrometallurgy. Minerals Engineering, 2003, 16(1): 31-39
[3]	Chang Y F, Zhai X J, Li B C, Fu Y. Removal of iron from acidic leach liquor of lateritic nickel ore by goethite precipitate. Hydrometallurgy, 2010, 101(1-2): 84-87
[4]	Gui Wei-Hua, Yang Chun-Hua, Chen Xiao-Fang, Wang Ya-Lin. Modeling and optimization problems and challenges arising in nonferrous metallurgical process. Acta Automatica Sinica, 2013, 39(3): 197-207(桂卫华, 阳春华, 陈晓方, 王雅琳. 有色冶金过程建模与优化的若干问题及挑战. 自动化学报, 2013, 39(3): 197-207)
[5]	Gui Wei-Hua, Yang Chun-Hua, Li Yong-Gang, He Jian-Jun, Yin Lin-Zi. Data-driven operational-pattern optimization for copper flash smelting process. Acta Automatica Sinica, 2009, 35(6): 717-724(桂卫华, 阳春华, 李勇刚, 贺建军, 尹林子. 基于数据驱动的铜闪速熔炼过程操作模式优化及应用. 自动化学报, 2009, 35(6): 717-724)
[6]	Chai Tian-You, Ding Jin-Liang, Wang Hong, Su Chun-Yi. Hybrid intelligent optimal control method for operation of complex industrial processes. Acta Automatica Sinica, 2008, 34(5): 505-515(柴天佑, 丁进良, 王宏, 苏春翌. 复杂工业过程运行的混合智能优化控制方法. 自动化学报, 2008, 34(5): 505-515)
[7]	Ma Tian-Yu, Gui Wei-Hua. Optimal control for continuous bauxite grinding process in ball-mill. Control Theory & Applications, 2012, 29(10): 1339-1347(马天雨, 桂卫华. 铝土矿连续磨矿过程球磨机优化控制. 控制理论与应用, 2012, 29(10): 1339-1347)
[8]	Ye J X, Xu H L, Feng E M, Xiu Z L. Optimization of a fed-batch bioreactor for 1, 3-propanediol production using hybrid nonlinear optimal control. Journal of Process Control, 2014, 24(10): 1556-1569
[9]	Zhou P, Chai T Y, Wang H. Intelligent optimal-setting control for grinding circuits of mineral processing process. IEEE Transactions on Automation Science and Engineering, 2009, 6(4): 730-743
[10]	Zhou P, Chai T Y, Sun J. Intelligence-based supervisory control for optimal operation of a DCS-controlled grinding system. IEEE Transactions on Control Systems Technology, 2013, 21(1): 162-175
[11]	Li Y G, Gui W H, Teo K L, Zhu H Q, Chai Q Q. Optimal control for zinc solution purification based on interacting CSTR models. Journal of Process Control, 2012, 22(10): 1878-1889
[12]	Yang C H, Gui W H, Kong L S, Wang Y L. Modeling and optimal-setting control of blending process in a metallurgical industry. Computers & Chemical Engineering, 2009, 33(7): 1289-1297
[13]	Sun B, Gui W H, Wang Y L, Yang C H. Intelligent optimal setting control of a cobalt removal process. Journal of Process Control, 2014, 24(5): 586-599
[14]	Chai T Y, Ding J L, Wu F H. Hybrid intelligent control for optimal operation of shaft furnace roasting process. Control Engineering Practice, 2011, 19(3): 264-275
[15]	Loxton R, Lin Q, Teo K L. Minimizing control variation in nonlinear optimal control. Automatica, 2013, 49(9): 2652-2664
[16]	Xie Y F, Xie S W, Chen X F, Gui W H, Yang C H, Caccetta L. An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy. Hydrometallurgy, 2015, 151(1): 62-72
[17]	Marsalek R. The reduction of zinc using goethite process and adsorption of Pb+II, Cu+II and Cr+III on selected precipitate. International Journal of Environmental Science and Development, 2011, 2(4): 253-258
[18]	Xie Shi-Wen, Xie Yong-Fang, Yang Chun-Hua, Jiang Zhao-Hui, Gui Wei-Hua. A ferrous iron concentration prediction model for the process of iron precipitation by goethite. Acta Automatica Sinica, 2014, 40(5): 830-837(谢世文, 谢永芳, 阳春华, 蒋朝辉, 桂卫华. 针铁矿法沉铁过程亚铁离子浓度预测. 自动化学报, 2014, 40(5): 830-837)
[19]	Stumm W, Lee G G. Oxygenation of ferrous iorn. Industry & Engineering Chemistry, 1961, 53(2): 143-146
[20]	Seetharaman S. Treatise on Process Metallurgy, Volume 1: Process Fundamentals. Netherlands: Elsevier, 2014. 831-852
[21]	Tsoulos I G, Stavrakoudis A. On locating all roots of systems of nonlinear equations inside bounded domain using global optimization methods. Nonlinear Analysis: Real World Applications, 2010, 11(4): 2465-2471
[22]	Pontryagin L S. Mathematical Theory of Optimal Processes. New York: Gordon and Breach Science Publishers, 1986.
[23]	Luus R. Optimal control by dynamic programming using systematic reduction in grid size. International Journal of Control, 1990, 51(5): 995-1013
[24]	Teo K L, Goh C J, Wong K H. A unified computational approach to optimal control problems. New York: Longman Scientific and Technical, 1991.
[25]	Goh C J, Teo K L. Control parametrization: a unified approach to optimal control problems with general constraints. Automatica, 1988, 24(1): 3-18
[26]	Teo K L, Rehbock V, Jennings L S. A New computational algorithm for functional inequality constrained optimization problems. Automatica, 1993, 29(3): 789-792
[27]	Zhou X J, Yang C H, Gui W H. State transition algorithm. Journal of Industrial and Management Optimization, 2012, 8(4): 1039-1056
[28]	Zhou X J, Yang C H, Gui W H. Nonlinear system identification and control using state transition algorithm. Applied Mathematics and Computation, 2014, 226: 169-179

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湿法炼锌沉铁过程氧化速率优化控制

doi: 10.16383/j.aas.2015.c150192

作者简介:
谢世文中南大学信息科学与工程学院博士研究生. 主要研究方向为工业过程建模与优化控制研究, 智能控制系统.E-mail: mathking@csu.edu.cn

通讯作者:
谢永芳博士, 中南大学教授.主要研究方向为复杂工业过程建模与控制、分散鲁棒控制.本文通信作者.

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Optimal Control of Oxidizing Rate for Iron Precipitation Process in Zinc Hydrometallurgy

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湿法炼锌沉铁过程氧化速率优化控制

doi: 10.16383/j.aas.2015.c150192

作者简介: 谢世文中南大学信息科学与工程学院博士研究生. 主要研究方向为工业过程建模与优化控制研究, 智能控制系统.E-mail: mathking@csu.edu.cn

通讯作者: 谢永芳博士, 中南大学教授.主要研究方向为复杂工业过程建模与控制、分散鲁棒控制.本文通信作者.

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出版历程

Optimal Control of Oxidizing Rate for Iron Precipitation Process in Zinc Hydrometallurgy

计量

出版历程

目录

作者简介:
谢世文中南大学信息科学与工程学院博士研究生. 主要研究方向为工业过程建模与优化控制研究, 智能控制系统.E-mail: mathking@csu.edu.cn

通讯作者:
谢永芳博士, 中南大学教授.主要研究方向为复杂工业过程建模与控制、分散鲁棒控制.本文通信作者.