化工学报 ›› 2020, Vol. 71 ›› Issue (3): 1226-1233.doi: 10.11949/0438-1157.20191494

• 过程系统工程 • 上一篇    下一篇

不确定条件下的湿法炼锌除铜过程机会约束优化控制

王湘月,周晓君(),阳春华   

  1. 中南大学自动化学院，湖南 长沙 410083
  • 收稿日期:2019-12-09 修回日期:2019-12-17 出版日期:2020-03-05 发布日期:2019-12-24
  • 通讯作者: 周晓君 E-mail:michael.x.zhou@csu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61873285);湖南省自然科学基金项目(2018JJ3683);中南大学中央高校基本科研业务费专项资金(2019zzts575);中南大学创新驱动计划项目(2018CX012)

Chance constrained optimization for copper removal process under uncertainty in zinc hydrometallurgy

Xiangyue WANG,Xiaojun ZHOU(),Chunhua YANG   

  1. School of Automation, Central South University, Changsha 410083, Hunan, China
  • Received:2019-12-09 Revised:2019-12-17 Online:2020-03-05 Published:2019-12-24
  • Contact: Xiaojun ZHOU E-mail:michael.x.zhou@csu.edu.cn

RichHTML

5

PDF (PC)

71

摘要：

除铜过程是湿法炼锌净化工艺中的重要步骤，受生产环境多变、矿源多样、机理复杂等因素的影响，除铜过程存在不确定性，影响生产的稳定性和可靠性。针对除铜过程中入口溶液流量、底流返回量和入口铜离子浓度的不确定性，造成出口铜离子浓度不稳定的问题，研究不确定条件下的除铜过程机会约束优化控制方法。首先分析了除铜过程的不确定性，利用统计学方法分析不确定参数的分布特性，引入了机会约束的思想，将不确定条件下的除铜过程优化问题建模为机会约束优化问题。然后采用可行域映射方法，将机会约束优化问题转化为非线性规划问题。最后，使用序列二次规划求解该非线性规划问题。Monte Carlo仿真验证了该方法的有效性，可以提高系统的鲁棒性。

关键词: 过程控制, 非线性动力学, Monte Carlo模拟, 除铜过程, 不确定优化, 机会约束优化

Abstract:

Copper removal process(CRP) is an important step in hydrometallurgical zinc process. Due to the changeable production conditions, diverse mineral resources and complex mechanisms, there exist uncertainties in the process of copper removal, which affect the stability and reliability of production. In this paper, the chance constrained optimization for the copper removal process under uncertainty is studied, considering the uncertainty of the inlet flow rate, the returned underflow rate and the inlet copper ions concentration. Firstly, the uncertainties of the copper removal process are analyzed, and the distribution characteristics of the uncertain parameters are analyzed by using statistical methods. The idea of chance constraint is introduced, and the problem of the copper removal process under uncertain conditions is formulated as a chance constrained optimization problem. Then the chance constrained optimization problem is transformed into a nonlinear programming problem by the back-mapping approach(BMA). Finally, sequential quadratic programming is used to solve the resulting problem. Monte Carlo simulation verify the effectiveness of the proposed method and demonstrate that the robustness of the system is significantly improved.

Key words: process control, nonlinear dynamics, Monte Carlo simulation, copper removal process, uncertain optimization, chance constrained optimization

中图分类号: 

  • TP 273

图1

除铜过程流程结构图"

图2

入口溶液流量数据值直方分布图"

图3

入口溶液流量正态概率纸检验"

图4

可行域映射方法框架"

表1

可行域映射法(BMA)与期望值法(EOM)结果比较"

α/%u1/(kg/h)u2/(kg/h)J计算时间/sMonte Carlo模拟结果/%
BMAEOMBMAEOMBMAEOMBMAEOMBMAEOM
6581.166.679.165.7320.4264.60.20.0265.0748.18
70142.266.645.365.7375.0264.60.40.0270.0248.18

图5

Monte Carlo仿真结果"

1 Xie S W,Xie Y F,Ying H,et al.A hybrid control strategy for real-time control of the iron removal process of the zinc hydrometallurgy plants[J].IEEE Transactions on Industrial Informatics,2018,14(12):5278-5288.
2 桂卫华,阳春华,陈晓方,等.有色冶金过程建模与优化的若干问题及挑战[J].自动化学报,2013,39(3):197-207.
Gui W H,Yang C H,Chen X F,et al.Modeling and optimization problems and challenges arising in nonferrous metallurgical processes[J].Acta Automatica Sinica,2013,39(3):197-207.
3 周晓君,阳春华,桂卫华.全局优化视角下的有色冶金过程建模与控制[J].控制理论与应用,2015,32 (9):1158-1169.
Zhou X J,Yang C H,Gui W H.Modeling and control of nonferrous metallurgical processes on the perspective of global optimization[J].Control Theory & Applications,2015,32(9):1158-1169.
4 Zhang B,Yang C H,Zhu H Q,et al.Evaluation strategy for the control of the copper removal process based on oxidation–reduction potential[J].Chemical Engineering Journal,2016,284:294-304.
5 Zhang B,Yang C H,Li Y G,et al.Additive requirement ratio prediction using trend distribution features for hydrometallurgical purification processes[J].Control Engineering Practice,2016,46:10-25.
6 Zhang B,Yang C H,Zhu H Q,et al.Controllable-domain-based fuzzy rule extraction for copper removal process control[J].IEEE Transactions on Fuzzy Systems,2017,26(3):1744-1756.
7 Demirkıran N,Ekmekyapar A,Künkül A,et al.A kinetic study of copper cementation with zinc in aqueous solutions[J].International Journal of Mineral Processing,2007,82(2):80-85.
8 Ahmed I M,El-Nadi Y A,Daoud J A.Cementation of copper from spent copper-pickle sulfate solution by zinc ash[J].Hydrometallurgy,2011,110(1/2/3/4):62-66.
9 Gros F,Baup S,Aurousseau M.Copper cementation on zinc and iron mixtures（2）: Fluidized bed configuration[J].Hydrometallurgy,2011,106(1/2):119-126.
10 Zhang B,Yang C H,Zhu H Q,et al.Kinetic modeling and parameter estimation for competing reactions in copper removal process from zinc sulfate solution[J].Industrial & Engineering Chemistry Research,2013,52(48):17074-17086.
11 Li Y G,Gui W H,Teo K L,et al.Optimal control for zinc solution purification based on interacting CSTR models[J].Journal of Process Control,2012,22(10):1878-1889.
12 张凤雪,阳春华,周晓君,等.基于控制周期计算的锌液净化除铜过程优化控制[J].控制理论与应用,2017,34(10):1388-1395.
Zhang F X,Yang C H,Zhou X J,et al.Optimal control based on control period calculation for copper removal process of zinc solution purification[J].Control Theory & Applications,2017,34(10):1388-1395.
13 Huang M,Zhou X,Huang T,et al.Dynamic optimization based on state transition algorithm for copper removal process[J].Neural Computing and Applications,2019,31(7):2827-2839.
14 Zhou X J,Huang M,Huang T W,et al.Dynamic optimization for copper removal process with continuous production constraints[J].IEEE Transactions on Industrial Informatics,2019.DOI:10.1109/TII.2019.2943500.
doi: 10.1109/TII.2019.2943500
15 Charnes A,Cooper W W,Symonds G H.Cost horizons and certainty equivalents: an approach to stochastic programming of heating oil[J].Management Science,1958,4(3):235-263.
16 Charnes A,Cooper W W.Chance-constrained programming[J].Management Science,1959,6(1):73-79.
17 Boggs P T,Tolle J W.Sequential quadratic programming[J].Acta Numerica,1995,4:1-51.
18 Gill P E,Wong E.Sequential quadratic programming methods[M]//Mixed Integer Nonlinear Programming.New York:Springer,2012:147-224.
19 王耀武,彭建平,狄跃忠,等.铝电解槽干式防渗料在电解过程中的反应机理探讨[J].化工学报,2019,70(3):1035-1041.
Wang Y W,Peng J P,Di Y Z,et al.Mechanism of deterioration for dry barrier material in aluminum electrolysis cells[J].CIESC Journal,2019,70(3):1035-1041.
20 牛大鹏,刘元清.基于改进即时学习算法的湿法冶金浸出过程建模[J].化工学报,2017,68(7):2873-2879.
Niu D P,Liu Y Q.Modeling hydrometallurgical leaching process based on improved just-in-time learning algorithm[J].CIESC Journal,2017,68(7):2873-2879.
21 Näsi J.Statistical analysis of cobalt removal from zinc electrolyte using the arsenic-activated process[J].Hydrometallurgy,2004,73(1/2):123-132.
22 Yang C H,Zhou X J,Zhu H Q,et al.Process modeling issues arising in zinc hydrometallurgy[J].Science Foundation in China,2015,23(2):66-80.
23 Laatikainen K,Lahtinen M,Laatikainen M,et al.Copper removal by chelating adsorption in solution purification of hydrometallurgical zinc production[J].Hydrometallurgy,2010,104(1):14-19.
24 阳春华,韩洁,周晓君,等.有色冶金过程不确定优化方法探讨[J].控制与决策,2018,33(5):856-865.
Yang C H,Han J,Zhou X J,et al.Discussion on uncertain optimization methods for nonferrous metallurgical processes[J].Control and Decision,2018,33(5):856-865.
25 Zhang F X,Yang C H,Zhou X J,et al.Fractional order fuzzy PID optimal control in copper removal process of zinc hydrometallurgy[J].Hydrometallurgy,2018,178:60-76.
26 柴天佑.复杂工业过程运行优化与反馈控制[J].自动化学报,2013,39(11):1744-1757.
Chai T Y.Operational optimization and feedback control for complex industrial processes[J].Acta Automatica Sinica,2013,39(11):1744-1757.
27 Wendt M,Li P,Wozny G.Nonlinear chance-constrained process optimization under uncertainty[J].Industrial & Engineering Chemistry Research,2002,41(15):3621-3629.
28 Geletu A,Hoffmann A,Klöppel M,et al.Monotony analysis and sparse-grid integration for nonlinear chance constrained process optimization[J].Engineering Optimization,2011,43(10):1019-1041.
29 Garcia H A,Wozny G.Chance constrained optimization of process systems under uncertainty(I): Strict monotonicity[J].Computers & Chemical Engineering,2009,33(10):1568-1583.
30 Li P,Arellano-Garcia H,Wozny G.Chance constrained programming approach to process optimization under uncertainty[J].Computers & Chemical Engineering,2008,32(1/2):25-45.
31 Zhang H,Li P.Chance constrained programming for optimal power flow under uncertainty[J].IEEE Transactions on Power Systems,2011,26(4):2417-2424.
32 Zhang H,Li P.Probabilistic analysis for optimal power flow under uncertainty[J].IET Generation,Transmission & Distribution,2010,4(5):553-561.
[1] 杜宇浩, 阎高伟, 李荣, 王芳. 基于局部线性嵌入的测地线流式核多工况软测量建模方法[J]. 化工学报, 2020, 71(3): 1278-1287.
[2] 韩宇, 李俊芳, 高强, 田宇, 禹国刚. 基于故障判别增强KECA算法的故障检测[J]. 化工学报, 2020, 71(3): 1254-1263.
[3] 王磊, 陈玉婷, 徐燕燕, 叶爽, 黄伟光. 综合考虑经济性与效率的换热网络多目标约束优化方法[J]. 化工学报, 2020, 71(3): 1189-1201.
[4] 张璐, 张嘉成, 韩红桂, 乔俊飞. 基于模糊神经网络的污水处理生化除磷过程控制[J]. 化工学报, 2020, 71(3): 1217-1225.
[5] 李晨莹, 刘琳琳, 张磊, 顾偲雯, 都健. 不确定性下基于多工况优化的可控性换热器网络综合[J]. 化工学报, 2020, 71(3): 1154-1162.
[6] 赵绍磊, 王耀国, 张腾, 周丽娜, 龚俊波, 汤伟伟. 制药结晶中的先进过程控制[J]. 化工学报, 2020, 71(2): 459-474.
[7] 耿庆庆,李瑞琦,杨茉. Hurst指数在判别火灾轰燃中的应用[J]. 化工学报, 2019, 70(S2): 369-375.
[8] 支恩玮, 闫飞, 任密蜂, 阎高伟. 基于迁移变分自编码器-标签映射的湿式球磨机负荷参数软测量[J]. 化工学报, 2019, 70(S1): 150-157.
[9] 宋泽雨, 李国庆, 刘凌轩. 一种新的无模型自适应控制模型参数整定方法[J]. 化工学报, 2019, 70(9): 3430-3440.
[10] 尚良超, 陈晓东, 肖杰. 喷雾干燥颗粒表面形貌形成过程粗粒化模拟[J]. 化工学报, 2019, 70(6): 2153-2163.
[11] 黄冬, 罗雄麟. 化工过程大范围工况变化的工况迁移控制策略判定[J]. 化工学报, 2019, 70(5): 1848-1857.
[12] 谢昭明, 邓容锐, 刘作华, 邓丽, 李千文, 张柱, 殷兆迁, 陶长元. 钠化焙烧转炉钒渣粉体分形生长的演化行为[J]. 化工学报, 2019, 70(5): 1904-1912.
[13] 石博文, 尹燕燕, 刘飞. 基于PSO-控制变量参数化混合策略的间歇化工过程优化控制[J]. 化工学报, 2019, 70(3): 979-986.
[14] 王志甄, 邹志云. 基于神经网络的pH中和过程非线性预测控制[J]. 化工学报, 2019, 70(2): 678-686.
[15] 耿志强, 景邵星, 白菊, 王仲凯, 朱群雄, 韩永明. 基于MWSPCA-CBR的智能预警方法研究及其在石化工业中的应用[J]. 化工学报, 2019, 70(2): 572-580.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 姜岩, 任南琪, 蔡徇, 吴迪, 乔丽艳, 林森. Biodegradation of Phenol and 4-Chlorophenol by the Mutant Strain CTM 2[J]. CIESC Journal, 2008, 16(5): 796 -800 .
[2] 王连邦, 詹星月, 杨珍珍, 马淳安. Catalytic Hydrolysis of Borohydride for Fuel Cells[J]. CIESC Journal, 2011, 19(4): 693 -697 .
[3] 洪夏萍, 张蓉, 童少平, 马淳安. Preparation of Ti/PTFE-F-PbO2 Electrode with a Long Life from the Sulfamic Acid Bath and Its Application in Organic Degradation[J]. CIESC Journal, 2011, 19(6): 1033 -1038 .
[4] 韩明汉,李晓锦,林世雄,张吉瑞,陈曙. FX-01催化剂上苯与丙烯烷基化反应过程研究(Ⅰ)——反应动力学 [J]. CIESC Journal, 1999, 50(1): 65 -69 .
[5] 彭文博, 漆虹, 陈纲领, 邹琳玲, 邢卫红, 徐南平. 19通道多孔陶瓷膜渗透过程的CFD模拟 [J]. 化工学报, 2007, 58(8): 2021 -2026 .
[6] 张跃敏,许志宏,周家驹. 计算机辅助蒸馏过程模拟及控制(Ⅰ)——多组分复杂蒸馏控制的相对增益矩阵计算研究 [J]. CIESC Journal, 1990, 41(3): 364 -371 .
[7] 曹伟红,韩世钧. 从液体混合物的粘度数据推算汽液平衡数据 [J]. CIESC Journal, 1990, 41(4): 508 -514 .
[8] 关宇, 郭烈锦, 张西民, 吕友军, 郝小红. 生物质模型化合物在超临界水中的气化 [J]. 化工学报, 2006, 57(6): 1426 -1431 .
[9] 徐俊;张军营;潘霞;郑楚光 . CO2矿物碳酸化隔离实验初探 [J]. CIESC Journal, 2006, 57(10): 2455 -2458 .
[10] 林金清. 含水粒子层的红外线干燥 [J]. CIESC Journal, 1996, 47(5): 607 -614 .
版权所有 © 2019 《化工学报》编辑部
北京市东城区青年湖南街13号（100011）
电话：010-64519489，010-64519485，010-64519486，010-64519490，010-64519362
E-Mail：hgxb@cip.com.cn
京ICP备12046843号-3
京公网安备 11010102001995号
本系统由北京玛格泰克科技发展有限公司设计开发