基于锌电解过程机理模型的酸锌浓度控制策略

doi:10.3969/j.issn.0438-1157.2013.12.019

化工学报 ›› 2013, Vol. 64 ›› Issue (12): 4396-4400.DOI: 10.3969/j.issn.0438-1157.2013.12.019

基于锌电解过程机理模型的酸锌浓度控制策略

公衍海, 张威, 熊智华

清华大学自动化系, 北京 100084

收稿日期:2013-08-26 修回日期:2013-09-06 出版日期:2013-12-05 发布日期:2013-12-05
通讯作者: 熊智华
作者简介:公衍海（1988- ），男，硕士研究生。
基金资助:
国家重点基础研究发展计划项目（2012CB720500）；轻工过程先进控制教育部重点实验室开放课题资助（江南大学）项目。

Zinc-ion concentration control based on mechanical model of zinc electrowinning process

GONG Yanhai, ZHANG Wei, XIONG Zhihua

Department of Automation, Tsinghua University, Beijing 100084, China

Received:2013-08-26 Revised:2013-09-06 Online:2013-12-05 Published:2013-12-05
Supported by:
supported by the National Basic Research Program of China (2012CB720500) and the Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University)，MOE，China.

摘要/Abstract

摘要： 锌电解过程是一个典型的大惯性系统，电解槽的酸锌浓度往往难以控制。提出了基于锌电解机理模型的酸锌浓度前馈-反馈控制方法，以克服电流密度、进液浓度工艺参数的扰动。首先在Scott等提出的锌电解机理模型基础上，估计了传质系数等关键参数，得到了锌电解能耗模型，再加入电解槽酸锌浓度的物料动态平衡模型，从而建立了较完整的锌电解过程仿真模型。针对电流密度变化、进料中锌浓度波动等扰动引起电解液酸锌浓度变化的情况，提出了电解液锌浓度的前馈-反馈控制策略，其中反馈通道采用PID控制，而前馈通道的增益直接由机理模型计算得到。最后以仿真模型为对象验证了该控制策略，仿真结果表明该方法简单有效。

关键词: 电解, 能耗模型, 动态仿真, 前馈控制

Abstract: Zinc electrowinning process is a typical large time-delay system,so zinc ion and acid concentration are difficult to control.To overcome the fluctuations of current density and zinc ion concentration in the feed,feedforward-feedback control strategy based on the mechanism model of zinc electrolytic is used.First,based on the model given by Scott,the key parameters such as the mass transfer coefficient are estimated and the energy consumption model is built.Then,a more complete zinc electrowinning process simulation model is established by adding the dynamic balance model of zinc ion concentration in the cell.For the situation such as the changes of current density and fluctuations of feed zinc ion concentration that affect the zinc ion concentration in the cell,feedforward-feedback control strategy is put forward,where the feedback loop uses PID,while the feedforward gain is calculated directly from the mechanistic models.Finally,the control strategy is verified using the simulation model,and the results show that this method is simple and effective.

Key words: electrolysis, energy consumption model, dynamic simulation, feedforward control

中图分类号:

TF803.27

公衍海, 张威, 熊智华. 基于锌电解过程机理模型的酸锌浓度控制策略[J]. 化工学报, 2013, 64(12): 4396-4400.

GONG Yanhai, ZHANG Wei, XIONG Zhihua. Zinc-ion concentration control based on mechanical model of zinc electrowinning process[J]. CIESC Journal, 2013, 64(12): 4396-4400.

参考文献 16

[1]	Bryson A W.Modelling the performance of electrowinning cells//Hydrometallurgy '81, Society of Chemical Industry Symposium[C].Manchester, 1981
[2]	Scott A C. Development and application of a mathematical model for the zinc electrowinning process[D].Sydney:University of Sydney, 1988
[3]	Scott A C, Pitblado R M, Barton G W.A mathematical model of a zinc electrowinning cell//APCOM 87. Proceedings of the Twentieth International Symposium on the Application of Computers and Mathematics in the Mineral Industries:Metallurgy[C].Johannesburg, SAIMM, 1987
[4]	Barton G W, Scott A C.A validated mathematical model for a zinc electrowinning cell[J].Journal of Applied Electrochemistry, 1992 (22):104-115
[5]	Barton G W, Scott A C.Scale-up effects in modeling a full-size zinc electrowinning cell[J].Journal of Applied Electrochemistry, 1992 (22):687-692
[6]	Barton G W, Scott A C.Industrial applications of a mathematical model for the zinc electrowinning process[J].Journal of Applied Electrochemistry, 1994 (24):377-383
[7]	Mahon M, Wasik L, Alfantazi A.Development and implementation of a zinc electrowinning process simulation[J].Journal of The Electrochemical Society, 2012 (8):486-492
[8]	Gui Weihua (桂卫华), Yang Chunhua (阳春华).Intelligent Modeling, Control and Optimization for Complex Non-ferrous Metallurgy Production Process (复杂有色冶金生产过程智能建模、控制与优化)[M].Beijing:Science Press, 2010
[9]	Mei Guanggui (梅光贵), Wang Derun (王德润), Zhou Jingyuan (周静元), Wang Hui (王辉).Zinc Hydrometallurgy (湿法炼锌学)[M].Changsha:Central South University Press, 2001
[10]	Scott A C, Pitblado R M, Barton G W, Alut A R. Experimental determination of the factors affecting zinc electrowinning efficiency[J].Journal of Applied Electrochemistry, 1988 (18):120-127
[11]	Alfantazi A M, Dreisinger D B.The role of zinc and sulfuric acid concentrations on zinc electrowinning from industrial sulfate based electrolyte[J].Journal of Applied Electrochemistry, 2001 (31):641-646
[12]	Wang Hui (王辉), Xiao Bowen (肖博文), Yuan Weikang (袁渭康).The behavior of heat removal unsteady state SO2 converters (Ⅱ):Controlling plan for two-point heat removal converter[J].Journal of Chemical Industry and Engineering (China) (化工学报), 1998, 49 (4):455-461
[13]	Wu Jianfeng (吴建锋), He Xiaorong (何小荣), Chen Bingzhen (陈丙珍).Back-propagation neural network model of dynamic system and its application[J].Journal of Chemical Industry and Engineering (China) (化工学报), 2000, 51 (3):378-382
[14]	Wang Shuqing (王树青).Industrial Process Control Engineering (工业过程控制工程)[M].Beijing:Chemical Industry Press, 2002
[15]	Hamann C H, Hamnett A, Vielstich W.Electrochemistry[M].Wiley-VCH, 2007
[16]	Huang Dexian (黄德先), Ye Xinyu (叶心宇), Zhu Jianmin (竺建敏), et al.Advanced Chemical Process Control (化工过程先进控制)[M].Beijing:Chemical Industry Press, 2006

基于锌电解过程机理模型的酸锌浓度控制策略

Zinc-ion concentration control based on mechanical model of zinc electrowinning process

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