Discussion on Modeling and Optimal Control of Nonferrous Metallurgical Purification Process
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摘要: 净化过程是有色金属湿法冶炼的关键工序.它通过置换沉淀的方式去除有色金属矿物浸出液中的杂质金属离子，为后续电解过程提供高纯度的金属电解液，其控制效果直接影响最终金属产品的质量、生产成本以及生产全流程的稳定性.目前，入矿来源混杂、反应机理复杂等因素制约了净化过程的高效和绿色生产.从净化过程工艺与反应机理的特点出发，提炼了净化过程各除杂工段在建模和优化控制中的共性问题，对净化过程建模与优化控制方法的研究现状进行了综述，并以湿法炼锌净化过程为例，较详细地介绍了在沉铁、除铜、除钴工序建模和优化控制方面的最新研究成果.最后结合自动化技术的发展新动向，对湿法冶金净化过程自动化的未来发展趋势进行了展望.Abstract: Purification is an essential part of non-ferrous hydrometallurgy, which involves using additives to replace impurities in the leaching solution thus to provide highly purified solution for subsequent electrowinning. The performance of purification process largely affects the quality of final metal product and the stability of the entire hydrometallurgy process. Due to the diversity of concentrate sources and the complexity of removal reactions, it is difficult to realize high efficiency and green production. From the perspectives of technological features and reaction mechanisms, the common problems in modeling and optimal control of purification process are first extracted. Then the current status in modeling and optimal control of purification process are reviewed. Zinc hydrometallurgical purification process is described as a typical example to introduce the recent results of the author's group in modeling, optimization and control of iron precipitation, copper removal and cobalt removal processes. This paper ends with a perspective on the potential trends in the automation of nonferrous hydrometallurgical purification process.
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Key words:
- Non-ferrous metallurgy /
- purification process /
- process modeling /
- optimal control
1) 本文责任编委 王伟 -
图 5 多反应器关联梯度优化双层控制框架
Fig. 5 Two layer control frame for cooperated gradient optimization of multiple reactor system
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[1] 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 http://www.aas.net.cn/CN/abstract/abstract17799.shtmlGui Wei-Hua, Yang Chun-Hua, Chen Xiao-Fang, Wang Ya-Lin. Modeling and optimization problems and challenges arising in nonferrous metallurgical processes. Acta Automatica Sinica, 2013, 39(3): 197-207 http://www.aas.net.cn/CN/abstract/abstract17799.shtml [2] 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.控制理论与应用, 2015, 32(9): 1158-1169 http://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201509004.htmZhou Xiao-Jun, Yang Chun-Hua, Gui Wei-Hua. Modeling and control of nonferrous metallurgical processes on the perspective of global optimization. Control Theory & Applications, 2015, 32(9): 1158-1169 http://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201509004.htm [3] Sun B, Gui W H, Wu T B, Wang Y L, Yang C H. An integrated prediction model of cobalt ion concentration based on oxidation-reduction potential. Hydrometallurgy, 2013, 140: 102-110 doi: 10.1016/j.hydromet.2013.09.015 [4] Hodaifa G, Ochando-Pulido J M, Rodriguez-Vives S, Martinez-Ferez A. Optimization of continuous reactor at pilot scale for olive-oil mill wastewater treatment by Fenton-like process. Chemical Engineering Journal, 2013, 220: 117-124 doi: 10.1016/j.cej.2013.01.065 [5] Komulainen T, Doyle Ⅲ F J, Rantala A, Jämsä-Jounela S L. Control of an industrial copper solvent extraction process. Journal of Process Control, 2009, 19(1): 2-15 doi: 10.1016/j.jprocont.2008.04.019 [6] Yuan Q Y, Wang F L, He D K, Jia R D, Wang C. Study on the plant-wide modeling of gold hydrometallurgical process. In: Proceedings of the 26th Chinese Control and Decision Conference (2014 CCDC). Changsha, China: IEEE, 2014. 4013-4018 [7] Liotta F, Chatellier P, Esposito G, Fabbricino M, Van Hullebusch E D, Lens P N L. Hydrodynamic mathematical modelling of aerobic plug flow and nonideal flow reactors: a critical and historical review. Critical Reviews in Environmental Science and Technology, 2014, 44(23): 2642-2673 doi: 10.1080/10643389.2013.829768 [8] Jones P N, Özcan-Taşkin N G, Yianneskis M. The use of momentum ratio to evaluate the performance of CSTRs. Chemical Engineering Research and Design, 2009, 87(4): 485-491 doi: 10.1016/j.cherd.2008.12.005 [9] Wang L Y, Gui W H, Teo K L, Loxton R, Yang C H. Optimal control problems arising in the zinc sulphate electrolyte purification process. Journal of Global Optimization, 2012, 54(2): 307-323 doi: 10.1007/s10898-012-9863-x [10] 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 doi: 10.1016/j.jprocont.2012.09.008 [11] Saeed T, Sun G Z. Kinetic modelling of nitrogen and organics removal in vertical and horizontal flow wetlands. Water Research, 2011, 45(10): 3137-3152 doi: 10.1016/j.watres.2011.03.031 [12] Roudsari S F, Ein-Mozaffari F, Dhib R. Use of CFD in modeling MMA solution polymerization in a CSTR. Chemical Engineering Journal, 2013, 219: 429-442 doi: 10.1016/j.cej.2012.12.049 [13] Stark A K, Altantzis C, Bates R B, Ghoniem A F. Towards an advanced reactor network modeling framework for fluidized bed biomass gasification: incorporating information from detailed CFD simulations. Chemical Engineering Journal, 2016, 303: 409-424 doi: 10.1016/j.cej.2016.06.026 [14] Zhang B, Yang C H, Zhu H Q, Li Y G, Gui W H. Kinetic modeling and parameter estimation for competing reactions in copper removal process from zinc sulfate solution. Industrial & Engineering Chemistry Research, 2013, 52(48): 17074-17086 [15] Fugleberg S, Järvinen A, Yllö E. Recent development in solution purification at Outokumpu zinc plant, Kokkola. In: Proceedings of the 1993 International Symposium on World Zinc'93. Victoria, Australia: Australasian Institute of Mining and Metallurgy, 1993. 241-247 [16] Tozawa K, Nishimura T, Akahori M, Malaga M A. Comparison between purification processes for zinc leach solutions with arsenic and antimony trioxides. Hydrometallurgy, 1992, 30(1-3): 445-461 doi: 10.1016/0304-386X(92)90099-L [17] Nelson A, Demopoulos G P, Houlachi G. The effect of solution constituents and novel activators on cobalt cementation. Canadian Metallurgical Quarterly, 2000, 39(2): 175-186 doi: 10.1179/cmq.2000.39.2.175 [18] Nelson A, Wang W, Demopoulos G P, Houlachi G. The removal of cobalt from zinc electrolyte by cementation: a critical review. Mineral Processing and Extractive Metallurgy Review, 2000, 20(4-6): 325-356 doi: 10.1080/08827500008547436 [19] Boyanov B, Konareva V, Kolev N. Removal of cobalt and nickel from zinc sulphate solutions using activated cementation. Journal of Mining and Metallurgy, Section B: Metallurgy, 2004, 40(1): 41-55 doi: 10.2298/JMMB0401041B [20] Näsi J. Statistical analysis of cobalt removal from zinc electrolyte using the arsenic-activated process. Hydrometallurgy, 2004, 73(1-2): 123-132 doi: 10.1016/j.hydromet.2003.09.005 [21] 赵光明, 李向阳, 张广积, 杨超.铜离子在硫酸锌电解液深度净化除钴中的作用.过程工程学报, 2010, 10(3): 482-487 http://www.cnki.com.cn/Article/CJFDTOTAL-HGYJ201003012.htmZhao Guang-Ming, Li Xiang-Yang, Zhang Guang-Ji, Yang Chao. Effect of copper ions on deep removal of cobalt from zinc sulfate solution. The Chinese Journal of Process Engineering, 2010, 10(3): 482-487 http://www.cnki.com.cn/Article/CJFDTOTAL-HGYJ201003012.htm [22] Zeng G S, Xie G, Yang D J, Wang J K, Li X, Li R X. The effect of cadmium ion on cobalt removal from zinc sulfate solution. Minerals Engineering, 2006, 19(2): 197-200 doi: 10.1016/j.mineng.2005.05.010 [23] Buzzi-Ferraris G, Manenti F. Kinetic models analysis. Chemical Engineering Science, 2009, 64(5): 1061-1074 doi: 10.1016/j.ces.2008.10.062 [24] Pantelides C C, Renfro J G. The online use of first-principles models in process operations: review, current status and future needs. Computers & Chemical Engineering, 2013, 51: 136-148 [25] Deng J, Huang B. Identification of nonlinear parameter varying systems with missing output data. AIChE Journal, 2012, 58(11): 3454-3467 doi: 10.1002/aic.13735 [26] Jin X, Huang B, Shook D S. Multiple model LPV approach to nonlinear process identification with EM algorithm. Journal of Process Control, 2011, 21(1): 182-193 doi: 10.1016/j.jprocont.2010.11.008 [27] Wang X F, Chen J D, Liu C B, Pan F. Hybrid modeling of penicillin fermentation process based on least square support vector machine. Chemical Engineering Research and Design, 2010, 88(4): 415-420 doi: 10.1016/j.cherd.2009.08.010 [28] 于亮, 毛志忠, 贾润达.基于ICA-LSSVM的铜萃取混合模型.东北大学学报(自然科学版), 2014, 35(10): 1369-1372 doi: 10.3969/j.issn.1005-3026.2014.10.001Yu Liang, Mao Zhi-Zhong, Jia Run-Da. Hybrid model based on ICA-LSSVM for copper extraction. Journal of Northeastern University (Natural Science), 2014, 35(10): 1369-1372 doi: 10.3969/j.issn.1005-3026.2014.10.001 [29] Grbić R, Slišković D, Kadlec P. Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models. Computers & Chemical Engineering, 2013, 58: 84-97 [30] He K X, Cheng H, Du W L, Qian F. Online updating of NIR model and its industrial application via adaptive wavelength selection and local regression strategy. Chemometrics and Intelligent Laboratory Systems, 2014, 134: 79-88 doi: 10.1016/j.chemolab.2014.03.007 [31] 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: 62-72 doi: 10.1016/j.hydromet.2014.11.004 [32] Zhang B, Yang C H, Li Y G, Wang X L, Zhu H Q, Gui W H. Additive requirement ratio prediction using trend distribution features for hydrometallurgical purification processes. Control Engineering Practice, 2016, 46: 10-25 doi: 10.1016/j.conengprac.2015.09.006 [33] Sun B, Gui W H, Wang Y L, Yang C H, He M F. A gradient optimization scheme for solution purification process. Control Engineering Practice, 2015, 44: 89-103 doi: 10.1016/j.conengprac.2015.07.008 [34] 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 http://www.aas.net.cn/CN/abstract/abstract18351.shtmlXie 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 http://www.aas.net.cn/CN/abstract/abstract18351.shtml [35] 谢世文, 谢永芳, 李勇刚, 阳春华, 桂卫华.湿法炼锌沉铁过程氧化速率优化控制.自动化学报, 2015, 41(12): 2036-2046 http://www.aas.net.cn/CN/abstract/abstract18777.shtmlXie Shi-Wen, Xie Yong-Fang, Li Yong-Gang, Yang Chun-Hua, Gui Wei-Hua. Optimal control of oxidizing rate for iron precipitation process in zinc hydrometallurgy. Acta Automatica Sinica, 2015, 41(12): 2036-2046 http://www.aas.net.cn/CN/abstract/abstract18777.shtml [36] Zhang B, Yang C H, Zhu H Q, Li Y G, Gui W H. Evaluation strategy for the control of the copper removal process based on oxidation-reduction potential. Chemical Engineering Journal, 2016, 284: 294-304 doi: 10.1016/j.cej.2015.07.094 [37] 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 doi: 10.1016/j.jprocont.2014.03.002 [38] Sun B, Yang C H, Gui W H. A discussion of the control of nonferrous metallurgical processes. IFAC-PapersOnLine, 2015, 48(17): 80-85 doi: 10.1016/j.ifacol.2015.10.082 [39] Børve K, Østvold T. Norzink removal of cobalt from zinc sulphate electrolytes. In: Proceedings of the 1994 International Symposium "Hydrometallurgy'94". Cambridge, England: Springer, 1994. 563-577 [40] Antonelli R, Astolfi A. Continuous stirred tank reactors: easy to stabilise? Automatica, 2003, 39(10): 1817-1827 [41] Wu F. LMI-based robust model predictive control and its application to an industrial CSTR problem. Journal of Process Control, 2001, 11(6): 649-659 doi: 10.1016/S0959-1524(00)00052-4 [42] Knapp T D, Budman H M, Broderick G. Adaptive control of a CSTR with a neural network model. Journal of Process Control, 2001, 11(1): 53-68 doi: 10.1016/S0959-1524(99)00065-7 [43] Yu D L, Chang K T, Yu D W. A stable self-learning PID control for multivariable time varying systems. Control Engineering Practice, 2007, 15(12): 1577-1587 doi: 10.1016/j.conengprac.2007.02.004 [44] Di Ciccio M P, Bottini M, Pepe P, Foscolo P U. Observer-based nonlinear control law for a continuous stirred tank reactor with recycle. Chemical Engineering Science, 2011, 66(20): 4780-4797 doi: 10.1016/j.ces.2011.06.038 [45] Hoang H, Couenne F, Jallut C, Le Gorrec Y. Lyapunov-based control of non isothermal continuous stirred tank reactors using irreversible thermodynamics. Journal of Process Control, 2012, 22(2): 412-422 doi: 10.1016/j.jprocont.2011.12.007 [46] Chai T Y, Qin S J, Wang H. Optimal operational control for complex industrial processes. Annual Reviews in Control, 2014, 38(1): 81-92 doi: 10.1016/j.arcontrol.2014.03.005 [47] 伍铁斌, 阳春华, 李勇刚, 朱红求, 桂卫华.基于模糊操作模式的砷盐除钴过程操作参数协同优化.自动化学报, 2014, 40(8): 1690-1698 http://www.aas.net.cn/CN/abstract/abstract18436.shtmlWu Tie-Bin, Yang Chun-Hua, Li Yong-Gang, Zhu Hong-Qiu, Gui Wei-Hua. Fuzzy operational-pattern based operating parameters collaborative optimization of cobalt removal process with arsenic salt. Acta Automatica Sinica, 2014, 40(8): 1690-1698 http://www.aas.net.cn/CN/abstract/abstract18436.shtml [48] Kim S I, Kim K E, Park E K, Song S W, Jung S. Estimation methods for efficiency of additive in removing impurity in hydrometallurgical purification process. Hydrometallurgy, 2007, 89(3-4): 242-252 doi: 10.1016/j.hydromet.2007.07.009 [49] Xie Y F, Xie S W, Li Y G, Yang C H, Gui W H. Dynamic modeling and optimal control of goethite process based on the rate-controlling step. Control Engineering Practice, 2017, 58: 54-65 doi: 10.1016/j.conengprac.2016.10.001 [50] Zhang B, Yang C H, Gui W H. Control strategy for hydrometallurgical removal process based on modelling and evaluation. IFAC-PapersOnLine, 2016, 49(20): 161-166 doi: 10.1016/j.ifacol.2016.10.114 [51] 柴天佑, 李少远, 王宏.网络信息模式下复杂工业过程建模与控制.自动化学报, 2013, 39(5): 469-470 http://www.aas.net.cn/CN/abstract/abstract17922.shtmlChai Tian-You, Li Shao-Yuan, Wang Hong. Modeling and control for complex industrial processes in networked information. Acta Automatica Sinica, 2013, 39(5): 469-470 http://www.aas.net.cn/CN/abstract/abstract17922.shtml [52] 刘强, 秦泗钊.过程工业大数据建模研究展望.自动化学报, 2016, 42(2): 161-171 http://www.aas.net.cn/CN/abstract/abstract18807.shtmlLiu Qiang, Qin S. Joe. Perspectives on big data modeling of process industries. Acta Automatica Sinica, 2016, 42(2): 161-171 http://www.aas.net.cn/CN/abstract/abstract18807.shtml [53] 桂卫华, 陈晓方, 阳春华, 谢永芳.知识自动化及工业应用.中国科学:信息科学, 2016, 46(8): 1016-1034 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201608006.htmGui Wei-Hua, Chen Xiao-Fang, Yang Chun-Hua, Xie Yong-Fang. Knowledge automation and its industrial application. Scientia Sinica: Informationis, 2016, 46(8): 1016-1034 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201608006.htm -
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