有色冶金过程建模与优化的若干问题及挑战

桂卫华; 阳春华; 陈晓方; 王雅琳

doi:10.3724/SP.J.1004.2013.00197

有色冶金过程建模与优化的若干问题及挑战

doi: 10.3724/SP.J.1004.2013.00197

1.
中南大学信息科学与工程学院长沙 410083

详细信息

通讯作者:
阳春华

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- 被引次数: 0
出版历程
- 收稿日期: 2012-11-07
- 修回日期: 2012-11-08
- 刊出日期: 2013-03-20

Modeling and Optimization Problems and Challenges Arising in Nonferrous Metallurgical Processes

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

摘要

摘要: 有色金属工业发展正面临资源、能源与环境的严重制约, 而有色冶金过程建模与优化是实现有色冶金生产节能降耗减排的关键技术之一. 论文从有色冶金过程的特点出发,首先探讨了有色冶金过程的机理建模、连续搅拌釜式反应器(Continuous stirred tank reactor, CSTR)模型和智能集成建模的理论与方法,提出了智能集成建模的描述方法, 归纳了模型的集成形式,给出了工业应用上的几类智能集成模型; 然后围绕有色冶金过程工程优化,讨论了操作模式优化、软约束调整满意优化、多目标智能优化等方法,并阐述了大型湿法炼锌电解过程的综合优化控制技术; 最后探讨了有色冶金过程建模与优化所面临的新挑战.
- 有色冶金过程 /
- 智能集成建模 /
- 工程优化 /
- 操作模式 /
- 锌电解
Abstract: Challenges in current development of nonferrous metallurgical industry include resource shortage, energy crisis and environmental pollution. As one of the key techniques, methods of modeling and optimization are extensively used to save energy, reduce consumption and emissions in the nonferrous metallurgical processes. In this paper, firstly, the modeling problem for nonferrous metallurgical process is considered. Based on the characteristics of the nonferrous metallurgical processes, several methods and theories for the modeling of nonferrous metallurgical processes, including the mechanism-based, continuous stirred tank reactor (CSTR)-based, and intelligent integrated modeling methods, are investigated. We focus on the description method in intelligent integrated modeling and its integration structures, and give some types of intelligent integrated models in various industrial applications. Secondly, the engineering optimization problem arising in nonferrous metallurgical process is considered. Some engineering optimization methods, including operational-pattern optimization, satisfactory optimization with soft constraints adjustment, multi-objective intelligent optimization methods, and a comprehensive optimal control technique for a large-scale zinc electrolysis process are illustrated. In the end, some new challenges in process modeling and optimization are discussed.
- Nonferrous metallurgy process /
- intelligent integrated modeling /
- engineering optimization /
- operational-pattern /
- zinc electrolysis

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参考文献(1)

[1]

The "Twelfth Five-Year" Development Plan of Nonferrous Industry. Ministry of Industry and Information Technology, December 4, 2011 (有色金属工业"十二五"发展规划. 工业与信息化部, 2011年12月4日)[2] Gui Wei-Hua, Yang Chun-Hua. Intelligent Modeling, Control and Optimization of Complex Nonferrous Metallurgical Process. Beijing: Science Press, 2010(桂卫华, 阳春华. 复杂有色冶金生产过程智能建模、控制与优化. 北京: 科学出版社, 2010)[3] Hodouin D. Methods for automatic control, observation, and optimization in mineral processing plants. Journal of Process Control, 2011, 21(2): 211-225[4] Komulainen T, Pekkala P, Rantala A, Jms-Jounela S L. Dynamic modelling of an industrial copper solvent extraction process. Hydrometallurgy, 2006, 81(1): 52-61[5] Stadler S, Eksteen J J, Aldrich C. Physical modelling of slag foaming in two-phase and three-phase systems in the churn-flow regime. Minerals Engineering, 2006, 19(3): 237-245[6] Wang X L, Yang C H, Gui W H, Wang Y L. Wet grindability of an industrial ore and its breakage parameters estimation using population balances. International Journal of Mineral Processing, 2011, 98(1-2): 113-117[7] Gui W H, Wang Y L, Yang C H. Composition-prediction-model-based intelligent optimisation for lead-zinc sintering blending process. Measurement and Control, 2007, 40(6): 176-181[8] Qiu Zhu-Xian. Nonferrous Metals Metallurgy. Beijing: Metallurgical Industry Press, 1988 (邱竹贤. 有色金属冶金学. 北京: 冶金工业出版社, 1988)[9] 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)[10] Hoang H, Couenne F, Jallut C, Le Gorrec Y. The port Hamiltonian approach to modeling and control of continuous stirred tank reactors. Journal of Process Control, 2011, 21(10): 1449-1458[11] Sláva J, Švandová Z, Markoš J. Modelling of reactive separations including fast chemical reactions in CSTR. Chemical Engineering Journal, 2008, 139(3): 517-522[12] Takinoue M, Ma Y, Mori Y, Yoshikawa K. Extended continuous-flow stirred-tank reactor (ECSTR) as a simple model of life under thermodynamically open conditions. Chemical Physics Letters, 2009, 476(4-6): 323-328[13] Wang X L, Yang C H, Gui W H, Young B R, Chen X D. CSTR-based modelling for the continuous carbonation of sodium aluminate solution. The Canadian Journal of Chemical Engineering, 2011, 89(3): 617-624[14] Wang L Y, Gui W H, Teo K L, Loxton R, Yang C H. Time delayed optimal control problems with multiple characteristic time points: computation and industrial applications. Journal of Industrial and Management Optimization, 2009, 5(4): 705-718[15] Araromi D O, Afolabi T J, Aloko D. Neural network control of CSTR for reversible reaction using reverence model approach. Leonardo Journal of Sciences, 2007, 10(1-6): 25-40[16] Attaran S M, Abdullah S S B. Modeling of non isothermal CSTR with the method of RBF NN. In: Proceedings of the 2011 International Conference on Modeling, Simulation and Applied Optimization (ICMSAO). Kuala Lumpur, USA: IEEE, 2011. 1-6[17] 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[18] Babuška R, Verbruggen H B, van Can H J L. Fuzzy modeling of enzymatic penicillin-G conversion. Engineering Applications of Artificial Intelligence, 1999, 12(1): 79-92[19] Liau L C K, Yang T C K, Tsai M T. Expert system of a crude oil distillation unit for process optimization using neural networks. Expert Systems with Applications, 2004, 26(2): 247-255[20] Yang C H, Deconinck G, Gui W H, Li Y G. An optimal power-dispatching system using neural networks for the electrochemical process of zinc depending on varying prices of electricity. IEEE Transactions on Neural Networks, 2002, 13(1): 229-236[21] Chai T Y, Zhai L F, Yue H. Multiple models and neural networks based decoupling control of ball mill coal-pulverizing systems. Journal of Process Control, 2011, 21(3): 351-366[22] Qiao J H, Chai T Y. Soft measurement model and its application in raw meal calcination process. Journal of Process Control, 2012, 22(1): 344-351[23] Zhang S N, Wang F L, He D K, Jia R D. Real-time product quality control for batch processes based on stacked least-squares support vector regression models. Computers and Chemical Engineering, 2012, 36: 217-226[24] Zhao C H, Wang F L, Lu N Y, Jia M X. Stage-based soft-transition multiple PCA modeling and on-line monitoring strategy for batch processes. Journal of Process Control, 2007, 17(9): 728-741[25] Chang Yu-Qing, Wang Xiao-Gang, Wang Fu-Li. Multi neural network method for soft sensing and its application. Journal of Northeastern University, 2005, 26(6): 519-522 (常玉清, 王小刚, 王福利. 基于多神经网络模型的软测量方法及应用. 东北大学学报, 2005, 26(6): 519-522)[26] Fu Y, Chai T Y. Nonlinear multivariable adaptive control using multiple models and neural networks. Automatica, 2007, 43(6): 1101-1110[27] Yang Chun-Hua, Xie Ming, Gui Wei-Hua, Peng Xiao-Bo. A prediction model for matte grade in copper flash smelting process. Information and Control, 2008, 37(1): 28-33 (阳春华, 谢明, 桂卫华, 彭晓波. 铜闪速熔炼过程冰铜品位预测模型的研究与应用. 信息与控制, 2008, 37(1): 28-33)[28] Gui W H, Wang L Y, Yang C H, Xie Y F, Peng X B. Intelligent prediction model of matte grade in copper flash smelting process. Transactions of Nonferrous Metals Society of China, 2007, 17(5): 1075-1081[29] Yan Ai-Jun, Chai Tian-You, Yue Heng. Multivariable intelligent optimizing control approach for shaft furnace roasting process. Acta Automatica Sinica, 2006, 32(4): 636-640 (严爱军, 柴天佑, 岳恒. 竖炉焙烧过程的多变量智能优化控制. 自动化学报, 2006, 32(4): 636-640)[30] Wang Ya-Lin, Gui Wei-Hua, Yang Chun-Hua, Xie Yong-Fang, Song Hai-Ying. Intelligent integrated modeling for the dynamic copper-converting process based on limited data information. Control Theory and Applications, 2009, 26(8): 860-866 (王雅琳, 桂卫华, 阳春华, 谢永芳, 宋海鹰. 基于有限信息的铜吹炼动态过程智能集成建模. 控制理论与应用, 2009, 26(8): 860-866)[31] Zhang Shu-Ning, Wang Fu-Li, You Fu-Qiang, He Da-Kuo. On the hybrid modeling method of cobalt oxalate grain size distribution in hydrometallurgy synthesis process. Journal of Northeastern University (Natural Science), 2010, 31(1): 8-11 (张淑宁, 王福利, 尤富强, 何大阔. 湿法冶金草酸钴粒度分布混合建模方法. 东北大学学报(自然科学版), 2010, 31(1): 8-11)[32] 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 and Chemical Engineering, 2009, 33(7): 1289-1297[33] Peng Xiao-Bo, Gui Wei-Hua, Li Yong-Gang, Wang Ling Yun, Chen Yong. Copper flash smelting parameter soft sensor based on dynamic T-S recurrent fuzzy neural network. Chinese Journal of Scientific Instrument, 2008, 29(10): 2029-2033 (彭晓波, 桂卫华, 李勇刚, 王凌云, 陈勇. 基于动态T-S递归模糊神经网络的闪速熔炼过程参数软测量. 仪器仪表学报, 2008, 29(10): 2029-2033)[34] Zhou X J, Yang C H, Gui W H. State transition algorithm. Journal of Industrial and Management Optimization, 2012, 8(4): 1039-1056[35] Chai Q Q, Yang C H, Teo K L, Gui W H. Optimal control of an industrial-scale evaporation process: sodium aluminate solution. Control Engineering Practice, 2012, 20(6): 618-628[36] Nascimento C A O, Giudici R, Guardani R. Neural network based approach for optimization of industrial chemical processes. Computers and Chemical Engineering, 2002, 24(9-10): 2303-2314[37] Liu P, Su J H, Dong Q M, Li H J. Optimization of aging treatment in lead frame copper alloy by intelligent technique. Materials Letters, 2005, 59(26): 3337-3342[38] Chen X F, Gui W H, Wang Y L, Cen L H. Multi-step optimal control of complex process: a genetic programming strategy and its application. Engineering Applications of Artificial Intelligence, 2004, 17(5): 491-500[39] Wu Yong-Jian, Zhang Li, Yue Heng, Chai Tian-You. Intelligent optimal control based on CBR for fused magnesia production. Journal of Chemical Industry and Engineering (China), 2008, 59(7): 1686-1690 (吴永建, 张莉, 岳恒, 柴天佑. 基于案例推理的电熔镁炉智能优化控制. 化工学报, 2008, 59(7): 1686-1690)[40] Geng Zeng-Xian, Chai Tian-You. Intelligently optimal index setting for flotation process by CBR. Journal of Northeastern University (Natural Science), 2008, 29(6): 761-764 (耿增显, 柴天佑. 基于案例推理的浮选过程智能优化设定. 东北大学学报(自然科学版), 2008, 29(6): 761-764)[41] 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[42] 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[43] Wang Z J, Wu Q D, Chai T Y. Optimal-setting control for complicated industrial processes and its application study. Control Engineering Practice, 2004, 12(1): 65-74[44] Chai Tian-You. Challenges of optimal control for plant-wide production processes in terms of control and optimization theories. Acta Automatica Sinica, 2009, 35(6): 641-649 (柴天佑. 生产制造全流程优化控制对控制与优化理论方法的挑战. 自动化学报, 2009, 35(6): 641-649)[45] Yang Chun-Hua, Wang Xiao-Li, Tao Jie, Gui Wei-Hua, Wang Ya-Lin. Modeling and intelligent optimization algorithm for burden process of copper flash smelting. Journal of System Simulation, 2008, 20(8): 2152-2155 (阳春华, 王晓丽, 陶杰, 桂卫华, 王雅琳. 铜闪速熔炼配料过程建模与智能优化方法研究. 系统仿真学报, 2008, 20(8): 2152-2155)[46] Yang C H, Gui W H, Kong L S, Wang Y L. A two-stage intelligent optimization system for the raw slurry preparing process of alumina sintering production. Engineering Applications of Artificial Intelligence, 2009, 22(4-5): 786-795

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