Fuzzy Operational-pattern Based Operating Parameters Collaborative Optimization of Cobalt Removal Process with Arsenic Salt
-
摘要: 针对湿法炼锌砷盐除钴过程工况变化频繁和操作参数之间具有强耦合关系,导致操作参数优化困难的问题,提出了一种基于模糊操作模式的操作参数协同优化方法.根据大量的砷盐除钴工业运行数据,提炼初始操作模式库,根据入口工况参数,采用模糊匹配方法检索出相似操作模式,在操作模式重用时综合考虑系统参数缓慢变化和资源消耗的特点,然后采用灰色模糊最小二乘支持向量机(Least squares support vector machine,LSSVM)评估操作模式重用后的操作参数的可行性,并根据评估结果采用模糊专家规则修正操作参数.在工况发生变化时,系统能自动优化设定操作参数.工业验证结果表明,本文提出的操作参数协同优化方法保证了生产稳定,可有效提高净化后溶液中钴离子浓度的合格率和降低锌粉的消耗.Abstract: In the process of cobalt removal with arsenic salt in zinc hydrometallurgy, since the work conditions change frequently and the operating parameters couple strongly, it is difficult to optimize the operating parameters. To solve the problem, a method of operating parameters collaborative optimization based on fuzzy operational-pattern is proposed. The initial operational-pattern library is abstracted from the abundant industrial operating data of the cobalt removal process. A fuzzy matching method is adopted to search for similar operational-patterns according to the entrance working parameters. When the operational-pattern is reused, the slow change of system parameters and the characteristics of energy consumption should be considered comprehensively. Then the gray fuzzy least squares support vector machine (LSSVM) is used to evaluate the feasibility of operation parameter after reusing, and the operating parameters will be modified by fuzzy expert rules according to the evaluation result. When the working conditions change, the system can optimize and set the operating parameters automatically. The industrial test results show that the proposed operating parameters collaborative optimization method can ensure a stable production process running increase the qualified rate of outlet cobalt concentration, and help reduce zinc consumption effectively.
-
Key words:
- Operational-pattern /
- operating parameters /
- collaborative optimization /
- fuzzy /
- evaluation
-
[1] Bckman O, stvold T. Products formed during cobalt cementation on zinc in zinc sulfate electrolytes. Hydrometallurgy, 2000, 54(2-3): 65-78 [2] [2] 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 [3] [3] Yamashita S, Okubo M, Goto S, Hata K. Purification of zinc leaching solutionmechanism of removal of cobalt by zinc dust with arsenious oxide and copper ion. Metallurgical Review of MMIJ, 1997, 14(1): 37-52 [4] [4] Jri N. Statistical analysis of cobalt removal from zinc electrolyte using the arsenic-activated process. Hydrometallurgy, 2004, 73(1-2): 123-132 [5] [5] 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 [6] Gui 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 (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) [7] Zhang Jia-Yan, Ma Zhong-Hai, Qian Xiao-Bin, Li Shao-Ming, Lang Jia-Hong. Application of optimal control strategy to converter gas recovery system. Acta Automatica Sinica, 2012, 38(6): 1017-1024 (章家岩, 马中海, 钱晓斌, 李绍铭, 郎佳红. 转炉煤气回收系统优化控制策略应用. 自动化学报, 2012, 38(6): 1017-1024) [8] [8] Yan A J, Chai T Y, Yu W, Xu Z. Multi-objective evaluation-based hybrid intelligent control optimization for shaft furnace roasting process. Control Engineering Practice, 2012, 20(9): 857-868 [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] Fugleberg S, Jarvinen A, Yllo E. Recent development in solution purification at Outokumpu Zinc Plant, Kokkola. World Zinc, 1993, 93(1): 241-247 [11] Khanal S K, Huang J C. ORP-based oxygenation for sulfide control in anaerobic treatment of high-sulfate wastewater. Water Research, 2003, 37(9): 2053-2062 [12] Yang Chun-Hua, Zhu Hong-Qiu, Gui Wei-Hua, Wu Tie-Bin, Zhang Quan-Du, Wei Wen-Wu, Hu Zhi-Kun, Li Yong-Gang, Sun Bei, Lin Tian-Shui. Spent Acid Dosage Control in Cobalt Removal with Arsenic in Zinc Hydrometallurgy, CHN. Patent 201210204301, October 2012 (阳春华, 朱红求, 桂卫华, 伍铁斌, 张权度, 魏文武, 胡志坤, 李勇刚, 孙备, 林天水. 一种锌湿法冶炼砷盐净化除钴过程废酸添加控制方法, 中国, 201210204301, 2012年10月) [13] de M [14] ntaras R L, Bridge D, Mcsherry D. Case-based reasoning: an overview. AI Communications, 1997, 10(1): 21-29 [15] Lin C F, Wang S D. Fuzzy support vector machines. IEEE Transactions on Neural Networks, 2002, 13(2): 464-471 [16] Wu Tie-Bin, Yang Chun-Hua, Sun Bei, Zhu Hong-Qiu, Li Yong-Gang. Grey fuzzy-LSSVM forecasting model and its application in cobalt removal from zinc electrolyte. The Chinese Journal of Nonferrous Metals, 2012, 22(8): 2382-2386 (伍铁斌, 阳春华, 孙备, 朱红球, 李勇刚. 灰色模糊LSSVM预测模型在锌净化除钴中的应用. 中国有色金属学报, 2012, 22(8): 2382-2386)
点击查看大图
计量
- 文章访问数: 1934
- HTML全文浏览量: 99
- PDF下载量: 908
- 被引次数: 0