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基于数据的湿法冶金全流程操作量优化设定补偿方法

李康 王福利 何大阔 贾润达

李康, 王福利, 何大阔, 贾润达. 基于数据的湿法冶金全流程操作量优化设定补偿方法. 自动化学报, 2017, 43(6): 1047-1055. doi: 10.16383/j.aas.2017.c170122
引用本文: 李康, 王福利, 何大阔, 贾润达. 基于数据的湿法冶金全流程操作量优化设定补偿方法. 自动化学报, 2017, 43(6): 1047-1055. doi: 10.16383/j.aas.2017.c170122
LI Kang, WANG Fu-Li, HE Da-Kuo, JIA Run-Da. A Data-based Compensation Method for Optimal Setting of Hydrometallurgical Process. ACTA AUTOMATICA SINICA, 2017, 43(6): 1047-1055. doi: 10.16383/j.aas.2017.c170122
Citation: LI Kang, WANG Fu-Li, HE Da-Kuo, JIA Run-Da. A Data-based Compensation Method for Optimal Setting of Hydrometallurgical Process. ACTA AUTOMATICA SINICA, 2017, 43(6): 1047-1055. doi: 10.16383/j.aas.2017.c170122

基于数据的湿法冶金全流程操作量优化设定补偿方法

doi: 10.16383/j.aas.2017.c170122
基金项目: 

流程工业综合自动化国家重点实验室基础科研业务费 2013ZCX02-04

国家自然科学基金 61533007

详细信息
    作者简介:

    王福利 东北大学教授.主要研究方向为复杂工业过程建模与优化, 故障诊断.E-mail:flwang@mail.neu.edu.cn

    何大阔 东北大学教授.主要研究方向为复杂工业过程建模, 控制和优化.E-mail:hedakuo@ise.neu.edu.cn

    贾润达 东北大学副教授.主要研究方向为复杂工业过程建模、控制和优化.E-mail:jiarunda@ise.neu.edu.cn

    通讯作者:

    李康 东北大学博士研究生.主要研究方向为复杂工业过程建模, 控制和优化.E-mail:likang@research.neu.edu.cn

A Data-based Compensation Method for Optimal Setting of Hydrometallurgical Process

Funds: 

State Key Laboratory of Synthetical Automation for Process Industries Fundamental Research Funds 2013ZCX02-04

National Natural Science Foundation of China 61533007

More Information
    Author Bio:

    Professor at the College of Information Science and Engineering, Northeastern University. His research interest covers modeling and optimization of complex system, and fault diagnosis

    Professor at Northeastern University. His research interest covers modeling, control and optimization in complex industrial system

    Associate professor at Northeastern University. His research interest covers modeling, control and optimization in complex industrial system

    Corresponding author: LI Kang Ph. D. candidate at the Northeastern University. His interest research covers modeling, control and optimization in complex industrial system. Corresponding author of this paper
  • 摘要: 湿法冶金过程具有反应机理复杂、工艺流程长、工序众多等特点,由于模型误差等因素,基于模型得到的生产过程最优工作点不是实际生产过程的最优工作点.如何保持湿法冶金生产流程运行在经济效益最优的状态成为生产优化控制的难点.本文提出了一种基于数据的湿法冶金过程操作量优化设定补偿方法.该方法在基于模型得到的最优工作点基础上,采用即时学习(Just-in-time learning,JITL)的思想,在当前工作点附近利用历史数据建立操作量补偿值和经济效益增量的相关模型,优化求解在当前工作点下,使经济效益增量最大化的操作量补偿值,施加到生产流程,并在新工作点进行迭代补偿.将所提出的方法仿真应用于某精炼厂的湿法冶金生产流程,仿真结果验证了所提出方法的有效性.
    1)  本文责任编委 苏宏业
  • 图  1  高硫金精矿生产流程工艺流程图

    Fig.  1  Flow chart of high-sulfur refractory gold concentrates process

    图  2  优化控制结构图

    Fig.  2  Optimization and control structure

    图  3  优化设定迭代补偿结构图

    Fig.  3  Iterative optimization setting compensation structure

    图  4  优化设定迭代补偿流程图

    Fig.  4  Flowchart of iterative optimization setting compensation

    图  5  基于机理模型优化补偿的经济效益

    Fig.  5  Economic beneflts of the process based on compensation and optimization to the mechanism model

    图  6  基于数据模型迭代补偿的经济效益

    Fig.  6  Economic beneflts of the process based on compensation and optimization to the data model

    图  7  基于SCFO的生产过程经济效益

    Fig.  7  Economic beneflts of the production process based on SCFO

    表  1  模型参数拟合结果

    Table  1  Model parameters fltting results

    组别kAukCN
    参数组14.862.94
    参数组24.952.91
    下载: 导出CSV

    表  2  模型参数取值

    Table  2  Model parameter values

    参数取值
    Qs10 160
    ds80
    Cw0.30
    D0, r35
    D0, Au0
    C0, CN0
    下载: 导出CSV

    表  3  优化结果

    Table  3  Optimization results

    变量名称机理模型最优值实际过程最优值
    J(¥/h)1670.452045.17
    q1, CN(kg/h)65.4976.92
    q2, CN(kg/h)67.1576.11
    q3, CN(kg/h)57.3464.25
    q4, CN(kg/h)52.6152.98
    q5, CN(kg/h)32.8521.13
    q6, CN(kg/h)30.1115.62
    qZn(kg/h)2.432.43
    下载: 导出CSV

    表  4  参数取值

    Table  4  Parameter values

    参数名称数值
    Const19.47
    σ0.05
    δ0.01
    下载: 导出CSV

    表  5  迭代补偿结果

    Table  5  Iterative compensation results

    迭代次数q1, CNq2, CNq3, CNq4, CNq5, CNq6, CNqZnyp
    13.473.522.411.02-3.58-5.750102.4
    22.422.232.120.34-2.94-3.530111.21
    32.311.951.24-0.21-3.05-2.58084.57
    42.120.250.780.17-1.24-1.93037.92
    50.240.110.28-0.52-0.21-0.19020.17
    下载: 导出CSV

    表  6  补偿后操作量和最优值比较

    Table  6  Comparison of the operation and the optimal values after compensation

    最优值补偿后
    q1, CN(kg/h)76.9276.05
    q2, CN(kg/h)76.1175.21
    q3, CN(kg/h)64.2564.17
    q4, CN(kg/h)52.9853.41
    q5, CN(kg/h)21.1321.83
    q6, CN(kg/h)15.6216.13
    qZn(kg/h)2.432.43
    下载: 导出CSV

    表  7  数据模型优化结果

    Table  7  Data model optimization results

    变量名称机理模型最优值实际过程最优值
    J(¥,/h)1594.272045.17
    q1,CN(kg/h)66.3876.92
    q2,CN(kg/h)69.1276.11
    q3,CN(kg/h)60.6964.25
    q4,CN(kg/h)53.4252.98
    q5,CN(kg/h)31.7821.13
    q6,CN(kg/h)30.2615.62
    qZn(kg/h)2.432.43
    下载: 导出CSV
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  • 收稿日期:  2017-03-08
  • 录用日期:  2017-05-11
  • 刊出日期:  2017-06-20

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