化工学报 ›› 2020, Vol. 71 ›› Issue (3): 1122-1130.doi: 10.11949/0438-1157.20191467

• 过程系统工程 • 上一篇    下一篇

基于经济模型预测控制的金氰化浸出过程动态实时优化

关宏伟1(),叶凌箭2(),沈非凡2,顾德3,宋执环4   

  1. 1. 宁波财经学院机械与电气工程学院, 浙江 宁波 315175
    2. 浙江大学宁波理工学院, 浙江 宁波 315100
    3. 江南大学轻工过程先进控制教育部重点实验室,江苏 无锡 214122
    4. 浙江大学控制科学与工程学院, 浙江 杭州 310027
  • 收稿日期:2019-12-03 修回日期:2019-12-10 出版日期:2020-03-05 发布日期:2019-12-07
  • 通讯作者: 叶凌箭 E-mail:ghw_nit@126.com;lingjian.ye@gmail.com
  • 作者简介:关宏伟(1977—),男,博士,讲师,ghw_nit@126.com
  • 基金资助:
    国家自然科学基金项目(61673349);浙江省科协育才工程(2017YCGC014);浙江省自然科学基金项目(LQ19F030004);宁波市自然科学基金项目(2018A610188);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(APCLI1802)

Dynamic real-time optimization for gold cyanidation leaching process using economic model predictive control

Hongwei GUAN1(),Lingjian YE2(),Feifan SHEN2,De GU3,Zhihuan SONG4   

  1. 1. School of Mechanical and Electrical Engineering, Ningbo University of Finance and Economics, Ningbo 315175, Zhejiang, China
    2. Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, Zhejiang, China
    3. Key Laboratory of Advanced Control for Light Industry Processes of Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China
    4. School of Control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
  • Received:2019-12-03 Revised:2019-12-10 Online:2020-03-05 Published:2019-12-07
  • Contact: Lingjian YE E-mail:ghw_nit@126.com;lingjian.ye@gmail.com

摘要:

针对金氰化浸出过程时间常数大、不确定性强等问题,提出了一种基于经济模型预测控制(EMPC)的动态实时优化方法。不同于传统的模型预测控制,EMPC将经济指标直接作为滚动优化的目标函数,在每个采样时刻求解滚动窗口内的最优操作序列。和稳态优化方法相比,基于EMPC的方法能保证动态最优性,提高经济收益。此外,金氰化浸出过程受随机噪声、未知参数可变等不确定性影响,提出使用扩展卡尔曼滤波(EKF),通过构造增广系统对状态变量及不确定参数进行在线同步估计,加强EMPC的准确性和可靠性。仿真结果表明,提出的EMPC+EKF策略能有效提高金氰化浸出过程的经济性能。

关键词: 金氰化浸出过程, 动态优化, 经济模型预测控制, 不确定性, 卡尔曼滤波

Abstract:

Gold cyanidation leaching processes suffer from slow-dynamics and strong uncertainties. To this end, an economic model predictive control (EMPC) based dynamic real-time optimization scheme is proposed for the gold cyanidation leaching process. Unlike traditional model predictive control, EMCC takes economic indicators directly as the objective function of rolling optimization, and solves the optimal operation sequence within the rolling window at each sampling time. Compared to the static optimization, the EMPC-based scheme ensures dynamic optimality, which enables to obtain a better economic performance. To deal with measurement noise and unknown parameters, the extended Kalman filter (EKF) is employed to identify the states and parameters simultaneously by constructing an extended system, such that the precision and reliability of EMPC are enhanced. Simulation results show that the proposed EMPC+EKF strategy can effectively improve the economic performance of gold cyanidation leaching process.

Key words: gold cyanidation leaching process, dynamic optimization, economic model predictive control, uncertainty, Kalman filter

中图分类号: 

  • TP 273

图1

金氰化浸出过程"

表1

金氰化浸出过程模型参数"

参数 标称值 参数 标称值
C CN , 0 /(mg/kg) 200 d ˉ /μm 139
Cl, 0 /(mg/kg) 0 k 1 0.0011
C s , 0 /(mg/kg) 100 k 2 2.13
C o/(mg/kg) 7 k 3 0.961
C w/(kg/kg) 0.39 k 4 0.228
P Au/(CNY/mg) 0.226 k 5 3.6821×10-9
P CN/(CNY/mg) 0.000015 k 6 3.71
P CNd/(CNY/mg) 0.0000025 ρ s/(g/cm3) 2.8
Q s/(kg/h) 2500 ρl /(g/cm3) 1
V/m3 68

图2

直接配置法(n=3)"

图3

EKF状态变量估计效果(标称工况)"

图4

EKF状态及参数估计效果( θ =[0.0012 1.917 3.866×10-9]T)"

图5

操作输入Q CN的运行轨迹(标称工况)"

图6

成本函数J的轨迹(标称工况)"

图7

操作输入Q CN的运行轨迹(参数θ未知)"

图8

成本函数J的轨迹(参数θ未知)"

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