2.793

2018影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

间歇过程的批内自优化控制

叶凌箭

叶凌箭. 间歇过程的批内自优化控制. 自动化学报, 2020, 45(x): 1−11 doi: 10.16383/j.aas.c190855
引用本文: 叶凌箭. 间歇过程的批内自优化控制. 自动化学报, 2020, 45(x): 1−11 doi: 10.16383/j.aas.c190855
YE Ling-Jian. Within-Batch Self-optimizing Control For Batch Processes. Acta Automatica Sinica, 2020, 45(x): 1−11 doi: 10.16383/j.aas.c190855
Citation: YE Ling-Jian. Within-Batch Self-optimizing Control For Batch Processes. Acta Automatica Sinica, 2020, 45(x): 1−11 doi: 10.16383/j.aas.c190855

间歇过程的批内自优化控制

doi: 10.16383/j.aas.c190855
基金项目: 国家自然科学基金项目(61673349), 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), 宁波市自然科学基金(2018A610188)资助
详细信息
    作者简介:

    叶凌箭:浙大宁波理工学院信息学院教授. 2006年, 2011年分别获得浙江大学化工系学士, 控制系博士学位. 主要研究方向为控制结构设计, 不确定系统的实时优化. 本文通信作者. E-mail: lingjian.ye@gmail.com

Within-Batch Self-optimizing Control For Batch Processes

Funds: National Natural Science Foundation of China(61673349), Foundation of Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University, APCLI1802), Ningbo Natural Science Foundation (2018A610188)
  • 摘要: 针对间歇过程的实时优化问题, 提出了一种基于自优化控制的批内优化方法. 以测量变量的线性组合为被控变量, 在单批次内跟踪控制被控变量实现间歇过程的实时优化. 根据是否在间歇过程的不同阶段切换被控变量, 给出了两种自优化控制策略, 对每种策略又分别提出两种设定轨线选取方案. 为求解这些情形下的最优被控变量(组合矩阵), 以最小化平均经济损失为目标, 推导了组合矩阵和经济损失之间的函数关系, 分别将其描述为相应的非线性规划问题. 在此基础上, 进一步引入了扩张组合矩阵, 将这些非线性规划问题归纳为求解扩张组合矩阵的一致形式(扩张组合矩阵具有不同的结构约束), 并推导得到了其中一种方案的解析解计算方法. 以一个间歇反应器为研究对象, 验证了方法的有效性.
  • 图  1  间歇过程的离散化变量及自优化控制策略

    Fig.  1  Discretization of batch processes and self-optimizing control strategy

    图  2  最优扩张组合矩阵 $\small \bar H$ 的求解步骤

    Fig.  2  Procedure for solving the optimal extended combination matrix $\small \bar H$

    图  3  标称点的最优输入轨迹

    Fig.  3  Optimal input trajectory at the nominal point

    图  4  策略1方案1的设定值轨线

    Fig.  4  Setpoint trajectory for Strategy 1 (Scheme 1)

    图  5  批内自优化控制效果( $ k_1 $ : +20%, $ k_2 $ : -20%)

    Fig.  5  Within-batch self-optimizing performance ( $ k_1 $ : +20%, $ k_2 $ : -20%)

    图  6  批内自优化控制效果( $ k_1 $ : +40%, $ k_2 $ : -40%)

    Fig.  6  Within-batch self-optimizing performance ( $ k_1 $ : +40%, $ k_2 $ : -40%)

    表  1  间歇反应器参数及标称值

    Table  1  Parameters for the reactor model and nominal values

    符号 物理含义 标称值
    $ k_1 $ 主反应反应常数 0.053 L mol/min
    $ k_2 $ 副反应反应常数 0.128 L mol/min
    $ u_L $ $ u $ 下限 0 L/min
    $ u_U $ $ u $ 上限 0.001 L/min
    $ c_{Bin} $ B进料浓度 5 mol/L
    $ c_{Ao} $ A初始浓度 0.72 mol/L
    $ c_{Bo} $ B初始浓度 0.0614 mol/L
    $ V_o $ V初始值 1 L
    $ t_f $ 批次运行时间 250 min
    下载: 导出CSV

    表  2  损失函数 $ L_{\rm{av}} $

    Table  2  Loss function $ L_{\rm{av}} $

    方案 $ N = 2 $ $ N = 20 $
    策略1方案1 0.0371 0.0083
    策略1方案2 0.03423 0.0024
    策略2方案1 0.0368 0.0069
    策略2方案2 0.03420 0.0022
    下载: 导出CSV

    表  3  100组随机扰动下的非线性损失统计量

    Table  3  Statistics of nonlinear losses for 100 groups of random disturbances

    方案 平均损失 最大损失 标准差
    标称操作 0.0036 0.0227 0.0068
    控制 $ c_B $ 0.0042 0.0165 0.0054
    策略1方案1 0.0026 0.0167 0.0050
    策略1方案2 0.0007 0.0053 0.0016
    下载: 导出CSV
  • [1] Chachuat B, Srinivasan B, Bonvin D. Adaptation strategies for real-time optimization. Computers & Chemical Engineering, 2009, 33(10): 1557−1567
    [2] 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 doi: 10.3724/SP.J.1004.2009.00641

    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 doi: 10.3724/SP.J.1004.2009.00641
    [3] Engell S. Feedback control for optimal process operation. Journal of Process Control, 2007, 17(3): 203−219 doi: 10.1016/j.jprocont.2006.10.011
    [4] Chen C Y, Joseph B. On-line optimization using a two-phase approach: an application study. Industrial & Engineering Chemistry Research, 1987, 26(9): 1924−1930
    [5] Marlin T E, Hrymak A N. Real-time operations optimization of continuous processes. In: AIChE Symposium Series. 1997: New York, NY: American Institute of Chemical Engineers, 1987. 1971−2002
    [6] Marchetti A, Chachuat B, Bonvin D. Modifier-Adaptation Methodology for Real-Time Optimization. Industrial & Engineering Chemistry Research, 2009, 48(13): 6022−6033
    [7] Marchetti A G, Francois G, Faulwasser T, Bonvin D. Modifier adaptation for real-time optimization—methods and applications. Processes, 2016, 4(4): 55 doi: 10.3390/pr4040055
    [8] 代伟, 柴天佑. 数据驱动的复杂磨矿过程运行优化控制方法. 自动化学报, 2014, 40(9): 2005−2014

    Dai Wei, Chai Tian-You. Data-driven optimal operational control of complex grinding processes. Acta Automatica Sinica, 2014, 40(9): 2005−2014
    [9] 李金娜, 高溪泽, 柴天佑, 范家璐. 数据驱动的工业过程运行优化控制. 控制理论与应用, 2016, 33(12): 1584−1592 doi: 10.7641/CTA.2016.60455

    Li Jin-Na, Gao Xi-Zhe, Chai Tian-You. Data-driven operational optimization control of industrial processes. Control Theory & Applications, 2016, 33(12): 1584−1592 doi: 10.7641/CTA.2016.60455
    [10] 代伟, 陆文捷, 付俊, 马小平. 工业过程多速率分层运行优化控制. 自动化学报, 2019, 45(10): 1946−1959

    Dai Wei, Lu Wen-Jie, Fu Jun, Ma Xiao-Ping. Multi-rate Layered Optimal Operational Control of Industrial Processes. Acta Automatica Sinica, 2019, 45(10): 1946−1959
    [11] Skogestad S. Plantwide control: the search for the self-optimizing control structure. Journal of Process Control, 2000, 10(5): 487−507 doi: 10.1016/S0959-1524(00)00023-8
    [12] Ye L, Cao Y, Yuan X. Global approximation of self-optimizing controlled variables with average loss minimization. Industrial & Engineering Chemistry Research, 2015, 54(48): 12040−12053
    [13] Jaschke J, Cao Y, Kariwala V. Self-optimizing control–A survey. Annual Reviews in Control, 2017, 43: 199−223 doi: 10.1016/j.arcontrol.2017.03.001
    [14] Ye L, Miao A, Zhang H. Real-Time Optimization of Gold Cyanidation Leaching Process in a Two-Layer Control Architecture Integrating Self-Optimizing Control and Modifier Adaptation. Industrial & Engineering Chemistry Research, 2017, 56(14): 4002−4016
    [15] 叶凌箭, 关宏伟. 金氰化浸出过程的自优化控制. 控制与决策, 2017, 32(3): 481−486

    Ye Ling-Jian, Guan Hong-Wei. Self-optimizing control of gold cyanidation leaching process. Control and Decision, 2017, 32(3): 481−486
    [16] Ye L, Cao Y, Yuan X, Zhang H. Retrofit self-optimizing control: A step forward toward real implementation. IEEE Transactions On Industrial Electronics, 2017, 64(6): 4662−4670 doi: 10.1109/TIE.2017.2668991
    [17] Francisco M, Skogestad S, Vega P. Model predictive control for the self-optimized operation in wastewater treatment plants: Analysis of dynamic issues. Computers & Chemical Engineering, 2015, 82: 259−272
    [18] 卢静宜, 曹志兴, 高福荣. 批次过程控制—回顾与展望. 自动化学报, 2017, 43(6): 933−943

    Lu Jing-Yi, Cao Zhi-Xing, Gao Fu-Rong. Batch Process Control-Overview and Outlook. Acta Automatica Sinica, 2017, 43(6): 933−943
    [19] Lu J, Cao Z, Zhao C, Gao F. 110th Anniversary: An Overview on Learning-Based Model Predictive Control for Batch Processes. Industrial & Engineering Chemistry Research, 2019, 58(37): 17164−17173
    [20] 池荣虎, 侯忠生, 黄彪. 间歇过程最优迭代学习控制的发展: 从基于模型到数据驱动. 自动化学报, 2017, 43(6): 917−932

    CHI Rong-Hu, HOU Zhong-Sheng, HUANG Biao. Optimal Iterative Learning Control of Batch Processes: From Model-based to Data-driven. Acta Automatica Sinica, 2017, 43(6): 917−932
    [21] Lu J, Cao Z, Gao F. Multipoint iterative learning model predictive control. IEEE Transactions on Industrial Electronics, 2018, 66(8): 6230−6240
    [22] Lu J, Cao Z, Zhang R, Gao F. Nonlinear monotonically convergent iterative learning control for batch processes. IEEE Transactions on Industrial Electronics, 2017, 65(7): 5826−5836
    [23] Srinivasan B, Bonvin D, Visser V, Palanki S. Dynamic optimization of batch processes - Ⅱ. Role of measurements in handling uncertainty. Computers & Chemical Engineering, 2003, 27(1): 27−44
    [24] Cao Z, Gondhalekar R, Dassau E, Doyle F J. Extremum seeking control for personalized zone adaptation in model predictive control for type 1 diabetes. IEEE Transactions on Biomedical Engineering, 2017, 65(8): 1859−1870
    [25] Cao Z, Dürr H B, Ebenbauer C, Allgower F, Gao F. Iterative learning and extremum seeking for repetitive time-varying mappings. IEEE Transactions on Automatic Control, 2016, 62(7): 3339−3353
    [26] 史洪岩, 苑明哲, 王天然, 袁德成. 间歇过程动态优化方法综述. 信息与控制, 2012, 41(1): 75−82

    Shi Hong-yan, Yuan Ming-Zhe, Wang Tian-Ran, Yuan De-Cheng. A suvey on dynamic optimization methods of batch processes. Information and Control, 2012, 41(1): 75−82
    [27] Srinivasan B, Palanki S, Bonvin D. Dynamic optimization of batch processes - I. Characterization of the nominal solution. Computers & Chemical Engineering, 2003, 27(1): 1−26
    [28] Halvorsen I J, Skogestad S, Morud J C, Alstad V. Optimal selection of controlled variables. Industrial & Engineering Chemistry Research, 2003, 42(14): 3273−3284
    [29] Kariwala V. Optimal measurement combination for local self-optimizing control. Industrial & Engineering Chemistry Research, 2007, 46(11): 3629−3634
    [30] Alstad V, Skogestad S, Hori E S. Optimal measurement combinations as controlled variables. Journal of Process Control, 2009, 19(1): 138−148 doi: 10.1016/j.jprocont.2008.01.002
    [31] Alstad V, Skogestad S. Null space method for selecting optimal measurement combinations as controlled variables. Industrial & Engineering Chemistry Research, 2007, 46(3): 846−853
    [32] Francois G, Srinivasan B, Bonvin D. Use of measurements for enforcing the necessary conditions of optimality in the presence of constraints and uncertainty. Journal of Process Control, 2005, 15(6): 701−712 doi: 10.1016/j.jprocont.2004.11.006
    [33] 叶凌箭, 宋执环, 马修水. 间歇过程的批间自优化控制. 化工学报, 2015, 66(07): 2573−2580

    Ye Ling-Jian, Song Zhi-Huan, Ma Xiu-Shui. Batch-to-batch self-optimizing control for batch processes. CIESC Journal, 2015, 66(07): 2573−2580
    [34] Ye Ling-Jian, Guan Hong-Wei, Yuan Xiao-Feng, Ma Xiu-Shui. Run-to-run optimization of batch processes with self-optimizing control strategy. The Canadian Journal Of Chemical Engineering, 2017, 95(4): 724−736 doi: 10.1002/cjce.22692
    [35] Ye L, Skogestad S. Dynamic self-optimizing control for unconstrained batch processes. Computers & Chemical Engineering, 2018, 117: 451−468
    [36] Skogestad S. Control structure design for complete chemical plants. Computers & Chemical Engineering, 2004, 28(1−2): 219−234
    [37] Biegler L T, Zavala V M. Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization. Computers & Chemical Engineering, 2009, 33(3): 575−582
  • 加载中
计量
  • 文章访问数:  118
  • HTML全文浏览量:  47
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-12-17
  • 录用日期:  2020-06-11

目录

    /

    返回文章
    返回