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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (5): 843-851    DOI: 10.3785/j.issn.1008-973X.2019.05.004
    
Gradient information-based strategy for real time optimization and control integration
Xiao-chen LI(),Hong-ye SU*(),Han-shan SHAO,Lei XIE
Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
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Abstract  

The hierarchy model of process system was analyzed, and the cascade structure of real time optimization and control integration was proposed, aiming at the optimal operation problem for industrial process. Gradient information-based steady state real time optimization approach was installed in the optimization layer. The setpoint was updated by collecting measurements online and estimating the gradient information of process. The proposed approach can effectively suppress the impact of plant model mismatch on optimization objective, since it avoided using an explicit process model. A least square thought was introduced to compute the gradient vector. The proposed algorithm not only had a low computational burden, but also can be applied to the steady state real time optimization of large-scale industrial processes. A method for selecting controlled variables of nonlinear process was discussed. The proposed method minimized the global average loss based on the nonlinear model. Reasonable assumptions were made for some conditions, so that the suboptimal solution was obtained, in order to solve the nonlinear programming problem efficiently. The analytical solution of controlled variables was given and the calculation efficiency was improved as well as the optimization layer was connected with the control layer. A numerical example, evaporation process and exothermic reaction process were studied to illustrate the effectiveness of the proposed method.

Key words： optimal operation      hierarchy model      cascade structure      gradient information      least square      controlled variables     
Received: 30 March 2018      Published: 17 May 2019
CLC:  TP 273  
Corresponding Authors: Hong-ye SU     E-mail: lixiaochen@zju.edu.cn;hysu@iipc.zju.edu.cn
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Xiao-chen LI
Hong-ye SU
Han-shan SHAO
Lei XIE
Cite this article:

Xiao-chen LI,Hong-ye SU,Han-shan SHAO,Lei XIE. Gradient information-based strategy for real time optimization and control integration. Journal of ZheJiang University (Engineering Science), 2019, 53(5): 843-851.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.05.004     OR     http://www.zjujournals.com/eng/Y2019/V53/I5/843


基于梯度信息的实时优化与控制集成策略

针对工业过程的最优操作问题，分析过程系统的层次模型，提出实时优化与控制集成的级联结构. 对于优化层采用基于梯度信息的稳态实时优化方法，通过在线采集过程测量值，估计过程的梯度信息，更新设定值. 不需要使用显式的过程模型，可以有效抑制模型失配对优化目标的影响. 利用最小二乘的思想求解梯度向量，降低计算成本，可应用于大规模工业过程的稳态实时优化. 提出非线性过程中被控变量的选取方法，利用非线性模型计算平均损失，优化效果具有全局性. 为了快速求解非线性规划问题，对某些条件进行合理假设，从而获得次优解，给出求解被控变量的解析方法，提高计算效率，同时将优化层与控制层联系起来. 通过对数值算例、蒸发过程和放热反应过程的研究，验证所提出方法的有效性.


关键词： 最优操作,  层次模型,  级联结构,  梯度信息,  最小二乘,  被控变量 
Fig.1 Hierarchy model of general process system
Fig.2 Cascade structure of real time optimization and control integration
Fig.3 Comparison for performance losses of proposed method and method proposed by reference [26]
Fig.4 Schematic diagram of evaporation process
变量名称 物理含义 标称值 单位
qm1 进料质量流量 9.469 kg/min
qm2 产物质量流量 1.334 kg/min
qm3 循环质量流量 24.721 kg/min
qm4 气相质量流量 8.135 kg/min
qm5 冷凝液质量流量 8.135 kg/min
x1 进料摩尔分数 5.0% ?
x2 产物摩尔分数 35.0% ?
1 进料温度 40.000 °C
2 产物温度 88.400 °C
3 气相温度 81.066 °C
L2 分离器液位 1.000 m
p2 操作压力 51.412 kPa
qm100 蒸汽质量流量 9.434 kg/min
100 蒸汽温度 151.520 °C
p100 蒸汽压力 400.000 kPa
Q100 热负荷 345.292 kW
qm200 冷却水质量流量 217.738 kg/min
200 冷却水入口温度 25.000 °C
201 冷却水出口温度 45.550 °C
Q200 冷却器热负荷 313.210 kW
Tab.1 Physical meanings and nominal values of variables in evaporation process
测量子集 本研究方法 文献[26]方法
Lav Lmax t/s Lav Lmax t/s
y1 4.33 34.28 0.185 9.92 56.72 0.412
y2 0.37 5.08 0.354 0.53 7.86 1.271
y3 0.28 3.93 0.783 0.46 6.24 3.464
y4 0.42 6.65 1.371 0.61 9.93 7.323
Tab.2 Calculation effect comparison of proposed method and method proposed by reference [26]
Fig.5 Reaction schematic diagram of continuous stirred tank reactor
变量名称 标称值 单位
co（A） 0.498 mol/L
co（B） 0.502 mol/L
To 426.803 K
ci（A） 1 mol/L
ci（B） 0 mol/L
Ti 424.292 K
τ 60 s
Tab.3 Nominal values of variables in CSTR model
Fig.6 Disturbance variables trajectories
Fig.7 Trajectories of variables measured through method proposed by reference [28]
Fig.8 Trajectories of variables measured through proposed method
Fig.9 Comparison of manipulate variables obtained through proposed method and method proposed by reference [28]
Fig.10 Comparison of objective functions obtained through proposed method and method proposed by reference [29]
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