Uncertainty Optimization of Industrial Production Operations Considering the Stochastic Performance of Control Loops

被引:0
作者
Li, Ling [1 ,2 ]
Xiang, Junlin [1 ,2 ]
Liu, Shu [3 ]
Li, Jiaxin [1 ]
Long, Hangli [1 ,2 ]
Xue, Yongfei [4 ]
机构
[1] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha 410205, Peoples R China
[2] Changsha Univ Sci & Technol, State Key Lab Disaster Prevent & Reduct Power Grid, Changsha 410205, Peoples R China
[3] Hangzhou Zhiyuan Res Inst Co Ltd, Hangzhou 310013, Peoples R China
[4] Cent South Univ Forestry & Technol, Sch Elect Informat & Phys, Changsha 410004, Peoples R China
基金
中国国家自然科学基金;
关键词
operation optimization; distributionally robust optimization; control loops; stochastic performance; variational mode decomposition; DISTRIBUTIONALLY ROBUST OPTIMIZATION; PORTFOLIO SELECTION;
D O I
10.3390/pr13010113
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Process optimization is a highly successful method for achieving optimal efficiency in industrial production. The conventional optimization approach presupposes that the operational parameters should align with the optimization settings. However, it fails to consider that, influenced by the stochastic performance of the control loops, the operating parameters may deviate from the optimal operating settings. Consequently, this results in the violation of constraints in the optimization results and affects production safety. Therefore, this paper proposes an uncertainty optimization method that considers the stochastic performance of control loops to accurately determine the optimal operational performance that can be practically achieved in industrial production. Firstly, a multi-optimization variational mode decomposition strategy is developed to precisely extract the smooth random and trend terms of the control loop output data. Secondly, the random grouping smooths out the random terms and accurately characterizes the uncertainty associated with these terms. Subsequently, a moment uncertainty set with mild mean-zero net condition is then defined to construct an improved distribution robust optimization model considering the stochastic performance of control loops. Finally, the validation of the proposed optimization method in the actual hydrocracking process shows that the optimization error of the proposed method is reduced by more than 10%, and the constraint violation rate is reduced by 14%, which fully proves the effectiveness and applicability of the method.
引用
收藏
页数:20
相关论文
共 35 条
[1]   Optimal inequalities in probability theory: A convex optimization approach [J].
Bertsimas, D ;
Popescu, I .
SIAM JOURNAL ON OPTIMIZATION, 2005, 15 (03) :780-804
[2]   EXTRACTING QUALITATIVE DYNAMICS FROM EXPERIMENTAL-DATA [J].
BROOMHEAD, DS ;
KING, GP .
PHYSICA D, 1986, 20 (2-3) :217-236
[3]  
Ceria S, 2006, J ASSET MANAG, V7, P109, DOI 10.1057/palgrave.jam.2240207
[4]   A Distributionally Robust Optimization Model for Unit Commitment Based on Kullback-Leibler Divergence [J].
Chen, Yuwei ;
Guo, Qinglai ;
Sun, Hongbin ;
Li, Zhengshuo ;
Wu, Wenchuan ;
Li, Zihao .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2018, 33 (05) :5147-5160
[5]  
Cottle RichardW., 1974, LINEAR ALGEBRA APPL, V8, P189, DOI [DOI 10.1016/0024-3795(74)90066-4, 10.1016/0024-3795(74)90066-4]
[6]   Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems [J].
Delage, Erick ;
Ye, Yinyu .
OPERATIONS RESEARCH, 2010, 58 (03) :595-612
[7]   Variational Mode Decomposition [J].
Dragomiretskiy, Konstantin ;
Zosso, Dominique .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62 (03) :531-544
[8]   A multi-stage stochastic programming model for the unit commitment of conventional and virtual power plants bidding in the day-ahead and ancillary services markets [J].
Fusco, Andrea ;
Gioffre, Domenico ;
Castelli, Alessandro Francesco ;
Bovo, Cristian ;
Martelli, Emanuele .
APPLIED ENERGY, 2023, 336
[9]   Optimization of crude oil operations scheduling by applying a two-stage stochastic programming approach with risk management [J].
Garcia-Verdier, Tomas Garcia ;
Gutierrez, Gloria ;
Mendez, Carlos A. ;
Palacin, Carlos G. ;
de Prada, Cesar .
JOURNAL OF PROCESS CONTROL, 2024, 133
[10]   Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry [J].
Gholizadeh, Hadi ;
Fazlollahtabar, Hamed .
COMPUTERS & INDUSTRIAL ENGINEERING, 2020, 147