Integration of Optimal Experimental Design and Process Optimisation to An Enzyme Reaction System

被引：0
|
作者：
Yu, Hui [1 ]
Guo, Jielong [1 ]
Wei, Xian [1 ]
Liang, Peidong [2 ]
Zhao, Lijun [3 ]
机构：
[1] Chinese Acad Sci, Fujian Inst Res Struct Matter, Quanzhou, Peoples R China
[2] Harbin Inst Technol, Fujian Quanzhou HIT Res Inst Engn & Technol, Quanzhou, Peoples R China
[3] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin, Peoples R China
来源：
2019 CHINESE AUTOMATION CONGRESS (CAC2019) | 2019年
关键词：
optimal experimental design (OED); process optimisation; multi-objective design; parameter estimation; enzyme reaction system; MODEL-BASED DESIGN; IDENTIFICATION;
D O I：
10.1109/cac48633.2019.8997445
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A multi-objective experimental design strategy is proposed for a kinetically controlled synthesis process system by considering both the parameter estimation and the desired production yield. The input design is focused on maximising the desired production yield while the observation design is aimed at minimising the parameter estimation uncertainties. This multi-objective experimental design strategy is compared with the integrated experimental design method in which all design factors are optimised to improve parameter estimation quality. The optimal experimental design (OED) problem for observation design is relaxed to a semi-definite programming problem which is solved via interior-point method. Numerical studies demonstrate the efficiency of the proposed OED strategy as well as keeping high production yield. Parameter estimation uncertainties have been reduced significantly and they are near to those from integrated experimental design. The main point of this work is to introduce the idea of combining process optimisation with system identification so that the proposed model is closer to the real operation conditions in chemical processes.
引用
收藏
页码：3615 / 3620
页数：6
相关论文
共 50 条
  • [31] Software supporting optimal experimental design: A case study of binary diffusion using EFCOSS
    Rasch, Arno
    Buecker, H. Martin
    Bardow, Andre
    COMPUTERS & CHEMICAL ENGINEERING, 2009, 33 (04) : 838 - 849
  • [32] Optimal experimental design for ill-posed problems
    Bardow, Andre
    16TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING AND 9TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING, 2006, 21 : 173 - 178
  • [33] Bayesian Optimal Experimental Design Using Asymptotic Approximations
    Argyris, Costas
    Papadimitriou, Costas
    MODEL VALIDATION AND UNCERTAINTY QUANTIFICATION, VOL 3, 2017, : 273 - 275
  • [34] A Two-loop Optimization Strategy for Multi-objective Optimal Experimental Design
    Yu, Hui
    Yue, Hong
    Halling, Peter
    IFAC PAPERSONLINE, 2016, 49 (07): : 803 - 808
  • [35] Optimal Experimental Design for a Bistable Gene Regulatory Network
    Braniff, Nathan
    Richards, Addison
    Ingalls, Brian
    IFAC PAPERSONLINE, 2019, 52 (26): : 255 - 261
  • [36] Deep reinforcement learning for optimal experimental design in biology
    Treloar, Neythen J.
    Braniff, Nathan
    Ingalls, Brian
    Barnes, Chris P.
    PLOS COMPUTATIONAL BIOLOGY, 2022, 18 (11)
  • [37] Optimal Bayesian experimental design for subsurface flow problems
    Tarakanov, Alexadner
    Elsheikh, Ahmed H.
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2020, 370
  • [38] The use of optimal experimental design concepts in bioprocess modelling
    Bernaerts, K
    Smets, I
    Gysemans, K
    Cappuyns, A
    Van Impe, JF
    FOODSIM '2004, 2004, : 69 - 74
  • [39] Optimal experimental design and some related control problems
    Pronzato, Luc
    AUTOMATICA, 2008, 44 (02) : 303 - 325
  • [40] Optimal experimental design based on global sensitivity analysis
    Rodriguez-Fernandez, Maria
    Kucherenko, Sergei
    Pantelides, Costas
    Shah, Nilay
    17TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, 2007, 24 : 63 - 68
12345