TY - GEN
T1 - Integration of Optimal Experimental Design and Process Optimisation to An Enzyme Reaction System
AU - Yu, Hui
AU - Guo, Jielong
AU - Wei, Xian
AU - Liang, Peidong
AU - Zhao, Lijun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - 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.
AB - 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.
KW - enzyme reaction system.
KW - multi-objective design
KW - optimal experimental design (OED)
KW - parameter estimation
KW - process optimisation
UR - https://www.scopus.com/pages/publications/85080057729
U2 - 10.1109/CAC48633.2019.8997445
DO - 10.1109/CAC48633.2019.8997445
M3 - 会议稿件
AN - SCOPUS:85080057729
T3 - Proceedings - 2019 Chinese Automation Congress, CAC 2019
SP - 3615
EP - 3620
BT - Proceedings - 2019 Chinese Automation Congress, CAC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Chinese Automation Congress, CAC 2019
Y2 - 22 November 2019 through 24 November 2019
ER -