TY - JOUR
T1 - Local parameter identifiability of large-scale nonlinear models based on the output sensitivity covariance matrix
AU - Mendez Blanco, Carlos
AU - Özkan, Leyla
PY - 2021/6/1
Y1 - 2021/6/1
N2 - The use of first-principle models is motivated by the potential of detailed information available as well as their versatility. Therefore, it is important to keep these models up to date so the models represent accurate enough the processes at hand. However, most of these models are nonlinear with a large number of states and parameters but with a relatively low number of measured outputs. This lack of measurements hinders the possibility to estimate all of the parameters present in the model. In this work, parameter identifiability of large-scale nonlinear models is explored using the empirical output controllability covariance matrix approach. This empirical covariance matrix is used to extract the output sensitivity matrix of the model to assess parameter identifiability. The advantages of the proposed methods are discussed while different sensitivity indexes are evaluated to draw sound conclusions on the parameter ranking results. A large-scale reactive batch distillation process simulation is used as a demonstrator.
AB - The use of first-principle models is motivated by the potential of detailed information available as well as their versatility. Therefore, it is important to keep these models up to date so the models represent accurate enough the processes at hand. However, most of these models are nonlinear with a large number of states and parameters but with a relatively low number of measured outputs. This lack of measurements hinders the possibility to estimate all of the parameters present in the model. In this work, parameter identifiability of large-scale nonlinear models is explored using the empirical output controllability covariance matrix approach. This empirical covariance matrix is used to extract the output sensitivity matrix of the model to assess parameter identifiability. The advantages of the proposed methods are discussed while different sensitivity indexes are evaluated to draw sound conclusions on the parameter ranking results. A large-scale reactive batch distillation process simulation is used as a demonstrator.
KW - Empirical covariance matrix
KW - Empirical gramian
KW - Sensitivity analysis
KW - Identifiability
KW - Output controllability
UR - http://www.scopus.com/inward/record.url?scp=85117959007&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2021.08.277
DO - 10.1016/j.ifacol.2021.08.277
M3 - Conference article
SN - 2405-8963
VL - 54
SP - 415
EP - 420
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
IS - 3
T2 - 16th IFAC Symposium on Advanced Control of Chemical Processes
Y2 - 13 June 2021 through 16 June 2021
ER -