TY - GEN
T1 - State estimation of a chemical reactor process model - A comparative study
AU - Mansouri, Majdi
AU - Nounou, Hazem
AU - Nounou, Mohamed
PY - 2013
Y1 - 2013
N2 - Due to the challenges associated with measuring some of the key variables of chemical processes, state estimators are often used to overcome this problem. This paper deals with the problem of state estimation of a chemical process model representing a continuously stirred tank reactor (CSTR) using the Extended Kalman Filter (EKF), Particle Filter (PF), and recently developed Variational Bayesian Filter (VBF). The VBF has been recently proposed to solve the nonlinear estimation problem because it can be applied to large parameter spaces, has better convergence properties and relatively easy to implement. Here, a comparative study is conducted to compare the estimation performances of these three estimation techniques in estimating the two states (the concentration and temperature) of the CSTR process model. Simulation results show that the VBF has improved state estimation performance over both EKF and PF, and the PF shows improved state estimation performance over EKF.
AB - Due to the challenges associated with measuring some of the key variables of chemical processes, state estimators are often used to overcome this problem. This paper deals with the problem of state estimation of a chemical process model representing a continuously stirred tank reactor (CSTR) using the Extended Kalman Filter (EKF), Particle Filter (PF), and recently developed Variational Bayesian Filter (VBF). The VBF has been recently proposed to solve the nonlinear estimation problem because it can be applied to large parameter spaces, has better convergence properties and relatively easy to implement. Here, a comparative study is conducted to compare the estimation performances of these three estimation techniques in estimating the two states (the concentration and temperature) of the CSTR process model. Simulation results show that the VBF has improved state estimation performance over both EKF and PF, and the PF shows improved state estimation performance over EKF.
KW - Continuously stirred tank reactor
KW - Extended Kalman filter
KW - Parameter estimation
KW - Particle filter
KW - State estimation
KW - Variational Bayesian filter
UR - http://www.scopus.com/inward/record.url?scp=84883101594&partnerID=8YFLogxK
U2 - 10.1109/SSD.2013.6563998
DO - 10.1109/SSD.2013.6563998
M3 - Conference contribution
AN - SCOPUS:84883101594
SN - 9781467364584
T3 - 2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013
BT - 2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013
T2 - 2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013
Y2 - 18 March 2013 through 21 March 2013
ER -