State estimation of a chemical reactor process model - A comparative study

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13 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013
DOIs
Publication statusPublished - 2013
Event2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013 - Hammamet, Tunisia
Duration: 18 Mar 201321 Mar 2013

Other

Other2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013
CountryTunisia
CityHammamet
Period18/3/1321/3/13

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Keywords

  • Continuously stirred tank reactor
  • Extended Kalman filter
  • Parameter estimation
  • Particle filter
  • State estimation
  • Variational Bayesian filter

ASJC Scopus subject areas

  • Signal Processing

Cite this

Mansouri, M., Nounou, H., & Nounou, M. (2013). State estimation of a chemical reactor process model - A comparative study. In 2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013 [6563998] https://doi.org/10.1109/SSD.2013.6563998