Cited by 239Control Systems and IdentificationFault Detection and Control SystemsAdvanced Control Systems Optimization

Review and classification of recent observers applied in chemical process systems

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Mohd Ali, Jarinah, Ha Hoang, N., Hussain, M.A., Dochain, Denis·2015·Computers & Chemical Engineering

Abstract

Observers are computational algorithms designed to estimate unmeasured state variables due to the lack of appropriate estimating devices or to replace high-priced sensors in a plant. It is always important to estimate those states prior to developing state feedback laws for control and to prevent process disruptions, process shutdowns and even process failures. The diversity of state estimation techniques resulting from intrinsic differences in chemical process systems makes it difficult to select the proper technique from a theoretical or practical point of view for design and implementation in specific applications. Hence, in this paper, we review the applications of recent observers to chemical process systems and classify them into six classes, which differentiate them with respect to their features and assists in the design of observers. Furthermore, we provide guidelines in designing and choosing the observers for particular applications, and we discuss the future directions for these observers.

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Crossref · 10.1016/j.compchemeng.2015.01.019

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  • Luenberger-based observers are well-suited for infinite-dimensional systems when the system parameters are perfectly known.

    “Luenberger-based observers are well-suited for infinite-dimensional systems when the system parameters are perfectly known.[27]”

    From Optimal control of axial dispersion tubular reactors with recycle: Addressing state‐delay through transport <scp>PDEs</scp> (2025) · cites this paper as [27]

    Section 3.3 says Luenberger-based observers are appropriate when system parameters are well known, and the conclusions specify perfect knowledge of those parameters. But Section 2 discusses extension to infinite-dimensional systems for asymptotic, exponential, and interval observers, not Luenberger-based observers; it describes the latter as suitable for less complex linear systems. Page numbers are not available in our copy. (LLM, conf=0.87)

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3 of the 215 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

  1. Chen Z. Bayesian filtering: from Kalman filters to particle filters, and beyond. Statis- tics 2003;182:1-69.
  2. D'Attellis CE, Garcia RA, Mancilla Aguilar JL. Nonlinear observers. Nonlinear analysis: theory. Methods Appl 1997;30:3891-6.
  3. Daum F. Nonlinear filters: beyond the Kalman filter. Aerospace and electronic sys- tems magazine. IEEE 2005;20:57-69.

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Authors
Mohd Ali, J., Ha Hoang, N., Hussain, M. A., & Dochain, D.
Affiliations
  • University of Malaya (MY)
  • UCLouvain (BE)
Corresponding author
Hussain, M.A.
Venue
Computers & Chemical Engineering
Publisher
Elsevier BV
Published
2015-05
Volume
76
Pages
27-41
ISSN
0098-1354 (ISSN-L)
OpenAlex
W2034764524
Cited by
239
References
215
Citations by year
2026: 6 · 2025: 11 · 2024: 13 · 2023: 15 · 2022: 20 · 2021: 31 · 2020: 24 · 2019: 44 · 2018: 27 · 2017: 24 · 2016: 20 · 2015: 4
FWCI
23.14
Type
journal article
Language
en
Open access
Locations
  • Computers & Chemical Engineering (publishedVersion, cc-by-nc-nd)
  • The University of Malaya Research Repository (University of Malaya) (acceptedVersion)
  • Digital Access to Libraries (submittedVersion)
Funders
  • University of Malaya
  • Ministry of Higher Education in Malaysia
  • Viet Nam National Foundation for Science and Technology Development (NAFOSTED) (104.99-2014.74)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
Keywords
state observer, state estimation, chemical process systems, observer design, sensor replacement, state feedback control, process fault detection

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