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Design of multi-loop control systems for distillation columns: review of past and recent mathematical tools

  • Changsoo Kim , Manas Shah and Ali M. Sahlodin EMAIL logo
Published/Copyright: March 26, 2021
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Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling Volume 17 Issue 2

Abstract

Design of a control structure in distillation columns involves selecting proper sets of manipulated and controlled variables (often including tray temperatures for inferential control of product compositions) and one-to-one pairing between the two sets. In this paper, various mathematical tools for achieving this goal are reviewed. First, traditional methods such as Singular Value Decomposition (SVD) and Relative Gain Array (RGA) that build upon a simplified steady-state or dynamic model of the column are explored. The role of optimization in systematizing the control design procedures is also investigated. Then, more recent inferential control techniques that rely on statistical methods such as Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), and other machine learning techniques such as Artificial Neural Networks (ANN) and Support Vector Machine Regression (SVMR) are discussed extensively. The discussions include newer distillation technologies with complex configurations such as dividing-wall columns. Finally, the use of process simulators in aiding the control structure design of distillation columns is surveyed.

Keywords: control structure selection; distillation column; inferential control; machine learning; multi-loop PID control
Corresponding author: Ali M. Sahlodin, Process Systems Engineering Laboratory, Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), P.O. Box: 15875-4413, Tehran, Iran, E-mail:

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2020-07-13
Accepted: 2021-02-24
Published Online: 2021-03-26

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Robustness study of the tricalcium phosphate synthesis by using Taguchi’s approach
  4. Sensitivity analysis and optimization of the utility consumption of natural gas liquids (NGLs) process in the Siri Island Gas
  5. On methods to reduce spurious currents within VOF solver frameworks. Part 1: a review of the static bubble/droplet
  6. Separation of HCl/water mixture using air gap membrane distillation, Taguchi optimization and artificial neural network
  7. Dynamic modeling of fouling over multiple biofuel production cycles in a membrane reactor
  8. Review
  9. Design of multi-loop control systems for distillation columns: review of past and recent mathematical tools
https://doi.org/10.1515/cppm-2020-0070
Keywords for this article
control structure selection; distillation column; inferential control; machine learning; multi-loop PID control

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Robustness study of the tricalcium phosphate synthesis by using Taguchi’s approach
  4. Sensitivity analysis and optimization of the utility consumption of natural gas liquids (NGLs) process in the Siri Island Gas
  5. On methods to reduce spurious currents within VOF solver frameworks. Part 1: a review of the static bubble/droplet
  6. Separation of HCl/water mixture using air gap membrane distillation, Taguchi optimization and artificial neural network
  7. Dynamic modeling of fouling over multiple biofuel production cycles in a membrane reactor
  8. Review
  9. Design of multi-loop control systems for distillation columns: review of past and recent mathematical tools
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