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

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.

Automated summary

This paper reviews various mathematical tools for designing control structures in distillation columns, including traditional methods such as SVD and RGA, as well as newer inferential control techniques like PCR and PLSR that rely on statistical methods and machine learning techniques. The discussions also cover complex distillation technologies such as dividing-wall columns, as well as the use of process simulators in aiding control structure design.