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Offset‐Free Inferential Feedback Control of Distillation Compositions Based on PCR and PLS Models
Author(s) -
Zhang J.
Publication year - 2006
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.200500259
Subject(s) - tray , fractionating column , offset (computer science) , temperature control , software , process control , principal component analysis , computer science , partial least squares regression , process (computing) , distillation , batch distillation , control theory (sociology) , control (management) , engineering , control engineering , chemistry , artificial intelligence , chromatography , machine learning , fractional distillation , mechanical engineering , programming language , operating system
An offset‐free inferential feedback control strategy for distillation composition control using principal component regression (PCR) and partial least squares (PLS) models is presented in this paper. PCR and PLS model based software sensors are developed from process operational data so that the top and bottom product compositions can be estimated from multiple tray temperature measurements. The PCR and PLS software sensors are then used in the feedback control of the top and bottom product compositions. With this strategy the problem of substantial time delay in composition analyzer based control and of substantial bias in single tray temperature control can be overcome. A practically very important issue in software sensor based feedback control is that static control offsets often exist due to a static estimation bias, especially when the process operating condition changes. A technique for eliminating the static estimation bias and the resulting static control offsets through mean updating of process measurements is proposed in this paper. Applications to a simulated methanol‐water separation column demonstrate the effectiveness of this control strategy.