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A Kalman filter algorithm and monitoring apparatus for at‐line control of fractional protein precipitation
Author(s) -
Holwill Ian J.,
Chard Stephen J.,
Flanagan Michael T.,
Hoare Michael
Publication year - 1997
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/(sici)1097-0290(19970105)53:1<58::aid-bit9>3.0.co;2-y
Subject(s) - kalman filter , process (computing) , computer science , filter (signal processing) , algorithm , process control , line (geometry) , control theory (sociology) , process engineering , control (management) , mathematics , engineering , artificial intelligence , computer vision , geometry , operating system
Downstream processing operations are often carried out blind in the process timescale since product monitoring on‐line is not common. Knowledge of the location and concentration of the product and key contaminants is complementary to other process information for process development and, if available on‐line in conjunction with a suitable model, control. This article sets out to demonstrate a model describing a two‐cut fractional protein precipitation process and how this may be used for control of the process to maximize yield in the face of variable process stream conditions. Estimation of the model parameters is achieved by means of data‐fitting by least squares and in comparison prediction by a Kalman filter algorithm. A description and error analysis of equipment for at‐line monitoring of the soluble product in a pilot plant environment is presented which includes a micro‐centrifuge necessary to clarify small volumes of sample prior to analysis. Finally, an account of the successful implementation of this equipment and the Kalman filter algorithm for control at bench scale is given where conditions in the process stream are deliberately disturbed to test the control operation. © 1997 John Wiley & Sons, Inc.