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Identification and Control of Dissolved Oxygen in Hybridoma Cell Culture in a Shear Sensitive Environment
Author(s) -
Simon Laurent,
Karim M. Nazmul
Publication year - 2001
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1021/bp010044r
Subject(s) - setpoint , pid controller , model predictive control , control theory (sociology) , bioreactor , controller (irrigation) , mass transfer , autoregressive model , mass transfer coefficient , transfer function , system identification , biological system , chemistry , analytical chemistry (journal) , chromatography , computer science , temperature control , mathematics , engineering , control engineering , biology , control (management) , statistics , agronomy , organic chemistry , electrical engineering , artificial intelligence , database , measure (data warehouse)
The productivity of mammalian cells can be enhanced by facilitating adequate oxygen transfer into the cultivation medium. However, current methods of controlling dissolved oxygen (DO) fail to account for alterations in medium composition during the course of the fermentation. These changes, which directly affect gas solubility and overall mass transfer coefficient, may be significant and deteriorate controller's performance in the long run. In this paper, the applications of Generalized Predictive Controllers (GPC) to DO control were investigated in a shear sensitive environment and compared to PID and Model Predictive Controllers (MPC). Input and output data for system identification were initially generated by varying the composition of oxygen fed into the bioreactor from 0 to 0.21 mol % while keeping the total inlet gas flow rate at 8.75 vvm. The process was identified using an AutoRegressive model with eXogeneous inputs (ARX) model and tested on different data sets. The model parameters were then correlated with the overall mass transfer coefficients. In simulation tests, the output of the PID controller switched from minimum to maximum values while more continuous control signals were obtained with the MPC and GPC controllers. When tested in a cell‐free medium, all three controllers were able to track setpoint changes with some chattering observed in the control signals. The GPC outperformed the MPC and PID controllers when applied to the cultivation of hybridoma cells.