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Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

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Predictive controller evaluation including non-stationary high frequency noise and outliers for batch solid substrate

J R Pérez-Correa¹, M Fernández-Fernández

¹Departamento de Ingeniería Química y Bioprocesos, Pontificia Universidad Católica de Chile, Casilla 306, Santiago 22, Chile. perez@ing.puc.cl

Bioprocess and Biosystems Engineering

|November 4, 2006

Summary

Controlling large-scale solid substrate fermentation (SSF) bioreactors is challenging. Advanced control algorithms struggle with real-time noise, even with filtering, impacting performance in simulations.

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Area of Science:

Biotechnology
Chemical Engineering
Process Control

Background:

Large-scale solid substrate fermentation (SSF) bioreactors present significant operational and automatic control challenges.
Advanced control algorithms are often too costly and time-consuming to design and tune for real-time SSF applications.

Purpose of the Study:

To evaluate the performance of linear model predictive controllers (LMPC) for SSF bioreactors under realistic simulated conditions.
To investigate the impact of process noise on LMPC performance and explore noise-reduction strategies.

Main Methods:

A phenomenological process model of an SSF pilot bioreactor was developed.
A realistic noise model was coupled with the process model to simulate noisy sensor outputs.
Linear model predictive controllers were tested, incorporating Butterworth low-pass and Hampel outlier shaving filters.

Main Results:

Good control performance was achieved in simulations using the clean phenomenological model.
Control performance degraded significantly when the model output was contaminated with realistic noise.
Signal filtering (Butterworth and Hampel) did not fully restore control performance in the presence of realistic noise.

Conclusions:

Realistic noise significantly degrades the performance of advanced control algorithms in SSF bioreactor simulations.
Current filtering techniques may be insufficient to overcome the challenges posed by realistic noise in real-time SSF control.
Further research is needed to develop more robust control strategies for noisy SSF processes.