Knowledge integration for improved bioprocess supervision

Abstract

The ability to supervise and control highly non-linear and time variant bioprocess is of considerable importance to the biotechnological industries which are continually striving to obtain improved productivity and to reduce process variability. The proposed Intelligent Supervisory System consists of several modules, but in this contribution most attention was given to the fault detection module. Four pattern recognition techniques (Artificial Neural Networks, Principal Component Analysis, Multi-way Principal Component Analysis and Autoassociative Neural Networks) were applied to an industrial fed-batch process. It is shown that a deviation from nominal behaviour of the process can be detected even early on in the fermentation run. Data from industrial penicillin G fermenters is used to demonstrate the procedures.