title: Automated production support for the bioprocess industry.
abstract: This paper describes the application of Artificial Intelligence and Multivariate Statistical Techniques to two industrial fermentation systems. In the first example, an Expert System is shown to provide tighter control of an important process parameter. This is shown to lead to improved consistency of operation. In the second application, Principal Component Analysis is applied to a final stage fermentation production facility. The results presented indicate that the algorithm can provide concise indicators of process faults that can be presented to the operators to assist them in taking suitable corrective actions.
authors.affiliation: School of Engineering, University of Manchester, U.K. barry.lennox@man.ac.uk
authors.forename: Barry
authors.initials: B
authors.lastname: Lennox
authors.forename: Kathryn
authors.initials: K
authors.lastname: Kipling
authors.forename: Jarka
authors.initials: J
authors.lastname: Glassey
authors.forename: Gary
authors.initials: G
authors.lastname: Montague
authors.forename: Mark
authors.initials: M
authors.lastname: Willis
authors.forename: Hugo
authors.initials: H
authors.lastname: Hiden
mesh_terms: D000465:Algorithms; D005103:Expert Systems; D005285:Fermentation; D007218:Industrial Microbiology / Q000379:methods*; D015999:Multivariate Analysis; D011786:Quality Control; D013269:Stochastic Processes