Abstract:
:Large continuous flow bioreactors are often under the influence of noise in the feed stream(s). Prior removal of noise is done by filters based either on specific algorithms or on artificial intelligence. Neither method is perfect. Hybrid filters combine both methods and thereby capitalize on their strengths while minimizing their weaknesses. In this study, a number of hybrid models have been compared for their ability to recover nearly noise-free stable oscillations of continuous flow Saccharomyces cerevisiae cultures from aberrant behavior caused by noise in the feed stream. Each hybrid filter had a different neural network in conjunction with an extended Kalman filter (EKF). The choice of the best configuration depended on the performance index. All hybrid filters were superior to both the EKF and purely neural filters. Along with previous studies of monotonic fermentations, the present results establish the suitability of hybrid neural filters for noise-affected bioreactors.
journal_name
Bioprocess Biosyst Engjournal_title
Bioprocess and biosystems engineeringauthors
Patnaik PRdoi
10.1007/s00449-007-0113-4subject
Has Abstractpub_date
2007-05-01 00:00:00pages
181-8issue
3eissn
1615-7591issn
1615-7605journal_volume
30pub_type
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