Patnaik, P R (2001) Further enhancement of fed-batch streptokinase yield in the presence of inflow noise by coupled neural networks. Process Biochemistry, 37 (2). pp. 145-151. ISSN 13595113
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Abstract
Noise carried by the feed stream is a common feature of large-scale bioreactor operations. This noise may be modelled by a set of time-dependent Gaussian distributions. Recent studies have shown that neither unfiltered nor completely filtered noise is desirable. The best performance requires optimally filtered noise. A previous publication in this journal showed that streptokinase (SK) activity in a fed-batch fermentation can be improved substantially through controlled static filtering. Later work with β-galactosidase showed that dynamic filtering by means of a neuralnetwork was superior, especially when it was coupled to a neural filter. That concept has been applied to SK. Coupling of two neuralnetworks increased the peak SK activity (in g/l) by 42% over that for a noise-free feed whereas the improvement with a static filter was 22%
Item Type: | Article |
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Additional Information: | Copyright of this artiocle belongs to Elsevier Science. |
Uncontrolled Keywords: | Streptokinase fermentation; Inflownoise; Neural filtering; Neural control |
Subjects: | Q Science > QD Chemistry |
Depositing User: | Dr. K.P.S.Sengar |
Date Deposited: | 03 Feb 2012 16:14 |
Last Modified: | 09 Jan 2015 10:24 |
URI: | http://crdd.osdd.net/open/id/eprint/869 |
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