Patnaik, P R (2000) Dynamic sensing of intra-cellular variables in an imperfectly mixed bioreactor by a recurrent neural network. INDIAN JOURNAL OF CHEMICAL TECHNOLOGY , 7 (6). pp. 318-325. ISSN 0971-457X
Full text not available from this repository. (Request a copy)Abstract
On-line measurement of intra-cellular variables in a bioreactor is difficult through methods using instrumentation. However, close monitoring of these variables is essential in recombinant fermentations, which are sensitive to disturbances and spatial heterogeneity. Based on an earlier study, a recurrent neural network of the Elman type was applied to a fed-batch fermentation for beta -galactosidase by a temperature-sensitive strain of Escherichia coli. Simulated data of four intra-cellular concentrations in an imperfectly mixed bioreactor were used to train the network, and its predictive capability was tested with unseen data of the rDNA and beta -galactosidase concentrations. Over a wide range of mixing intensity and a fermentation period longer than normally required, the Elman network's performance was better than that reported for modified feedforward networks and it was unaffected by the hydrodynamics. This type of neural network may thus be employed as an online soft sensor for large scale recombinant fermentations.
| Item Type: | Article |
|---|---|
| Additional Information: | Copyright of this article belongs to NISCAIR. |
| Subjects: | T Technology > TP Chemical technology |
| Depositing User: | Dr. K.P.S.Sengar |
| Date Deposited: | 02 Feb 2012 16:31 |
| Last Modified: | 02 Feb 2012 16:31 |
| URI: | http://crdd.osdd.net/open/id/eprint/846 |
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