creators_name: Patnaik, P R
type: article
datestamp: 2012-01-27 15:08:27
lastmod: 2015-01-12 04:15:16
metadata_visibility: show
title: Neural simulation of an unsteady state continuous recombinant fermentation with imperfect mixing.
ispublished: pub
subjects: QR
full_text_status: none
note: Copyright of this article belongs to Springer Science.
abstract: A continuous fermentation based on a recombinant Escherichia coli strain producing tryptophan synthetase has been simulated by a back-propagation neural network. Data for the network were generated through known kinetics applied to a reactor model with an adjustable degree of macromixing of the broth. A network with just one hidden layer performed satisfactorily for both poor and good macromixing. The best performance was at an intermediate level of mixing, in the region of maximum productivity of the recombinant protein.
date: 1996
publication: Biotechnology Techniques
volume: 10
number: 8
publisher: Springer Science
pagerange: 573-578
id_number: doi:10.1007/BF00157364
refereed: TRUE
issn: 0951-208X
official_url: http://dx.doi.org/10.1007/BF00157364
related_url_url: http://www.springerlink.com/content/v206221k81160234/
related_url_type: pub
citation:   Patnaik, P R  (1996) Neural simulation of an unsteady state continuous recombinant fermentation with imperfect mixing.  Biotechnology Techniques, 10 (8).  pp. 573-578.  ISSN 0951-208X