Fed-batch Optimization of PHB Synthesis through Mechanistic, Cybernetic and Neural Approaches

Despite its superiority over chemically synthesized petroleum-based polymers, poly-?-hydroxybutyrate (PHB) has been less successful commercially. A prime reason is the low productivity of microbial processes for PHB. High fermentation efficiency requires good modelling and optimization. Neither clas...

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Main Author: Pratap R. Patnaik
Format: Article
Language:English
Published: Academic Publishing House 2006-12-01
Series:Bioautomation
Subjects:
Online Access:http://www.clbme.bas.bg/bioautomation/2006/vol_5.1/files/5_1.3.pdf
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author Pratap R. Patnaik
author_facet Pratap R. Patnaik
author_sort Pratap R. Patnaik
collection DOAJ
description Despite its superiority over chemically synthesized petroleum-based polymers, poly-?-hydroxybutyrate (PHB) has been less successful commercially. A prime reason is the low productivity of microbial processes for PHB. High fermentation efficiency requires good modelling and optimization. Neither classical mechanistic models nor the recent cybernetic models have resulted in sufficiently high yields of PHB. So a neural network description has been proposed here. Relative to the other two approaches, neural optimization doubled the maximum PHB concentration in fed-batch fermentation with Ralstonia eutropha, the most commonly employed organism for PHB production, and it consumed less of the substrates. This advantage and their model-free nature make neural networks an attractive technique to enhance PHB productivity.
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spelling doaj.art-ca74cf80ed824816aadb9288e80530e02022-12-21T23:12:57ZengAcademic Publishing HouseBioautomation1313-261X1312-451X2006-12-01512338Fed-batch Optimization of PHB Synthesis through Mechanistic, Cybernetic and Neural ApproachesPratap R. PatnaikDespite its superiority over chemically synthesized petroleum-based polymers, poly-?-hydroxybutyrate (PHB) has been less successful commercially. A prime reason is the low productivity of microbial processes for PHB. High fermentation efficiency requires good modelling and optimization. Neither classical mechanistic models nor the recent cybernetic models have resulted in sufficiently high yields of PHB. So a neural network description has been proposed here. Relative to the other two approaches, neural optimization doubled the maximum PHB concentration in fed-batch fermentation with Ralstonia eutropha, the most commonly employed organism for PHB production, and it consumed less of the substrates. This advantage and their model-free nature make neural networks an attractive technique to enhance PHB productivity.http://www.clbme.bas.bg/bioautomation/2006/vol_5.1/files/5_1.3.pdf PHBFed-batch fermentationNeural kineticsOptimum dispersion
spellingShingle Pratap R. Patnaik
Fed-batch Optimization of PHB Synthesis through Mechanistic, Cybernetic and Neural Approaches
Bioautomation
PHB
Fed-batch fermentation
Neural kinetics
Optimum dispersion
title Fed-batch Optimization of PHB Synthesis through Mechanistic, Cybernetic and Neural Approaches
title_full Fed-batch Optimization of PHB Synthesis through Mechanistic, Cybernetic and Neural Approaches
title_fullStr Fed-batch Optimization of PHB Synthesis through Mechanistic, Cybernetic and Neural Approaches
title_full_unstemmed Fed-batch Optimization of PHB Synthesis through Mechanistic, Cybernetic and Neural Approaches
title_short Fed-batch Optimization of PHB Synthesis through Mechanistic, Cybernetic and Neural Approaches
title_sort fed batch optimization of phb synthesis through mechanistic cybernetic and neural approaches
topic PHB
Fed-batch fermentation
Neural kinetics
Optimum dispersion
url http://www.clbme.bas.bg/bioautomation/2006/vol_5.1/files/5_1.3.pdf
work_keys_str_mv AT prataprpatnaik fedbatchoptimizationofphbsynthesisthroughmechanisticcyberneticandneuralapproaches