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Experimental investigation of poly-β-hydroxybutyrate production by azohydromonas lata: Kinetics and artificial neural network modeling

Karbasi, F ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1080/00986445.2014.990631
  3. Publisher: Taylor and Francis Ltd , 2016
  4. Abstract:
  5. Batch culture of Azohydromonas lata was investigated for the production of intracellular poly-b-hydroxybutyrate (PHB). In order to determine the C:N value of the culture media for maximizing the microbial productivity of PHB, different concentrations of glucose and ammonium chloride were used as carbon and nitrogen sources, respectively. The optimal temperature and shaking rate was obtained at 30_C and 180 rpm, respectively. The maximum intracellular PHB concentration obtained was 5.09 g/l, which was 20% (w/w) of the cell dry weight (CDW) after 72 h. Also, the synthesis of PHB was growth associated with the C:N ratio of 153.71. The maximum calculated Yp/s was 0.212 (gr/gr) and the specific production rate value after 12 h was 0.264 g/l/h, with 40 and 50 g/l of glucose concentrations, respectively, with 0.5 g/l ammonium chloride kept constant. The chemical composition of the resulting PHB was analyzed by Fourier transform infrared spectroscopy and proton nuclear magnetic resonance spectroscopy. The Leudeking-Piret model was used for kinetic analysis of the PHB production, the statistical analysis of which was modeled by response surface methodology. An artificial neural network technique was applied for modeling of the microbial production of PHB by A. lata as a function of the glucose concentration and CDW, where the minimum mean square error of the model was 0.0012 and 0.0038 for glucose concentrations of 50 g/l and 40 g/l, respectively, when 0.5 g/l ammonium chloride was kept constant
  6. Keywords:
  7. Artificial neural network ; Leudeking-Piret model ; Taguchi method ; Batch cell culture ; Carbon ; Charge density waves ; Chemical analysis ; Chlorine compounds ; Fourier transform infrared spectroscopy ; Glucose ; Magnetic resonance spectroscopy ; Mean square error ; Nuclear magnetic resonance spectroscopy ; Taguchi methods ; Artificial neural network modeling ; Azohydromonas lata ; Experimental investigations ; Leudeking-piret models ; Polyhydroxyalkanoates ; Proton nuclear magnetic resonance spectroscopy ; Response surface methodology ; RSM ; Neural networks
  8. Source: Chemical Engineering Communications ; Volume 203, Issue 2 , 2016 , Pages 224-235 ; 00986445 (ISSN)
  9. URL: http://www.tandfonline.com/doi/full/10.1080/00986445.2014.990631