Cognitive optimization of microbial PHB production in an optimally dispersed bioreactor by single and mixed cultures

Abstract

Cognitive (or intelligent) models are often superior to mechanistic models for nonideal bioreactors. Two kinds of cognitive models--cybernetic and neural--were applied recently to fed-batch fermentation by Ralstonia eutropha in a bioreactor with optimum finite dispersion. In the present work, these models have been applied in simulation studies of co-cultures of R. eutropha and Lactobacillus delbrueckii. The results for both cognitive and mechanistic models have been compared with single cultures. Neural models were the most effective for both types of cultures and mechanistic models the least effective. Simulations with co-culture fermentations predicted more PHB than single cultures with all three types of models. Significantly, the predicted enhancements in PHB concentration by cognitive methods for mixed cultures were four to five times larger than the corresponding increases in biomass concentration. Further improvements are possible through a hybrid combination of all three types of models.

Relevance of microbial coculture fermentations in biotechnology

“…Improving the fermentative production of PHAs by a coculture of Azotobacter chroococcum and Bacillus megaterium is suggested by Zhang et al (2003). By the application of neural optimization, the PHB yield of a coculture of Ralstonia eutropha and Lactobacillus delbrueckii could be increased by 19AE4% compared with the single cultivation of R. eutropha (Patnaik 2009). The price of microbial PHA is strongly dependent on the price of substrates (Khanna and Srivastava 2005).…”

“…(2003). By the application of neural optimization, the PHB yield of a coculture of Ralstonia eutropha and Lactobacillus delbrueckii could be increased by 19·4% compared with the single cultivation of R. eutropha ( Patnaik 2009 ) .…”