Economic production of Bacillus subtilis SPB1 biosurfactant using local agro‐industrial wastes and its application in enhancing solubility of diesel

BACKGROUND: The present work aimed to optimize a new economic medium for lipopeptide biosurfactant production by Bacillus subtilis SPB1 for application in the environmental field as an enhancer of diesel solubility. Statistical experimental designs and response surface methodology were employed to optimize the medium components. RESULTS: A central composite design was applied to increase the production yield and predict the optimal values of the selected factors. An optimal medium, for biosurfactant production of about 4.5 g L^?1, was found to be composed of sesame peel flour (33 g L^?1) and diluted tuna fish cooking residue (40%) with an inoculum size of 0.22. Increased inoculum size (final OD₆₀₀) significantly improved the production yield. The emulsifier produced was demonstrated to be an alternative to chemically synthesized surfactants since it shows high solubilization efficiency towards diesel oil in comparison with SDS and Tween 80. CONCLUSION: Optimization studies led to a strong improvement in production yield. The emulsifier produced, owing its high solubilization capacity and its large tolerance to acidic and alkaline pH values and salinity, shows great potential for use in bioremediation processes to enhance the solubility of hydrophobic compounds. © 2012 Society of Chemical Industry     

Performance evaluation of batch and unsteady state fed‐batch reactor operations for the production of a marine microbial surfactant

BACKGROUND: Biosurfactants are microbially derived surface‐active and amphipathic molecules produced by various microorganisms. These versatile biomolecules can find potential applications in food, cosmetics, petroleum recovery and biopharmaceutical industries. However, their commercial use is impeded by low yields and productivities in fermentation processes. Thus, an attempt was made to enhance product yield and process productivity by designing a fed‐batch mode reactor strategy. RESULTS: Biosurfactant (BS) production by a marine bacterium was performed in batch and fed‐batch modes of reactor operation in a 3.7 L fermenter. BS concentration of 4.61 ± 0.07 g L^?1 was achieved in batch mode after 22 h with minimum power input of 33.87 × 10³ W, resulting in maximum mixing efficiency. The volumetric oxygen flow rate (K_La) of the marine culture was about 0.08 s^?1. BS production was growth‐associated, as evident from fitting growth kinetics data into the Luedeking‐Piret model. An unsteady state fed batch (USFB) strategy was employed to enhance BS production. Glucose feeding was done at different flow rates ranging from 3.7 mL min^?1 (USFB‐I) to 10 mL min^?1 (USFB‐II). USFB‐I strategy resulted in a maximum biosurfactant yield of 6.2 g l^?1 with an increment of 35% of batch data. The kinetic parameters of USFB‐I were better than those from batch and USFB‐II. CONCLUSION: Comparative performance evaluation of batch and semi‐continuous reactor operations was accomplished. USFB‐I operation improved biosurfactant production by about 35% over batch mode. USFB‐I strategy was more kinetically favorable than batch and USFB‐II. © 2012 Society of Chemical Industry     

Development of a hybrid neural network model to predict feeding method in fed‐batch cultivation for enhanced recombinant streptokinase productivity in Escherichia coli

BACKGROUND: A simple and efficient model for enhancing production of recombinant proteins is essential for cost effective development of processes at industrial scale. A hybrid neural network (HNN) model is proposed combining an unstructured model and neural network to predict the feeding method for the post‐induction phase of fed‐batch cultivation for increased recombinant streptokinase activity in Escherichia coli. RESULTS: The parameters of the unstructured model were estimated from experiments conducted with various feeding methods. The simulated model described the dynamics of the process satisfactorily, however, its predictive capability of the process for different feeding methods is limited due to wide disparity in process parameters. In contrast, a neural network model trained to map the variations in process parameters to state variables complements the ‘first principle’ model in predicting the state variables effectively. CONCLUSIONS: The HNN model is able to predict the product profile for different substrate feed rates. Further, the average volumetric streptokinase activity predicted by the HNN model matches closely the experimental values for fed‐batches having high as well as low streptokinase activity. The HNN model developed in this study could facilitate development of a process for recombinant protein production with minimum number of experiments. Copyright © 2011 Society of Chemical Industry