Experiments, Modeling, and Media Optimization of Rhamnolipid and Lipopeptide Production

Jujjavarapu, S E (2016) Experiments, Modeling, and Media Optimization of Rhamnolipid and Lipopeptide Production. PhD thesis, Indian Institute of Technology Hyderabad.

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Abstract

Biosurfactants are attractive alternative s to synthetic surfactants due to their environment - friendly nature and wide applications. The most important limitations for the commercial use of biosurfactants are the complexity of the process and high cost of production, which have restricted their us ag e on a large scale . Thus far, the only commercially available biosurfactants are rhamnolipids and surfactin. In this work , experimental data based on a five factor central composite rotatable des ign (CCRD) for batch production of biosurfactants (either rhamnolipid produced by Pseudomonas aeruginosa , or lipopeptide surfactin produced by Bacillus subtilis ) is collected and modeled using either Artificial Neural Network - (ANN) - based Response - surface m odel (RSM) or Regression - based RSM, respectively. The developed models are then used in conjunction with the advanced optimization techniques to determine the optimum medium conditions for maxi mal production of biosurfactant. S pecifically, we used ANN - linked Differential Evolution (DE) to maximize rhamnolipid yield and Regression model - linked Ant Colony Optimization (ACO) to maximize lipopeptide yield. ANN - linked DE predicted a maximum rhamnolipid yield of 55.9 mg/L in the batch conditions we used, and this correlated well with the corresponding experimental value of 56 mg/L. Similarly, ACO predicted a maximum lipopeptide yield of 1.501 g/L, which matched well with the corresponding experimental value of 1.498 g/L. ACO - prediction was also higher (by 8.2%) than 1.387 g/L predicted by Nelder Mead Optimization (NMO) demonstrating that ACO is the better optimization technique for the batch production of lipopeptide.

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