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Keywords:

  • rhamnolipid production;
  • Pseudomonas aeruginosa;
  • Pareto optimization;
  • artificial neural network;
  • differential evolution

Abstract

BACKGROUND: Rhamnolipid is a biosurfactant that finds wide applications in pharmaceuticals and beauty products. Pseudomonas aeruginosa is a producer of rhamnolipids, and the process can be implemented under laboratory-scale conditions. Rhamnolipid concentration depends on medium composition namely, carbon source concentration, nitrogen source concentration, phosphate content and iron content. In this work, existing data7 were used to develop an artificial neural network-based response surface model (ANN RSM) for rhamnolipid production by pseudomonas aeruginosa AT10. This ANN RSM model is integrated with non-dominated sorting differential evolution (DE) to identify the optimum medium composition for this process.

RESULTS: Different strategies for optimization of culture medium composition for this process were evaluated, and the best determined to be an ANN model combined with DE involving a combination of Naïve and Slow and ε-constrained techniques. The optimal culture medium is determined to have carbon source concentration of 49.86 g dm−3, nitrogen source concentration of 4.99 g dm−3, phosphate content of 1.42 g dm−3, and iron content of 17.12 g dm−3. The maximum rhamnolipid activity was found to be 18.07 g dm−3, which compares favorably with that previously reported (18.66 g dm−3), and is in fact closer to the experimentally determined value of 16.50 g dm−3.

CONCLUSION: This method has distinct advantages over methods using statistical regression models, and can be used for optimization of other multi-objective biosurfactant production processes. © 2012 Society of Chemical Industry