Shokoufe Tayyeb i - Department of Chemical and Petroleum Engineering,Sharif University of Technology, Azadi Ave.Tehran, Iran
Tayebe Bagheri Lotfabad - National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
Reza Roostaazad - Department of Chemical and Petroleum Engineering,Sharif University of Technology, Azadi Ave.Tehran, Iran

Biosurfactants are surface active compounds produced by various microorganisms. Production of biosurfactants via fermentation of immiscible wastes has the dual benefit of creating economic opportunitiesfor manufacturers, while improving environmental health. A predictor system, recommended in such processes,must be scaled-up. Hence, four neural networks were developed for the dynamic modeling of the biosurfactantproduction kinetics, in presence of soybean oil or refinery wastes including acid oil, deodorizer distillate andsoap stock. Each proposed feed forward neural network consists of three layers which are not fully connected.The input and output data for the training and validation of the neural network models were gathered frombatch fermentation experiments. The proposed neural network models were evaluated by three statistical criteria(R2, RMSE and SE). The typical regression analysis showed high correlation coefficients greater than 0.971, demonstrating that the neural network is an excellent estimator for prediction of biosurfactant production kinetic data in a two phase liquid-liquid batch fermentation system. In addition, sensitivity analysis indicates that residual oil has the significant effect (i.e. 49%) on the biosurfactant in the process

Batch fermentation Biosurfactant Dynamic modeling Neural network