Abstract
Biosurfactants are biological compounds with active surface interactions. They are produced through metabolism of microorganisms (bacteria, yeast and fungi) and many applications are mentioned in industry (chemical, food, pharmaceutical etc.). However, the production is compromised by the use of methodologies that is unable to compete economically with traditional methods (synthetic substrates, batch without monitoring and no applications). As an alternative indispensable to reduce the costs of the process, artificial intelligence is wide applied currently. Thus, the present work is concerned with the development of intelligent mathematical models to predict the crude biosurfactant concentration using a bench-top bioreactor system. The fermentation substrate was waste material composed of glycerol from biodiesel process and beet peel from restaurants. The microorganism used was Bacillus subtilis. In order to improve the final product quality, these models will be used in monitoring through faster decision-making regarding process variables. Two techniques from the artificial intelligence field were used: artificial neural networks (ANN) and neuro-fuzzy (ANFIS). The biosurfactant concentration is the process variable to be predicted using the historical data acquired from the bioreactor plant. Software MATLAB 2010a was used to implement ANN and ANFIS models. Both of models were built with six neurons in input layer (microbial concentration - MC, glucose concentration- GC, dissolved oxygen concentration - OD, surface tension – ST, dissolved surface tension in 10× - ST-1 and 100× - ST-2) and the others parameters from networks were developed by factors combination. The results showed that models are appropriate to predict profile of crude biosurfactant concentration successfully and were fast enough to be used in nonlinear predictive strategies with good adjustments.
