MONITORING OF BIOSURFACTANT PRODUCTION BY Bacillus subtilis USING BEET PEEL AS CULTURE MEDIUM VIA THE DEVELOPMENT OF A NEURAL SOFT-SENSOR IN AN ELECTRONIC SPREADSHEET

ABSTRACT

This work investigated the combination of agitation and aeration conditions in a bench-bioreactor to identify the optimal biosurfactant production from substrate based on beet peel and glycerol from a biodiesel process. Thus, a central composite rotatable design (CCRD) and responses were evaluated by response surface methodology (RSM) modeling. The optimal operation values determined were 200 rpm (agitation) and 0.5 vvm (aeration), reaching values of 1931.2 mg/L of crude biosurfactant concentration and 28.37 mN/m of surface tension. For the development of a mathematical model based on an artificial neural network (ANN), the experimental data from each run (CCRD) of the bioreactor were used. The results indicated a topology of 6-6-1 neurons with an excellent predictive capacity of biosurfactant concentration: dispersion plot with R² of 0.995, and error criteria SSE of 0.31, MSE of 7.29×10^-4 and RSME of 2.7×10^-2. A soft sensor was then designed in an electronic spreadsheet, computing the biosurfactant production from secondary measurements. Furthermore, the produced biosurfactant showed the ability to remediate oil spreading, evaluated through the appearance of clear zones on the surface of water covered with oil, and also from high emulsification indexes obtained on most of the solvents tested, such as toluene (~65%).

Key words:
Artificial neural network; Response surface modelling; Soft sensor; Beet peel; Oil spreading