Optimization Techniques and Development of Neural Models Applied in Biosurfactant Production by <i>Bacillus subtilis</i> Using Alternative Substrates — Oak Academic Publishing
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Optimization Techniques and Development of Neural Models Applied in Biosurfactant Production by <i>Bacillus subtilis</i> Using Alternative Substrates
School of Chemical Engineering (FEQ), Department of Chemical Systems Engineering (DESQ), University of Campinas, Campinas, Brazil
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Department of Chemical and Materials Engineering (DEQM), Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
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Research Center for Chemistry, Biology and Agriculture (CPQBA), University of Campinas, Campinas, Brazil
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School of Chemical Engineering (FEQ), Department of Chemical Systems Engineering (DESQ), University of Campinas, Campinas, Brazil
1 School of Chemical Engineering (FEQ), Department of Chemical Systems Engineering (DESQ), University of Campinas, Campinas, Brazil
2 Department of Chemical and Materials Engineering (DEQM), Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
3 Research Center for Chemistry, Biology and Agriculture (CPQBA), University of Campinas, Campinas, Brazil
4 School of Chemical Engineering (FEQ), Department of Chemical Systems Engineering (DESQ), University of Campinas, Campinas, Brazil
<i> Bacillus subtilis </i> was investigated as production of biosurfactant using a combination based on waste of candy industry and glycerol from biodiesel production process as only substrate. The experimental design chosen for optimization by response surface methodology was a central composite rotatable design (CCRD) and dry weight (DW) and crude biosurfactant (CB) concentrations were selected as responses in analysis. Two techniques were implemented response surface methodology (RSM) and artificial neural network (ANN). First challenge of study was to assess the effects of the interactions between variables and reach optimum values. With the CCRD results, RSM and ANN models were developed, optimizing the production of biosurfactant. The correlation coefficients (R 2 ) of RSM models explained 88% for DW and 73% for CB of the interactions among substrate concentrations, while ANN models explained 99% for DW and 98% for CB, demonstrating that developed ANN models were more accurate and consistent in predicting optimized conditions than RSM model. The maximum DW and CB produced in the optimum conditions were 25.60 ± 5.0 g/L and 668 ± 40 mg/L, respectively. The crude biosurfactant also showed applications in cases of oil spreading in water due to clear zone produced in Petri dishes assays.
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