Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Artificial Neural Network <s> This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques--(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)--were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI(24))] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were approximately 3 and approximately 6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were approximately 0.99 and approximately 3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield. <s> BIB001 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Artificial Neural Network <s> A nonlinear model describing the relationship between the biosurfactant concentration as a process output and the critical medium components as the independent variables was developed by artificial neural network modeling. The model was optimized for the maximum biosurfactant production by using genetic algorithm. Based on a single-factor-at-a-time optimization strategy, the critical medium components were found to be glucose, urea, SrCl(2) and MgSO(4). The experimental results obtained from a statistical experimental design were used for the modeling and optimization by linking an artificial neural network (ANN) model with genetic algorithm (GA) in MATLAB. Using the optimized concentration of critical elements, the biosurfactant yield showed close agreement with the model prediction. An enhancement in biosurfactant production by approximately 70% was achieved by this optimization procedure. <s> BIB002 </s> Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review <s> Artificial Neural Network <s> Abstract Environmental and medium parameters estimation is an essential step in Bioprocess engineering. In the present study, artificial neural network (ANN) was employed in estimation of biosurfactants yield from bacterial strain Klebseilla sp. FKOD36, surface tension reduction as well emulsification index. The data obtained from experimental design were used in modelling and optimization of ANN method. Temperature, pH value, incubation period, carbon, nitrogen and hydrocarbon sources were used as input of ANN model independently in the prediction of biosurfactants yield, surface tension reduction and emulsification index. Using the optimized values of critical input elements of ANN, the experimental values of biosurfactant yield, emulsification index and surface tension showed close agreement with the model estimate. The most efficient ANN model assessment was 0.030 g/l for actual value 0.038 g/l of biosurfactant yield, 31.67% for actual value 31.68% of emulsification index, and 21.6 dyne/cm for actual value 21.5 dyne/cm of surface tension respectively. <s> BIB003
Although, many statistical designs such as PBD, RSM using CCD, and factorial designs including the Taguchi design are usually adequate for Bs optimization, in some cases, complex non-linear biological interactions cannot be fully exploited. In this case, the use of advanced computational methods based on artificial intelligence can be used to predict Bs production using their dependent variables. Such is the case of a report by Ahmad et al. BIB003 , who used the Artificial Neural Network (ANN) which is used for simulating complex system performance based on limited experimental data. ANN has been shown to be a powerful tool for optimizing Bs production. In this particular study, the authors estimated Bs yield, ST reduction, and emulsion by Klebsiella sp. FKOD36. Along with the ANN, a Taguchi design was used and temperature, pH, incubation period, nitrogen, carbon, and hydrocarbon sources were used as variables. The most efficient ANN model showed a yield of 0.038 gL −1 , with and emulsion index and ST reduction of 31.67% and 21.6 mNm −1 , respectively. Sivapathasekaran et al. BIB002 also used the ANN strategy with Bacillus circulans MTCC 8281. After determining the most important factors for Bs production (glucose, urea, SrCl 2 , and MgSO 4 ), ANN was used to enhance Bs production by 70%. ANN has been compared to the use of statistical design, and ANN has been shown to be more accurate in predicting the optimum conditions for BS production as in the case of the study carried out by Pal et al. BIB001 . These authors improved the Bs (glycolipid containing trehalose as the major charbohydrate) production in Rhodococcus erythropolis. Because glycolipids are predominantly associated with cell growth, the factors affecting growth were analyzed first. An organic nitrogen source favored biomass production, while inorganic sources showed poor growth. Sucrose and glucose resulted in the best biomass yields, while toluene, paraffin, olive oil, and castor oil were good Bs inducers. The factors selected for optimization with ANN and RSM were sucrose, yeast extract, meat peptone, and toluene. At the end of the optimization experiments, Bs production yield was increased by 3.5-fold.