Optimization of fermentation medium components by response surface methodology (RSM) and artificial neural network hybrid with genetic algorithm (ANN-GA) for lipase production by Burkholderia cenocepacia ST8 using used automotive engine oil as substrate

Bioconversion of used automotive engine oil (UEO) into lipase was conducted via submerged fermentation by Burkholderia cenocepacia ST8, as a strategy for value-added product generation and waste management. Response surface methodology (RSM) and artificial neural network hybrid with genetic algorithm (ANN-GA) were employed to optimize the fermentation medium for enhancing extracellular lipase production by B. cenocepacia ST8. Employing a four-factor-five-level central composite rotatable experimental design (CCRD), a reduced quartic polynomial RSM model and ANN model (4-4-1) trained using Bayesian Regularization were developed to attain the optimized fermentation medium for maximum level of lipase production. The RSM model predicted the following as the optimum media constituents: 2.28% v/v of Tween 80, 2.26% v/v of UEO, 0.79% w/v of nutrient broth, and 1.33% w/v of gum arabic, with an actual lipase yield of 216 U/mL. While, ANN-GA predicted the optimum media constituents to be 3% v/v of Tween 80, 3% v/v of UEO, 0.72% w/v of nutrient broth, and 3.38% w/v of gum arabic, with actual lipase yield of 225 U/mL. In comparison to the unoptimized medium, optimized RSM and ANN-GA systems both demonstrated a 1.6-fold increment in lipase production. Tween 80 and nutrient broth concentrations were the most important variables influencing the lipase production. The findings of this study indicated that the ANN-GA and RSM could be useful for effective optimization of the fermentation medium for enzyme production.