A sequential optimization approach using statistical design of experiments was employed to enhance the lipase production by Candida rugosa in submerged batch fermentation. Twelve medium components were evaluated initially using the Plackett–Burman 2-level factorial design. The significant variables affecting lipase production were found to be glucose, olive oil, peptone, (NH4)2SO4, and FeCl3·6H2O. Various vegetable oils were tested in the second step, and among them, groundnut oil was found to be the best inducer for lipase production by C. rugosa. The third step was to identify the optimal values of the significant medium components with groundnut oil as the inducer using response surface methodology. The regression equation obtained from the experimental data designed using a central composite design was solved, and analyzing the response surface contour plots, the optimal concentrations of the significant variables were determined. A maximum lipase activity of 5.95 U·mL–1, which is 1.64 times the maximum activity obtained in the Plackett–Burman experimental trials, was observed. The optimum combination of medium constituents contained 19.604 g·L–1 glucose, 13.065 mL·L–1 groundnut oil, 7.473 g·L–1 peptone, 0.962 g·L–1 (NH4)2SO4, 0.0019 g·L–1 FeCl3·6H2O, and other insignificant components at the fixed level. A predictive model of the combined effects of the independent variables using response surface methodology and an artificial neural network was proposed. The unstructured kinetic models, logistic model, and Luedeking–Piret model were used to describe cell mass and lipase production. The parameters of the models were evaluated and the lipase production by C. rugosa was found to be growth associated.
Co-Design of an Active Suspension Using Simultaneous Dynamic Optimization