Optimization of biomass and polyhydroxyalkanoate production by Cupriavidus necator using response surface methodology and genetic algorithm optimized artificial neural network.

High polyhydroxyalkanoate (PHA) yield from selected substrates associated with minimal accumulation of residual biomass can improve the process economy. In this study, different carbon (glucose, sucrose, glycerol, and acetic acid) and nitrogen (NH₄Cl and urea) sources were screened for PHA production by Cupriavidus necator. The effects of incubation time, nitrogen, and phosphate concentration on biomass growth and PHA production were co-optimized through response surface methodology (RSM) and genetic algorithm-optimized artificial neural network (GA-ANN). Sucrose and urea were found to offer significantly better (p <0.001) biomass (1.468 ± 0.007 g l^-1) and PHA (0.924 ± 0.02 g l^-1) yield when compared with other carbon and nitrogen sources. Though the performance of both the models remains similar for biomass (R² = 0.97–0.98), GA-ANN (with six neurones in a hidden layer) seems exceptionally better in predicting PHA yield (R² = 0.97) when compared to the polynomial model (R² = 0.92). The maximum PHA concentration of 2.69 g l^-1 was predicted by the ANN model at an incubation time of 62.80 h with 2.0 g l^-1 of nitrogen and 4.0 g l^-1 of phosphate concentration. The multi-composite desirability using the GA-ANN model projected a better polymer-to-biomass ratio compared to the polynomial model. The inclusion of a cost-benefit analysis framework may be warranted before recommending the optimal conditions obtained through multivariate regression and GA-ANN models. [ABSTRACT FROM AUTHOR]