Process intensification of biopolymer polyhydroxybutyrate production by pseudomonas putida SS9: A statistical approach

Abstract

There are numerous elements of daily life where plastic is employed, yet it is uncertain exactly when it will deteriorate. Poly-(3-hydroxybutyrate) (PHB), a biodegradable polymer, is viewed as a possible substitute for synthetic plastics made from petroleum. With Pseudomonas putida SS9, the current study sought to enhance operational conditions and nutritional factors to enhance PHB production. To maximize the impacts of operational factors, a combination of response surface modeling (RSM) and artificial neural networks (ANN) has been applied. PHB content was used as the response while the interaction effects of the factors were examined. The optimal parameters for PHB synthesis were further tested in a lab scale fermentor. Under optimal conditions, 13.83 g/L of C, 0.57 g/L of N, 0.59 g/L of P, the maximal productivity of PHB obtained with Pseudomonas putida SS9 is 12.89 g/L after 84 h. A mean square value of 15.7 with P < 0.0001 were obtained from the ANOVA results of quadratic polynomial model using RSM. The same construct was employed in MATLAB software to train a feed-forward ANN using the back-propagation approach, generating 12.88 g/L. The data indicated that a properly trained ANN model outperforms the RSM model in prediction. Furthermore, employing dairy waste (cheese whey) as a low-cost feedstock resulted in an equally proportionate PHB yield of 12.02 g/L. Therefore, cheese whey appeared to be a viable alternative carbon source over optimized synthetic media.