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

Investigation of the influence of Candida tropicalis on bioethanol production using sugarcane bagasse: stochastic and in silico analysis

This study investigated the impact of Candida tropicalis NITCSK13 on sugarcane bagasse (SCB) consolidated bioprocessing (CSB) using various parameters, such as pH, steam explosion (STEX) pretreatment, and temperature (at two different temperatures, cellulose hydrolysis and ethanol fermentation). The backpropagation neural network (BPNN) method simulated the optimal CSB conditions, achieving a maximum ethanol yield of 44 ± 0.32 g/L (0.443 g of ethanol/g of SCB) from STEX pretreated SCB within 48 h at 55 °C for cellulose hydrolysis and 33 °C for ethanol fermentation and pH 3.5. The simulated conditions were experimentally validated and showed an R2 value of 0.998 and absolute average deviation (AAD) of 1.23%. The strain NITCSK13 also exhibited a high ethanol tolerance of 16% (v/v). The interactions between the inhibitors, cellobiose, furfural, and thermocellulase were assessed through molecular docking. The results revealed a maximum inhibitory constant of 3.7 mM for furfural against the endoglucanase (EnG) of Humicola insolens (2ENG) at 50 °C. Acremonium chrysogenum endoglucanase (5M2D) exhibited a maximum of 88.7 µM for cellobiose at 50 °C. The SWISS homology model of EnG from Candida viswanathii exhibited inhibitory effects similar to those of EnG from Thermoascus and Thermotoga, indicating that the moderately thermophilic yeast Candida sp. cellulase may be capable of efficiently tolerating inhibitors and could be a promising candidate for consolidated bioprocessing of cellulosic ethanol.

Impact of Acetic Acid Supplementation in Polyhydroxyalkanoates Production by Cupriavidus necator Using Mixture-Process Design and Artificial Neural Network

The trend in bioplastic application has increased over the years where polyhydroxyalkanoates (PHAs) have emerged as a potential candidate with the advantage of being bio-origin, biodegradable, and biocompatible. The present study aims to understand the effect of acetic acid concentration (in combination with sucrose) as a mixture variable and its time of addition (process variable) on PHA production by Cupriavidus necator. The addition of acetic acid at a concentration of 1 g l-1 showed a positive influence on biomass and PHA yield; however, the further increase had a reversal effect. The addition of acetic acid at the time of incubation showed a higher PHA yield, whereas maximum biomass was achieved when acetic acid was added after 48 h. Genetic algorithm (GA) optimized artificial neural network (ANN) was used to model PHA concentration from mixture-process design data. Fitness of the GA-ANN model (R2: 0.935) was superior when compared to the polynomial model (R2: 0.301) from mixture design. Optimization of the ANN model projected 2.691 g l-1 PHA from 7.245 g l-1 acetic acid, 12.756 g l-1 sucrose, and the addition of acetic acid at the time of incubation. Sensitivity analysis indicates the inhibitory effect of all the predictors at higher levels. ANN model can be further used to optimize the variables while extending the bioprocess to fed-batch operation.