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.

A novel pseudo-multicomponent isoconversional approach for the estimation of kinetic and thermodynamic parameters of potato stalk thermal degradation

This study examined the thermal degradation kinetics of potato stalk (PS) using a unique isoconversional technique. The kinetic analysis was assessed based on mathematical deconvolution approach with model-free method. The thermogravimetric analyzer (TGA) was used for the non-isothermal pyrolysis of PS at different heating rates. The Gaussian function was then used to extract three pseudo-components (PC) from the TGA findings. The average activation energy value for PS (125.99, 122.79, and 122.85 kJ/mol), PC1 (106.78, 103.83, and 103.92 kJ/mol), PC2 (120.26, 116.31, and 116.55 kJ/mol), and PC3 (373.12, 379.40, and 378.93 kJ/mol) based on OFW, KAS, and VZN model respectively. Furthermore, an artificial neural network (ANN) was used to forecast the thermal degradation data. The findings demonstrated a significant correlation between real and anticipated values. The kinetic and thermodynamic results, along with ANN are critical for constructing pyrolysis reactors that might use waste biomass as a potential feedstock for bioenergy production.

Oil spill modeling in deep waters: Estimation of pseudo-component properties for cubic equations of state from distillation data

Deep-water oil spills represent a major, localized threat to marine ecosystems. Multi-purpose computer models have been developed to predict the fate of spilled oil. These models include databases of pseudo-components from distillation cut analysis for hundreds of oils, and have been used for guiding response action, damage assessment, and contingency planning for marine oil spills. However, these models are unable to simulate the details of deep-water, high-pressure chemistry. We present a new procedure to calculate the chemical properties necessary for such simulations that we validate with 614 oils from the ADIOS oil library. The calculated properties agree within 20.4% with average values obtained from data for measured compounds, for 90% of the chemical properties. This enables equation-of-state calculations of dead oil density, viscosity, and interfacial tension. This procedure enables development of comprehensive oil spill models to predict the behavior of petroleum fluids in the deep sea.