Demonstration of the ability of the bacterial polysaccharide FucoPol to flocculate kaolin suspensions

In this study, the flocculation properties of FucoPol, a bacterial extracellular polysaccharide, were investigated. FucoPol is a high molecular weight polymer and negatively charged due to the presence of glucuronic acid and the acyl groups succinyl and pyruvyl. High flocculation rate values (>70%) were achieved with a low bioflocculant dosage of 1 mg/L, for pH values in the range 3-5 and temperature within 15-20°C. The bioflocculant was also shown to be stable after freezing/thawing and heating up to 100°C. Given the polymer's anionic character, the size of flocs formed and their surface profile, bridging seems to be the main flocculation mechanism of FucoPol. This study demonstrated that FucoPol is a promising natural, biodegradable and biocompatible alternative to the currently used synthetic or inorganic hazardous products, with potential to be used as a novel flocculation agent in several applications, such as water treatment, food or mining. Further studies will involve evaluating the reduction of cation dosage on flocculation efficiency, as well as testing the applicability of FucoPol to flocculate different types of suspended solids, such as, for example, activated carbons, soil solids or yeast cells.

Hybrid modeling of microbial exopolysaccharide (EPS) production: The case of Enterobacter A47

Enterobacter A47 is a bacterium that produces high amounts of a fucose-rich exopolysaccharide (EPS) from glycerol residue of the biodiesel industry. The fed-batch process is characterized by complex non-linear dynamics with highly viscous pseudo-plastic rheology due to the accumulation of EPS in the culture medium. In this paper, we study hybrid modeling as a methodology to increase the predictive power of models for EPS production optimization. We compare six hybrid structures that explore different levels of knowledge-based and machine-learning model components. Knowledge-based components consist of macroscopic material balances, Monod type kinetics, cardinal temperature and pH (CTP) dependency and power-law viscosity models. Unknown dependencies are set to be identified by a feedforward artificial neural network (ANN). A semiparametric identification schema is applied resorting to a data set of 13 independent fed-batch experiments. A parsimonious hybrid model was identified that describes the dynamics of the 13 experiments with the same parameterization. The final model is specific to Enterobacter A47 but can be easily extended to other microbial EPS processes.