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.
LAQV-REQUIMTE, Systems Biology and Engineering Group, Chemistry Department, FCT/Universidade Nova de Lisboa, 2829-516 Campus Caparica, Portugal.
Rodolfo
R
Marques
LAQV-REQUIMTE, Systems Biology and Engineering Group, Chemistry Department, FCT/Universidade Nova de Lisboa, 2829-516 Campus Caparica, Portugal.
Moritz
M
von Stosch
LAQV-REQUIMTE, Systems Biology and Engineering Group, Chemistry Department, FCT/Universidade Nova de Lisboa, 2829-516 Campus Caparica, Portugal.
Rui M C
RMC
Portela
UCIBIO-REQUIMTE, Biochemical Engineering Group, Chemistry Department, FCT/Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
Cristiana A V
CAV
Torres
UCIBIO-REQUIMTE, Biochemical Engineering Group, Chemistry Department, FCT/Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
Sílvia
S
Antunes
UCIBIO-REQUIMTE, Biochemical Engineering Group, Chemistry Department, FCT/Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
Filomena
F
Freitas
UCIBIO-REQUIMTE, Biochemical Engineering Group, Chemistry Department, FCT/Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
Maria A M
MAM
Reis
LAQV-REQUIMTE, Systems Biology and Engineering Group, Chemistry Department, FCT/Universidade Nova de Lisboa, 2829-516 Campus Caparica, Portugal. Electronic address: rmo@fct.unl.pt.
Rui
R
Oliveira
D061249:Batch Cell Culture Techniques; D019149:Bioreactors / Q000382:microbiology; D004754:Enterobacter / Q000254:growth & development* / Q000378:metabolism; D005643:Fucose / Q000737:chemistry*; D000069550:Machine Learning; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D011135:Polysaccharides, Bacterial / Q000096:biosynthesis*; D012212:Rheology; D014783:Viscosity
D011135:Polysaccharides, Bacterial; D005643:Fucose
Enterobacter A47; Hybrid modeling; Knowledge-based modeling; Machine learning; Microbial exopolysaccharides (EPS); Semiparametric identification