Table II.
Monitoring Solutions within Biotechnology
| Monitoring goal | Model scenario | Measurement scenario | Process system | Algorithm | Highlights | Ref. |
|---|---|---|---|---|---|---|
| Biomass growth | mass-balance with fixed stoichiometry | carbon in and outflow | P. chrysogenum | SQP (sequential quadratic programming) | (64) | |
| Biomass growth | mass-balance with variable stoichiometry | carbon and electron in and outflow | P. pastoris and E. coli | – | use of system redundancy | (65) |
| Oxygen consumption | mass balance | offgas | CHO | – | simple and robust | (66) |
| CO2 production | mass balance | offgas | CHO | – | carbonate buffered media | (67,68) |
| Biomass concentration | kinetic model | sugar measurements | Daucus carota | extended kalman filter | field of plant cells | (69) |
| Substrates & biomass | kinetic model | CO2, sugars, product | S. cerevisiae | extended kalman filter | NIR based online measurement | (70) |
| Product & biomass | kinetic flux model | offgas analysis, product | P. chrysogenum | particle filter | Raman based online measurements | (58) |
| Biomass growth | kinetic model | offline & online offgas | S. clavuligerus | extended kalman filter | account for measurement delay | (71) |
| Biomass growth | kinetic model with energy balance | calorimetry | E. coli | – | robust growth determination | (72) |