Simple control of specific growth rate in biotechnological fed-batch processes based on enhanced online measurements of biomass

Abstract

Reliable control of the specific growth rate (μ) in fed-batch fermentations depends on the availability of accurate online estimations of the controlled variable. Due to difficulties in measuring biomass, μ is typically estimated using reference models relating measurements of substrate consumption or oxygen uptake rate to biomass growth. However, as culture conditions vary, these models are adapted dynamically, resulting in complex algorithms that lack the necessary robustness for industrial applicability. A simpler approach is presented where biomass is monitored using dielectric spectroscopy. The measurements are subjected to online balances and reconciled in real time against metabolite concentrations and off-gas composition. The reconciled biomass values serve to estimate the growth rate and a simple control scheme is implemented to maintain the desired value of μ. The methodology is developed with the yeast Kluyveromyces marxianus, tested for disturbance rejection and validated with two other strains. It is applicable to other cellular systems with minor modifications.

A supervision and control tool based on artificial intelligence for high cell density cultivations

“…These changes in growth profile were correctly reproduced by the specific growth rate estimated from permittivity data (Figure 6(c)). The same approach, i.e., the use of on-line permittivity data for real time inference of growth rate for different organisms, has been reported elsewhere (Davey et al, 1996;Henry et al, 2007;Dabros et al, 2010 Culture carried out with auto-induction complex medium, containing glucose, glycerol and lactose as carbon sources, at 37 °C (Silva, 2011). (a) permittivity (perm -pF/cm), carbon evolution rate (CER -mmol/min), cell concentration (Cx -g DCW /L) and viability (10 9 *UFC/mL); (b) CER (mol/min), oxygen uptake rate (OUR -mol/min) and respiratory quotient (RQ); (c) specific growth rate (μ -h -1 ).…”

“…Furthermore, it can lead to decreased viability and even to cell death if the rate of carbon supplied is not enough to meet the maintenance requirements (Silva, 2011). For these reasons, a precise control of the feed flow rate of supplementary medium (F) is required and F must be adjusted to the demand at every stage of cultivation, which can vary considerably after the induction of protein synthesis due to metabolic stress associated to the expression of heterologous genes involved in the production of recombinant proteins (Dabros et al, 2010).…”

Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms

“…Whereas the discontinuous signal dominates the unknown input rateu(t), finite time convergence to s ≡ 0 is still guaranteed thanks to the continuous, but not Lipschitz, third term in (6). A typical state trajectory converging to the surface s = 0 is plotted in Fig.…”

“…Some of these algorithms essentially consist in differentiating the output measurement [31]. This approach is used in [6], where the measured signal is filtered over a 20-minute window to reduce the underlying noise effects. Another widely extended approach consists in using state observers of measured variables, the estimate error being used to construct or adapt the signal estimate.…”

On-Line Monitoring of Biomass Concentration Based on a Capacitance Sensor: Assessing the Methodology for Different Bacteria and Yeast High Cell Density Fed-Batch Cultures

“…Furthermore, once on-line inferred biomass concentration data are available, they can be used in several applications, including the on-line estimation of the specific growth rate and the control of feeding flow rate in fed-batch cultures (Dabros et al, 2010;Horta et al, 2012;Horta et al, 2014).…”