Abstract
Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel.
Highlights
Soft sensors are frequently used for on-line estimations based on analysis of measurement signals from hardware sensors with software implemented mathematical models (Figure 1) [1,2]
The moving average filtering methods, such as low-pass filters, Savitzky-Golay filters and extended Kalman filters [25,26,27], applied in these previous studies are in this article further improved by applying a sequential filtering method. We suggest that this method is of great benefit, in particular when conditions in the bioreactor are unfavorable, either due to small metabolic heat effects, to bioreactor constructions generating substantial noise effects or when a bioreactor culture grows at low rates
The purpose of the study was to apply the principle of monitoring and control from robust signals derived from standard bioreactor sensors and as a result, providing an example of a soft sensor configuration feasible for Process Analytical Technology (PAT) application
Summary
Soft sensors are frequently used for on-line estimations based on analysis of measurement signals from hardware sensors with software implemented mathematical models (Figure 1) [1,2]. The measurability and accuracy of the estimations are strongly related to the amount of heat produced by the culture, favoring high density cultivations, fast-growing organisms and large volume bioreactor systems This requires signal processing and digital filtering of the obtained data. The moving average filtering methods, such as low-pass filters, Savitzky-Golay filters and extended Kalman filters [25,26,27], applied in these previous studies are in this article further improved by applying a sequential filtering method We suggest that this method is of great benefit, in particular when conditions in the bioreactor are unfavorable, either due to small metabolic heat effects, to bioreactor constructions generating substantial noise effects or when a bioreactor culture grows at low rates. The soft sensor estimates were compared with an alternative on-line hardware sensor for cell viability concentration, a capacitance probe for cell viability
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