Estimation of multiple biomass growth rates and biomass concentration in a class of bioprocesses

Simultaneous State and Kinetic Observation of Class-Controllable Bioprocesses

Monitoring of bioprocesses is a challenge in designing modern systems for control. In the biotechnology industry, the lack of reliable hardware sensors for key variables related to the metabolism of microorganisms is a topical problem. This predetermines the progress of a scientific field that relies on the development of software sensors for immeasurable variables. In this paper, a new approach for the monitoring of class-controllable bioprocesses that evolve through various physiological states (metabolic regimes) is proposed. At the core of the approach is the potential to present total biomass as a sum of the biomass concentrations obtained during each of the metabolic regimes. Algorithms for estimation of immeasurable variables and their kinetics are here derived and applied using real experimental data. As a case-study, a fed-batch process for phytase production by E. coli is considered. Effectiveness of the method is proven by using two sets of real experiments. One is used to tune the software sensors and the other to verify the approach. The stability analyses are provided, as well. The obtained results and successful verification confirm the adaptive properties of the approach. The considered software sensors will be further built into an interactive system for training specialists/students of biotechnology.

Model-Based Monitoring of Biotechnological Processes—A Review

The monitoring of the main variables and parameters of biotechnological processes is of key importance for the research and control of the processes, especially in industrial installations, where there is a limited number of measurements. For this reason, many researchers are focusing their efforts on developing appropriate algorithms (software sensors (SS)) to provide reliable information on unmeasurable variables and parameters, based on the available on-line information. In the literature, a large number of developments related to this topic that concern data-based and model-based sensors are presented. Up-to-date reviews of data-driven SS for biotechnological processes have already been presented in the scientific literature. Hybrid software sensors as a combination between the abovementioned ones are under development. This gives a reason for the article to be focused on a review of model-based software sensors for biotechnological processes. The most applied model-based methods for monitoring the kinetics and state variables of these processes are analyzed and compared. The following software sensors are considered: Kalman filters, methods based on estimators and observers of a deterministic type, probability observers, high-gain observers, sliding mode observers, adaptive observers, etc. The comparison is made in terms of their stability and number of tuning parameters. Particular attention is paid to the approach of the general dynamic model. The main characteristics of the classic variant proposed by D. Dochain are summarized. Results related to the development of this approach are analyzed. A key point is the presentation of new formalizations of kinetics and the design of new algorithms for its estimation in cases of uncertainty. The efficiency and applicability of the considered software sensors are discussed.

State estimation for large-scale wastewater treatment plants

Many relevant process states in wastewater treatment are not measurable, or their measurements are subject to considerable uncertainty. This poses a serious problem for process monitoring and control. Model-based state estimation can provide estimates of the unknown states and increase the reliability of measurements. In this paper, an integrated approach is presented for the optimization-based sensor network design and the estimation problem. Using the ASM1 model in the reference scenario BSM1, a cost-optimal sensor network is designed and the prominent estimators EKF and MHE are evaluated. Very good estimation results for the system comprising 78 states are found requiring sensor networks of only moderate complexity.

New disposable tubes for rapid and precise biomass assessment for suspension cultures of mammalian cells

We present a new approach for biomass assessment in cell culture using a disposable microcentrifuge tube. The specially designed tube is fitted with an upper chamber for sample loading and a lower 5 microL capillary for cell collection during centrifugation. The resulting packed cell volume (PCV) can be quantitatively expressed as the percentage of the total volume of the sample. The present study focused on the validation of the method with mammalian cell lines that are widely used in bioprocessing. Using several examples, the PCV method was shown to be more precise, rapid, and reproducible than manual cell counting.

On maintenance models in severely and long-term limited membrane bioreactor cultivations

Membrane bioreactors (MBRs) are combinations of common bioreactors and membrane separation units for biomass retention. Through increased biomass concentration, they allow increased productivity (or smaller reactor volume, respectively). Besides high biomass concentrations, operation at very low growth rates is typical for MBRs. In this regime, maintenance metabolism where substrate uptake only yields energy for cell survival becomes of higher importance than in processes run at higher growth rates. While thermodynamically based correlations for the prediction of maintenance coefficients are available for chemostat or other medium growth rate processes, some authors have mentioned a change in energy demand in MBRs and a dependence of maintenance parameters on operating conditions. Due to the fact that often mixed cultures are used and resulting from the different evaluation methods used by different authors, views on the possible influences on maintenance parameters differ. However, it is accepted that common models describing microbial growth and production of metabolites or degradation of pollutants do not consider the effects caused by severe limitations and therefore cannot sufficiently be applied to MBRs. In this study, maintenance parameters were determined for a model organism (Ustilago maydis) and results from different evaluation methods were compared. A continuous fit of respiration data gave more consistent results than the traditional method of plotting specific uptake versus growth rate. They suggest that below micro = 10% micro(max) the maintenance coefficient drops to a third of the value in short-term limited cultures.