Bioreactors performance

Table 13. System and Biological Parameters that Affect Three-Phase Fluidized Bed Bioreactor Performance...

Intraparticle Mass Transfer. One way biofilm growth alters bioreactor performance is by changing the effectiveness factor, defined as the actual substrate conversion divided by the maximum possible conversion in the volume occupied by the particle without mass transfer limitation. An optimal biofilm thickness exists for a given particle, above or below which the particle effectiveness factor and reactor productivity decrease. As the particle size increases, the maximum effectiveness factor possible decreases (Andrews and Przezdziecki, 1986). If sufficient kinetic and physical data are available, the optimal biofilm thickness for optimal effectiveness can be determined through various models for a given particle size (Andrews, 1988 Ruggeri et al., 1994), and biofilm erosion can be controlled to maintain this thickness. The determination of the effectiveness factor for various sized particles with changing biofilm thickness is well-described in the literature (Fan, 1989 Andrews, 1988)... 

Martin SM, Bushell ME, Effect of hyphal morphology on bioreactor performance of antibiotic-producing Saccharopolyspora erythreae cultures. Microbiology 142 1783-1788, 1996. 

The design of real reactors, taking into account the diffusion, axial dispersion and enzyme inactivation effects, is described in the following sections, considering Michaelis-Menten kinetics as a model. These models are veiy important in predicting and simulating bioreactor performance and in modeling future processes. Also, for control purposes they are indispensable. 

Applications of responsive gels in biotechnology have also been examined. For example, Dong and Hoffman proposed the use of temperature sensitive hydrogels for the immobilization of enzymes in bioreactors [110]. Enzyme activity can be turned on and off by causing the gel to swell or shrink when shrunken, the enzyme activity is turned off, since the pores are blocked to the substrate. Different applications are possible for this system besides enhanced bioreactor performance, including bioassays. 

Effect of Acetate, Butyrate, and Corn Steep Liquor on Bioreactor Performance... 

The initial specific rates of 02 consumption and C02 production are calculated and interpreted, and the effect of diminishing 02 concentration on its specific consumption rate is investigated. For each test type, the mean and standard deviation of the 02 and C02 specific rates are calculated and tabulated in Table 1. The four types of stoppered culture tests were compared using two-sample t tests on these quantities to determine whether the particular conditions affected the cell metabolism [74]. If either initial rate showed a statistically significant (95 % confidence level) difference between the two test types being compared, then we concluded that the conditions affected metabolism and would be likely to affect bioreactor performance. 

Figure 12.5 Performance of the BioDeNOx process as a function of the bioreactor performance. The nominal operating point is represented by the black dot.

Sonnleitner B (1993) In Mortensen U, Noorman HJ (eds) Bioreactor performance. Biotechnology Research Foundation, Lund, Sweden, p 143... 

Curcio S, Calabro V, Iorio G (2006) A theoretical and experimental analysis of a membrane bioreactor performance in recycle configuration. J Membr Sci 273 129-142... 

The following sections highlight some of the basic functional properties of cells that profoundly influence bioreactor performance, selected recent advances, and future challenges connected with each. 

Biosensors. Sensors are required to adequately monitor bioreactor performance. Ideally, one would like to have online sensors to minimize the number of samples to be taken from the bioreactor and to automate the bioreactor process. Most bioreactors have autoclavable pH and dissolved oxygen (D.O.) electrodes as online sensors, and use offline detectors to measure other critical parameters such as glucose and glutamine concentration, cell density, and carbon dioxide partial pressure (pC02). An online fiber-optic-based pC02 sensor is commercially available and appears to be robust.37 Probes are also commercially available that determine viable cell density by measuring the capacitance of a cell suspension. Data from perfusion and batch cultures indicate that these probes are reasonably accurate at cell concentrations greater than 0.5 X 106 cells/mL.38,39... 

Hjertager, B. H., and Morud, K., Computational fluid dynamics simulation of bioreactors. In Bioreactor Performance (U. Mortensen and H. J. Noorman, eds.). Ideon, Lund, 1993, p. 47. 

Bouallagui, H., Touhami, Y., Ben Cheikh, R., and Hamdi, M. (2005). Bioreactor performance in anaerobic digestion of fruit and vegetable wastes. Process Biochem. 40, 989-995. 

Another important aspect of process development is pH control. The basal medium is formulated to contain a buffer compatible with cell growth the current industry standard is bicarbonate. Bicarbonate is in equilibrium with CO2 such that bioreactor pH can be lowered by addition of CO2 and raised by addition of a base (such as NaOH). Particularly at large scale, CO2 accumulation has been shown to be detrimental to bioreactor performance, and CO2 levels are lowered by stripping this dissolved gas with sparged nitrogen or air. Because cell growth is dependent on pH, optimization of this parameter allows for maximal cell mass accumulation and increased production of the product of interest. 

Initial process development elforts usually begin with evaluation of a standard process that is well characterized. During this evaluation, various indicators of bioreactor performance, such as cell mass and productivity, are monitored and then analyzed in order to design further process development studies. Bioreactor parameters optimized often include inoculum cell density, impeller speed, medium pH, nutrient levels, temperature, and so forth. 

Create a recombinant cell hne producing mAb product with desired bioreactor performance characteristics and product quality, and then manufacture and test a master cell bank of this cell line. 

Once ku has been experimentally determined (see section 3.5.2), the curve of reactor operation (X vs t) can be obtained for a certain enzyme concentration (meat)-Eq. 5.69 also allows reactor design (determination of reactor volume), since meat is simply the ratio of enzyme load to reaction volume (Mcat/VR). Simulation of batch bioreactor operation under different scenarios of enzyme inactivation is presented in Fig. 5.16 for simple Michaelis-Menten kinetics (a = 14-K/Si b = -1 c = 0) with Si/K =10. Enzyme load in the reactor was calculated to obtain 90% conversion after 10 h of reaction under no inactivation. The strong impact of enzyme inactivation on bioreactor performance can be easily appreciated. 

Liibbert A, Jprgenssen S (2001) Bioreactor performance a more scientific approach for practice. J Biotechnol 85 187-212... 

Table 7.2 Comparison of micro-bioreactor performance and its comparison with other systems... 

Both microscale and macroscale phenomena have the potential to control bioreactor performance. These are illustrated in Fig. 3 for an aerobic process. These processes occur within a spatially heterogeneous physical system, as was demonstrated in Fig. 1, a substrate bed consisting of moist solid particles between which are gas-filled voids. During the fermentation the bulk of the growth occurs at the particle surfaces. 

Second, interpretive models can be proposed. These models take as input operating variables of the bioreactor and measurements of those state variables which it is practical to measure, and give as output estimates of other state variables, including state variables which it may be impossible or impractical to measure during the fermentation. Such models are quite useful in SSF, because it is not practical to obtain direct measurements of the biomass, and parameters which can be measured on-line such as oxygen or carbon dioxide concentrations are only indirectly related to the amount of biomass. The accuracy of such models can be checked by predicting state variables which are easily measured experimentally, such as bed temperature. These models are interpretive and not predictive because they rely on the constant input of fermentation data they cannot predict bioreactor performance simply on the basis of initial conditions. However, they are still quite useful since, if the measured variables can be measured on-line, the model can be used quite successfully in control schemes. 

Models of bioreactor performance usually only take into accoimt the intraparticle diffusion of nutrients if heterogeneity at the macroscale across the bioreactor can be ignored [89, 90]. For bioreactors in which there is heterogeneity at the macroscale, and even in some cases where there is no macroscale heterogeneity, to simplify the model it is common to use empirical equations that do not rely on nutrient or oxygen concentrations. These empirical approaches are conunonly based on the logistic equation, which reasonably describes the biomass profiles found in many SSF systems, with extended periods of acceleration and deceleration of growth ... 

It has been noted several times in the preceding sections that growth activities lead to intraparticle concentration gradients, with consequences for bioreactor performance. No attention has been paid to intraparticle product concentra-... 

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