Diagnosing lactic acid fermentation based on specific rates of growth,substrate consumption and product formation

The on-line calculated specific rates of growth, substrate consumption and product formation were used to diagnose microbial activities during a lactic acid fermentation. The specific rates were calculated from on-line measured cell mass, and substrate and product concentrations. The specific rates were more sensitive indicators of slight changes in fermentation conditions than such monitored data as cell mass or product concentrations.List of Symbols 1/h specific rate of cell growth - 1/h specific rate of substrate consumption - 1/h specific rate of product formation - ^* dimensionless specific rate of cell growth - ^* dimensionless specific rate of substrate consumption - ^* dimensionless specific rate of product formation - _max 1/h maximum specific rate of cell growth - _max 1/h maximum specific rate of substrate consumption - _max 1/h maximum specific rate of product formation - X g/l cell mass concentration - S g/l substrate concentration - S ^* dimensionless substrate concentration - S ₀ g/l initial substrate concentration - P g/l product concentration     

Influence of the ratio of the initial substrate concentration to biomass concentration on the performance of a sequencing batch reactor

Biomass behaviour and COD removal in a benchscale activated sludge reactor have been studied alternating anaerobic and aerobic conditions. Particular attention has been paid to the influence of the ratio of the initial substrate concentration (S ₀) to the initial biomass concentration (X ₀) on the reactor performance. Tests at very low ratios (S ₀/X ₀<2) demonstrate the existence of a threshold below which the reactor performance is seriously affected (S ₀/X ₀=0.5). Under conditions of total suppression of cell duplication, substrate maintenance requirements have also been calculated for the microbial consortium present in the activated sludges. The results obtained show that stressed biomass can survive conditions of substrate lack better than unstressed biomass.List of Symbols b h^–1 specific death rate - COD g/l chemical oxygen demand - DO g/l dissolved oxygen concentration - K _s g/l Monod saturation constant - MLSS g/l mixed liquor suspended solid concentration - P g/l phosphorus concentration - S g/l substrate concentration - S ₀ g/l initial substrate concentration - SS g/l suspended solid concentration - t h time - X g/l biomass concentration - X ₀ g/l initial biomass concentration - Y _SX g/g yield of growth on substrate - _max h^–1 maximum specific growth rate     

Phenol degradation by a psychrotrophic strain of Pseudomonas putida

Summary Cell growth and phenol degradation kinetics were studied at 10°C for a psychrotrophic bacterium, Pseudomonas putida Q5. The batch studies were conducted for initial phenol concentrations, S_o, ranging from 14 to 1000 mg/1. The experimental data for 14<=S_o<=200 mg/1 were fitted by non-linear regression to the integrated Haldane substrate inhibition growth rate model. The values of the kinetic parameters were found to be: _m=0.119 h^–1, K _S=5.27 mg/1 and K _I=377 mg/1. The yield factor of dry biomass from substrate consumed was Y=0.55. Compared to mesophilic pseudomonads previously studied, the psychrotrophic strain grows on and degrades phenol at rates that are ca. 65–80% lower. However, use of the psychrotrophic microorganism may still be economically advantageous for waste-water treatment processes installed in cold climatic regions, and in cases where influent waste-water temperatures exhibit seasonal variation in the range 10–30°C.Nomenclature K _S saturation constant (mg/l) - K _I substrate inhibition constant (mg/l) - specific growth rate (h^–1) - _m maximum specific growth rate without substrate inhibition (h^–1) - _max maximum achievable specific growth rate with substrate inhibition (h^–1) - S substrate (phenol) concentration (mg/l) - S_o initial substrate concentration (mg/l) - S_max substrate concentration corresponding to _max (mg/l) - t time (h) - X cell concentration, dry basis (mg DW/l) - X_f final cell concentration, dry basis (mg DW/l) - X_o initial cell concentration, dry basis (mg DW/l) - Y yield factor (mg DW cell produced/mg substrate consumed)     

Degradation of 4-chlorophenol by enriched mixed cultures utilizing phenol and glucose as added growth substrate

Two mixed cultures, phenol-oxidizing (PO) and glucose-oxidizing (GO), were cultivated in two parallel chemostat reactors. The PO culture was enriched on phenol, and the GO culture was enriched on glucose. Batch biodegradation experiments were conducted to examine the degradation of 4-chlorophenol (4-CP) under various substrate conditions. The results indicate that in the absence of added growth substrate, 4-CP transformation by PO culture was complete at S _c ^o /X _o (initial 4-CP concentration/initial biomass concentration) 0.27 and that by GO culture was complete at S _c ^o /X _o = 0.09. In the presence of 5–500 mg phenol/l, the phenol dosage required to achieve the complete transformation of 4-CP was 60 mg/l at S _c ^o /X _o = 1, increasing to 120 mg/l at S _c ^o /X _o = 2, and to 180 mg/l at S _c ^o /X _o = 5. As glucose was added to the GO culture at a concentration of over 5–500 mg chemical oxygen demand (COD)/l, 4-CP was not completely transformed at S _c ^o /X _o = 5 [S _c ^o = 50 mg/l, X _o = 10 mg/l volatile suspended solids (VSS)]. These two cultures in utilizing added growth substrate were easily switched between glucose and phenol. Overall, the capacity of PO culture to degrade 4-CP, expressed as T _c (4-CP mass consumed /biomass inactivated, having unit of mg 4-CP/mg VSS), was 0.15–0.80, which compares with T _c values of 0.05–0.26 for GO culture. This work shows that adding phenol as a growth substrate is preferable over adding glucose, as it enhances 4-CP transformation, but a final choice should take into account both degradation efficiency and the risk of phenol toxicity.     

Variation of ommatidia number as a function of worker size inCamponotus pennsylvanicus (DeGeer) (Hymenoptera: Formicidae)

Summary The relation of worker size to ommatidia number was examined in the polymorphic antCamponotus pennsylvanicus (DeGeer). Linear regression described this relationship as:Y = 260.9 + 113.6×; whereYis ommatidia number andX is head width. A log-log regression described this relationship as:Y = 323.5 + 286.9*logX(r ² = 0.98). This analysis indicated an allometric relation of ommatidia number to head width, where ommatidia numbers increase at a slower rate than head width. This relationship is discussed in terms of ethotypes associated with worker morphotypes, and the possible mechanisms regulating polymorphic development.     

A kinetic model incorporating energy spilling for substrate removal in substrate-sufficient batch culture of activated sludge

Batch assays are currently used to study the kinetic behavior of microbial growth. However, it has been shown that the outcome of batch experiments is greatly influenced by the initial ratio of substrate concentration (S _o) to biomass concentration (X _o). Substrate-sufficient batch culture is known to have mechanisms of spilling energy that lead to significant nongrowth-associated substrate consumption, and the Monod equation is no longer appropriate. By incorporating substrate consumption associated with energy spilling into the balance of the substrate oxidation reaction, a kinetic model for the observed specific substrate consumption rate was developed for substrate-sufficient batch culture of activated sludge, and was further verified by experimental data. It was demonstrated that the specific substrate consumption rate increased with the increase of the S _o/X _o ratio, and the majority of substrate was consumed through energy spilling at high S _o/X _o ratios. It appears that the S _o/X _o ratio is a key parameter in regulating metabolic pathways of microorganisms. Received: 18 January 1999 / Received revision: 7 May 1999 / Accepted: 28 May 1999     

Simulation of a multicolumn recirculated packed bed reactor (MRPBR) for penicillin acylase

Enzyme reactors for the industrial hydrolysis of penicillin are analyzed in terms of biocatalyst stability to pH. A multicolumn system with packed beds placed in parallel and operating under recirculating conditions is proposed as an adequate reactor for this process. The system is studied both experimentally and with the aid of a simulation program.List of Symbols A transversal area (cm²) - C _A ammonia concentration in the reaction mixture (M) - C ₁ concentration of KH₂PO₄ in buffer (M) - C ₂ concentration of K₂HPO₄ in buffer (M) - d _p biocatalyst diameter (cm) - E enzyme or biocatalyst concentration (gcat l^–1) - K _APA APA non competitive inhibition constant (M) - K _IS excess substrate inhibition constant (M) - Km constant Michaelis-Menten (M) - K _PAA PAA competitive inhibition constant (M) - Q recirculation flow rate (cm³ min^–1) - Q _T recirculation flow rate per column (cm³ min^–1) - Re Reynolds number - S _E substrate concentration entering the neutralization tank (M) - S ₀ initial substrate concentration (M) - S _T substrate concentration in neutralization tank (M) - t time (min) - v _i initial reactor rate (mol min^–1 gcat^–1) - V _s superficial velocity (cm seg^–1) - V _T volume of neutralization tank (cm³) - X _E substrate conversion entering tank - X _T substrate conversion in neutralization tank - X conversion - Z reactor length (cm) - z axial position in reactor (cm) - z ^* non-dimensional axial position in reactor - biocatalyst's density (gcat cm^–3) - p pressure drop in the packed-bed reactor     

A heuristic approach to fed-batch optimisation of streptokinase fermentation

Previous studies have shown that the rate of formation of streptokinase, a secondary metabolite, in batch fermentation is proportional to the specific growth rate of the biomass, which in turn is inhibited by its substrate and the primary product (lactic acid). These kinetics suggest the suitability of fed-batch operation to increase the yield of streptokinase. A near-optimal feed policy has been calculated by the chemotaxis algorithm, and it shows a substrate feed rate decreasing nonlinearly and vanishing after 11 hours. This is followed by batch fermentation for a further 8 hours, at the end of which 12% more streptokinase is generated than by purely batch fermentation. Further improvements in productivity are possible.List of Symbols k _dh^–1 decay constant for active cells - k _ph^–1 decay constant for streptokinase - K _Igl^–1 inhibition constant for lactic acid - K_S gl^–1 inhibition constant for substrate - M gl^–1 lactic acid concentration - P gl^–1 streptokinase concentration - Q 1h^–1 substrate feed rate - S gl^–1 substrate concentration - S _ingl^–1 inlet concentration of substrate - t h time - t _bh end-point of batch fermentation - t _fh end-point of fed-batch fermentation - V l volume of broth in fermenter - V ₀ l initial value of V (at t=0) - V _ml maximum value of V - X gl^–1 total biomass concentration - X _agl^–1 concentration of active biomass - Y _MX yield coefficient for lactic acid from biomass - Y _PX yield coefficient for streptokinase from biomass - Y _XS yield coefficient for biomass from substrate Greek Letters h^–1 specific growth rate of biomass - _mh^–1 maximum specific growth rate     

Optimization of the microbial synthesis of dihydroxyacetone from glycerol with <Emphasis Type="Italic">Gluconobacter oxydans</Emphasis>

An optimized repeated-fed-batch fermentation process for the synthesis of dihydroxyacetone (DHA) from glycerol utilizing Gluconobacter oxydans is presented. Cleaning, sterilization, and inoculation procedures could be reduced significantly compared to the conventional fed-batch process. A stringent requirement was that the product concentration was kept below a critical threshold level at all times in order to avoid irreversible product inhibition of the cells. On the basis of experimentally validated model calculations, a threshold value of about 60 kg m^-3 DHA was obtained. The innovative bioreactor system consisted of a stirred tank reactor combined with a packed trickle-bed column. In the packed column, active cells could be retained by in situ immobilization on a hydrophilized Ralu-ring carrier material. Within 17 days, the productivity of the process could be increased by 75% to about 2.8 kg m^-3 h^-1. However, it was observed that the maximum achievable productivity had not been reached yet.Abbreviations K _O Monod half saturation constant of dissolved oxygen (kg m^-3) - K _S Monod half saturation constant of substrate glycerol (kg m^-3) - O Dissolved oxygen concentration (kg m^-3) - P Product concentration (kg m^-3) - P _crit Critical product concentration constant (kg m^-3) - S Substrate concentration (kg m^-3) - t Time (s) - X Biomass concentration (dry weight) (kg m^-3) - Y _P/S Yield coefficient of product from substrate - Y _X/S Yield coefficient of biomass from substrate - Growth dependent specific production rate constant (kg m^-3) - Growth independent specific production rate constant (s^-1) - Specific growth rate (s^-1) - _max Maximum specific growth rate constant (s^-1)     

Kinetics of soluble microbial product formation in substrate-sufficient batch culture of activated sludge

The kinetics of soluble microbial product (SMP) formation under substrate-sufficient conditions appear to exhibit different patterns from substrate-limited cultures. However, energy spilling-associated SMP formation is not taken into account in the existing kinetic models and classification of SMP. Based on the concepts of growth yield and energy uncoupling, a kinetic model describing energy spilling-associated SMP formation in relation to the ratio of initial substrate concentration to initial biomass concentration (S ₀/X ₀) was developed for substrate-sufficient batch culture of activated sludge, and was verified by experimental data. The specific rate of energy spilling-associated SMP formation showed an increasing trend with the S ₀/X ₀ ratio up to its maximum value. The SMP productivity coefficient (α _p/e) was defined from the model on the basis of energy spilling-associated substrate consumption. Results revealed that less than 5% of energy spilling-associated substrate consumption was converted into SMP. Electronic Publication     

Sequential kinetic parameter estimation of anaerobic fluidized bed bioreactor using an optimization technique

Mathematical model parameters for the methanogenic degradation of propylene glycol were estimated in a sequential manner by means of an optimization technique. Model parameters determined from an initial experimental data set using one bioreactor were then verified with the results from a second bioreactor. The proposed methodology is a useful tool to obtain model parameters for continuous flow reactors with completely mixed regime. Abbrevations: S – substrate concentration (mg COD l^–1); S _in – influent substrate concentration (mg COD l^–1); D _L – dilution rate (day^–1); – stoichiometric coefficients (ND); nx – number of microbial species (ND); X _S – fixed biomass concentration (mg biomass l^–1); X _L – suspended biomass concentration of (mg biomass l^–1); k _d – decay rate of biomass (day^–1); b _S – specific detachment rate of biofilm (day^–1); – specific growth rate of biomass (day^–1); _m – maximum specific growth rate of biomass (day^–1); K _S – half saturation constant (mg COD l^–1); K _I – inhibition constant (mg COD l^–1).     

The effects of oxygen levels on oxygen consumption, survival and ventilatory frequency of sharpsnout sea bream (Diplodus puntazzo Gmelin, 1789) at different conditions of temperature and fish weight

The respiratory behaviour of the sharpsnout sea bream (Diplodus puntazzo) with fish weights between 15 and 509 g at temperatures of 15–29°C was studied, with special attention paid to critical and lethal oxygen saturation (S_crit and LC₅₀, respectively) and ventilatory frequency (V_f). The species maintained a constant oxygen consumption rate regardless of the concentration of dissolved oxygen, until S_crit was reached. The mean of S_crit and LC₅₀ was 34% (2.4 mg L⁻¹) and 11% (0.8 mg L⁻¹), respectively. The S_crit was independent of fish weight and temperature, whereas the LC₅₀ values were positively correlated with both factors (P < 0.05). The higher resistance in small fish could be due to their greater V_f response to hypoxia than in larger animals. Furthermore, the increased metabolism resulting from the effect of temperature was offset by an increased V_f. The V_f remained constant down to a mean value of 67% oxygen saturation, regardless of fish weight and temperature. These findings suggest an optimum oxygen saturation of above 70% for D. puntazzo culture.     

Practical design equations for trickling-filter process

The concept of solid retention time (SRT) was applied in the trickling-filter process. A rational model of the trickling-filter process employing activated-sludge-process operational parameters was presented. The design equation was developed as follows; 1/SRT = [(S₀ ? S_n)/X ]·(F/V)·Y ? k_d, where SRT is the sludge retention time, S₀ is the influent substrate concentration; S_n is the effluent substrate concentration; X is the total cell mass retained per unit filter volume; V is the total volume of the filter; F is the influent flow rate; Y is the cell yield, and k_d is the cell decay rate. A laboratory-scale trickling-filter pilot plant treating synthetic sucrose waste-water was studied to verify the present design equation. The solid retention time was evaluated from the total slime mass (active and inactive) retained and the sludge wasted daily. It was found that the present design equation could be applied for designing the trickling-filter process by the application of SRT employed in the activated sludge process. Also, the SRT could be related to the hydraulic loading and influent substrate concentration for a given filter medium. The variation of SRT by the hydraulic loading at constant organic loading was observed and could be expressed by the mechanistic model. When SRT was maintained more than 12 days, it provided the highest five-day biological oxygen demand (BOD₅) removal, minimum sludge production, and lowest sludge volume index (SVI) value. The present model does include both microbial growth kinetic concepts, which can be more practical and meaningful for the design of a trickling filter.     

High concentration cultivation of Bifidobacterium longum in fermenter with cross-flow filtration

Summary High concentration cultivation of Bifidobacterium longum in a fermenter with cross-flow filtration using a ceramic filter is described. Continuous cross-flow filtration allowed complete recycling of the cells to the fermenter and also continuous separation of inhibitory metabolites. The final cell concentration attained in the cultivation was 54.4 g dry wt./l; this was seven times as high as that without cross-flow filtration. The time course of the cultivation with cross-flow filtration was predicted, based on the assumption that the specific growth rate can be expressed only as a function of concentrations of metabolites (acetate and lactate) in a culture broth.Nomenclature D dilution rate (h^-1) - m maintenance coefficient (h^-1) - OD ₅₇₀ optimal density at 570 nm - P _A acetate concentration (g/l) - P _A0 initial acetate concentration (g/l) - P _L lactate concentration (g/l) - P _L0 initial lactate concentration (g/l) - S lactose (substrate) concentration (g/l) - S ₀ initial lactose (substrate) concentration (g/l) - t cultivation time (h) - Y _x/s growth yield (g/g) - X dry cell concentration (g/l) - X ₀ initial dry cell concentration (g/l) - constant - constant     

The relationship between nutrient feed rate and specific growth rate in fed batch cultures

Summary Equations are described which relate nutrient feed rate to specific microbial growth rate in fed batch culture. Fed batch cultures are classified into three types: 1) those allowing constant specific microbial growth rate, 2) those in which the rate of change of flow rate is constant and 3) those in which the nutrient flow rate is constant. The basic properties of these three types are described.Symbols F medium flow rate, L³ T^–1 - F _o medium flow rate at zero time, L³ T^–1 - g rate of change of flow rate with time, L³ T^–2 - K _v volume constant, being the total cell weight at zero time divided by the product of the yield coefficient and growth-limiting substrate concentration in the feed, L³ - s _r growth limiting substrate concentration in the feed, ML^–3 - V volume of liquid in the growth vessel, L³ - V _f volume of medium fed to the growth vessel, L³ - V _o volume of liquid in the growth vessel at zero time, L³ - X total weight of cells, M - x concentration of cells, ML^–3 - X _g total weight of cells grown, M - X _o total weight of cells at zero time, M - Y yield coefficient, weight of cells grown per unit weight of growth-limiting substrate - specific microbial growth rate, T^–1     

Performance,kinetics, and substrate utilization in a continuous yeast fermentation with cell recycle by ultrafiltration membranes

Summary A continuous single stage yeast fermentation with cell recycle by ultrafiltration membranes was operated at various recycle ratios. Cell concentration was increased 10.6 times, and ethanol concentration and fermentor productivity both 5.3 times with 97% recycle as compared to no recycle. Both specific growth rate and specific ethanol productivity followed the exponential ethanol inhibition form (specific productivity was constant up to 37.5 g/l of ethanol before decreasing), similar to that obtained without recycle, but with greater inhibition constants most likely due to toxins retained in the system at hight recycle ratios.By analyzing steady state data, the fractions of substrate used for cell growth, ethanol formation, and what which were wasted were accounted for. Yeast metabolism varied from mostly aerobic at low recycle ratios to mostly anaerobic at high recycle ratios at a constant dissolved oxygen concentration of 0.8 mg/kg. By increasing the cell recycle ratio, wasted substrate was reduced. When applied to ethanol fermentation, the familiar terminology of substrate used for Maintenance must be used with caution: it is not the same as the wasted substrate reported here.A general method for determining the best recycle ratio is presented; a balance among fermentor productivity, specific productivity, and wasted substrate needs to be made in recycle systems to approach an optimal design.Nomenclature B Bleed flow rate, l/h - C _T Concentration of toxins, arbitrary units - D Dilution rate, h^-1 - F Filtrate or permeate flow rate, removed from system, l/h - F _o Total feed flow rate to system, l/h - K _s Monod form constant, g/l - P Product (ethanol) concentration, g/l - P _o Ethanol concentration in feed, g/l - PP} Adjusted product concentration, g/l - PD Fermentor productivity, g/l-h - R Recycle ratio, F/F _o - S Substrate concentration in fermentor, g/l - S _o Substrate concentration in feed, g/l - V Working volume of fermentor, l - V _MB Viability based on methylene blue test - X Cell concentration, g dry cell/l - X _o Cell concentration in feed, g/l - Y _ATP Cellular yield from ATP, g cells/mol ATP - Y _ATPS Yield of ATP from substrate, mole ATP/mole glucose - Y _G True growth yield or maximum yield of cells from substrate, g cell/g glucose - Y _P Maximum theoretical yield of ethanol from glucose, 0.511 g ethanol/g glucose - Y _P/S Experimental yield of product from substrate, g ethanol/g glucose - Y _x/s Experimental yield of cells from substrate, g cell/g glucose - S _NP/X Non-product associated substrate utilization, g glucose/g cell - k ₁, k₂, k₃, k₄ Constants - k ₁ ^APP , k ₂ ^APP Apparent k ₁, k₃ - k ₁ ^TRUE True k ₁ - m Maintenance coefficient, g glucose/g cell-h - m ^* Coefficient of substrate not used for growth nor for ethanol formation, g glucose/g cell-h - Specific growth rate, g cells/g cells-h, reported as h^-1 - _m Maximum specific growth rate, h^-1 - v Specific productivity, g ethanol/g cell-h, reported as h^-1 - v _m Maximum specific productivity, h^-1     

Linear growth in microbial cultures limited by accumulated substrate

Summary The linear growth phase in cultures limited by intracellular (conservative) substrate is represented by a flat exponential curve. Within the range of experimental errors, the presented model fits well the data from both batch and continuous cultures ofEscherichia coli, whose growth is limited in that way.List of symbols D dilution rate, h^–1 - K_S saturation constant, g.L^–1 - S concentration of the limiting substrate, g.L^–1 - S_i concentration of the limiting substrate accumulated in the cells, g.g^–1 - S_o initial concentration of the limiting substrate, g.L^–1 - t time of cultivation, h - t₁ time of exhaustion of the limiting substrate from medium, h - t_o beginning of exponential phase, h - X biomass concentration, g.L^–1 - X₁ biomass concentration at the time of exhaustion of the limiting substrate from the medium, g.L^–1 - X_o biomass concn. at the beginning of exponential phase, g.L^–1 - biomass concn. at steady-state, g.L^–1 - Y growth yield coefficient (biomass/substrate) - specific growth rate, h^–1 - _m maximum specific growth rate, h^–1     

Der Einfluß der Prozeßparameter auf die Ethanolausbeute in Batchprozessen bei Verwendung von Saccharomyces cerevisiae

The production costs of ethanol are dependent on the efficiency of the substrate-ethanol conversion to a high degree. The more the substrate used during the fermentation is converted into alcohol the better is the economy of the process. Therefore the ethanol yield Y _S^P is an important object of the process optimization. In batch fermentation processes the most essential influence factors are the initial biomass concentration X₀, the initial substrate concentration S₀, the temperature T, and the pH-value. A model reflecting the complex relationships between these influence factors and the ethanol yield could be obtained by regression. It allows an exact valuation of these optimum process parameters which are necessary for realizing high ethanol yields in the batch fermentation. For the strain Saccharomyces cerevisiae Sc 5 used in this research was found an ethanol yield maximum Y_S^P = 0˙5384 at the parameters X₀ = 64.61 g/l S₀ = 82.91 g/l T = 36.45°C pH = 6.54.     

Long term shear effects on a hybridoma cell line by dynamic perfusion devices

The long term shear effects on a hybridoma cell line were studied by the simulation of a hollow fiber perfusion system. Various mechanical/environmental stress conditions were applied and steady state concentrations of live, dead and lysed cells were measured or calculated in a continuous culture. From mathematical modeling, it is shown that inclusion of a lysed cell index (LCI) renders a better fit to the material balance equation at steady state. The specific cell death rate increased with increasing shear force as expected only when the LCI was included. Without the inclusion of the LCI, the calculated specific cell growth rates are about 25–60% of the value when included. The results reported may lend some insight to design improvements since most perfusion devices add shear stresses to the cells in the reactor.List of Symbols b ml/hr continuous culture flow rate - D hr^–1 dilution rate (b/V) - m g glucose/10⁹ cells/hr specific maintenance coefficient - S ₀ g/l feed substrate concentration - S g/l reactor substrate concentration - t hr time - V ml reactor volume - X ⁺ cells/ml live cell concentration - X ^– cells/ml dead cell concentration - X ⁰ cells/ml lysed cell concentration - Y _x/s 10⁹ cells/g glucose cell/substrate yield coefficient - hr^–1 specific growth rate - hr^–1 specific death rate - hr^–1 specific lysis rate - hr^–1 specific lysis rate for simultaneous death and lysis     

Factors which influence the sedimentation characteristics of Torulopsis glabrata after growth in a recirculation system

Summary Some environmental affects on cell aggregation described in the literature are briefly summarized. By means of a biomass recirculation culture (Contact system), using the yeast Torulopsis glabrata, the aggregation behavior of cells in static and in dynamic test systems is described. Sedimentation times required to obtain 50 g · l^–1 yeast dry matter in static systems were always higher than in dynamic ones.In addition to, influencing the biomass yield, the specific growth rate of the yeast also affected cell aggregation. The specific growth rate and therefore the aggregation could be regulated by the biomass recirculation rate as well as by the sedimenter volume.Abbreviations f_o Overflow flow rate (l·h^–1) - f_R Recycle flow rate (l·h^–1) - f_t0t Total flow rate through the fermenter (l·h^–1) - g Gram - h Hour - D_R Fermenter dilution rate due to recycle (h^–1) - D_S Fermeter dilution rate due to substrate (h^–1) - D_tot Total fermenter dilution rate (h^–1) - l Liter - Specific growth rate (h^–1) - P_F Fermenter productivity (g·l^–1·h^–1) - P_FS Overall productivity (g·l^–1·h^–1) - R_pM Rates per minute - RS Residual sugar content in the effluent with respect to the substrate concentration (%) - Y Yield of biomass with respect to sugar concentration (%) - Sed 50 Sedimentation time to reach a YDM of 50 g·l^–1 (min) - V Volume (l) - V_F Fermenter volume (l) - V_Sed Sedimenter volume (l) - VVM Volumes per volume and minute - X_F YDM in the fermenter (g·l^–1) - X_F YDM in the recycle (g·l^–1) - X_S Yeast dry matter due to substrate concentration (g·l^–1) - YDM Yeast dry matter (g·l^–1)