Selection of a support for immobilization of a microbial lipase for the hydrolysis of triglycerides

Baterial lipase from Staphylococcus carnosus (pLipMut2) has been immobilized on various supports in order to determine a suitable immobilization technique in terms of activity and stability, when utilized for the hydrolysis of tributyrin. The hydrophobic materials PBA Eupergit and PBA Eupergit 250L prooved to be appropriate supports, when the enzyme was crosslinked with glutaraldehyde after adsorption. No desorption of the immobilized enzyme occured during operation. The pore size of the support has a strong effect on the activity but does not influence stability.The initial activity for immobilized and soluble lipase is found to follow the Arrhenius equation at low temperature, where mass transfer does not affect reaction kinetics. Activation energies for soluble and immobilized lipase were evaluated to be 21.7 kJ mol^–1 and 60.8 kJ mol^–1, respectively.Operational stability was studied in a packed bed recirculation reactor. Thermal desactivation followed first order kinetics with a half-life of 1340 h at 10°C. Model calculations for productivity showed, that optimal temperatures for high productivity are well below the temperature of maximal activity.List of Symbols E _a [kJ mol^–1] activation energy - E _d [kJ mol^–1] activation energy of desactivation - H [–] half-number - k _d [h^–1] desactivation constant - k _d, [h^–1] constant - k _N [–] desactivation constant (number) - N [–] number of runs - p [mol dm^–3] productivity - t [h] time - t _0.5 [h] half-life - T [K] absolute temperature - V [U ml^–1] activity - V(N) [Uml^–1] activity exhibited in the n-th run - V _s,O [U ml^–1] initial activity of supernatant - V _s, [U ml^–1] activity of supernatant after immobilization - V _O [U ml^–1] initial activity - V [U ml^–1] constant - _imm [–] activity yield - [ml ml^–1] ratio of volume of support to volume of supernatant Financial support of this work by the Deutsche Forschungsgemeinschaft (SFB 145, A15) is gratefully acknowledged.     

Tolerance regions with segmented fermentation processes

Many microbial fermentation processes exhibit different phases (e.g. adaption phase, main growth phase, main production phase). The process variables e.g. the biomass vary randomly about their mean. The experimentalist is interested to know the break points of the different phases, and a tolerance region, i.e. a range of possible values of the process variable that can be considered as normal. This paper deals with statistical methods for determining break points and tolerance regions.List of Symbols a _i intercept in phasei - b _i specific growth rate in phasei - e _t deviation of a measurement in timet - tEX expectation of variableX - r number of phases of fermentation - T _i break point of phaseit - t _ij time of measurementj in phasei - t _n–2.1–/2 quantile oft distribution - Y(t) logarithm of measurement at timet Greek Letters 1 – cover probability of tolerance region - 1 – part covered by the tolerance region - ² variance ofe _t - (·) standard normal distribution - quantile of chisquare distribution     

On-line parameter and state estimation of continuous cultivation by extended Kalman filter

Summary The on-line estimation of biomass concentration and of three variable parameters of the non-linear model of continuous cultivation by an extended Kalman filter is demonstrated. Yeast growth in aerobic conditions on an ethanol substrate is represented by an unstructured non-linear stochastic t-variant dynamic model. The filter algorithm uses easily accessible data concerning the input substrate concentration, its concentration in the fermentor and dilution rate, and estimates the biomass concentration, maximum specific growth rate, saturation constant and substrate yield coefficient. The microorganismCandida utilis, strain Vratimov, was cultivated on the ethanol substrate. The filter results obtained with the real data from one cultivation experiment are presented. The practical possibility of using this method for on-line estimation of biomass concentration, which is difficult to measure, is discussed.Nomenclature D dilution rate (h^-1) - DO₂ dissolved oxygen concentration (%) - E identity matrix - F Jacobi matrix of the deterministic part of the system equations g - g continuousn-vector non-linear real function - h m-vector non-linear real function - K Kalman filter gain matrix - K _S saturation constant (kgm^-3) - K_S expectation of the saturation constant estimate - M Jacobi matrix of the deterministic part of the measurement equations h - P(t₀) co-variance matrix of the initial values of the state - P(t_k/t_k) c-variance matrix of the error in (t _k|t _k) - P(t_k+1/t_k) co-variance matrix of the error in (t _k+1|t _k - Q co-variance matrix of the state noise - R co-variance matrix of the output noise - S substrate concentration (kgm^-3) - S _i input substrate concentration - t time - t _k discrete time instant with indexk=0, 1, 2,... - u(t) input vector - v(t_k) measurement (output) noise sequence - w(t) n-vector white Gaussian random process - x(t₀) initial state of the system - (t₀) expectation of the initial state values - x(t) n-dimensional state vector - x(t_k) state vector at the time instantt _k - (t_k|t_k) expectation of the state estimate at timet _k when measurements are known to the timet _k - (t_k+1|t_k) expectation of the state prediction - X biomass concentration (kgm^-3) - expectation of the biomass concentration estimate - y(t_k) m-dimensional output vector at the time instantt _k - Y _XIS substrate yield coefficient - _X|S expectation of the substrate yield coefficient estimate - specific growth rate (h^-1) - _M maximum specific growth rate (h^-1) - expectation of the maximum specific growth rate estimate - state transition matrix     

Reactor comparison and scale-up for the microaerobic production of 2,3-butanediol by Enterobacter aerogenes at constant oxygen transfer rate

Stirred tank (STR), bubble column (BCR) and airlift (ALR) bioreactors of 0.05 and 1.5 m³ total volume were compared for the production of 2,3-butanediol using Enterobacter aerogenes under microaerobic conditions. Batch fermentations were carried out at constant oxygen transfer rate (OTR=35 mmol/lh). At 0.05 m³ scale, the STR reactor achieved much higher biomass and product concentrations than the BCR and ALR reactors. At 1.5 m³ scale, however, exactly the same biomass and product concentrations could be obtained in both STR and ALR reactors. The 1.5 m³ ALR reactor performed also much better than its counterpart at small scale, achieving a productivity 2.4-fold as high as that of the 0.05 m³ BCL and ALR reactors. No differences in performances were observed between BCR and ALR. As compared to STR the tower reactors have a 12 time higher energetic efficiency (referred to product formation) and thus should be the choice for large scale production of 2,3-butanediol.The criterion of constant OTR or constant k _L a is not applicable for the scale-up of this oxygen-sensitive culture due to strong influence of reactor hydrodynamics under microaerobic conditions. The effects of mixing and circulation time on growth and metabolism of E. aerogenes were quantitatively studied in scaled-down experiments with continuous culture. For a successful scale-up of this microaerobic culture it is necessary to have an homogeneous oxygen supply over the entire reactor volume. Under conditions of inhomogeneous oxygen supply an optimum liquid circulation time exists which gives a maximum production of 2,3-butanediol.List of Symbols BD 2,3-butanediol - [mmol/l] saturation value of dissolved oxygen - D [h^–1] dilution rate - D [mm] reactor diameter - D _K [mm] top section diameter - D _R [mm] stirrer diameter - D _S [mm] draft tube diameter - EtOH ethanol - E _P [kg/kWh] energy efficiency refered to product formation - H [mm] height of reactor - HAc acetate - H _L [mm] height of liquid - k _L a [h^–1] volumetric oxygen transfer coefficient - N [rpm=min^–1] stirrer speed - OTR [mmol/lh] oxygen transfer rate - OUR [mmol/lh] oxygen uptake rate - p [Pa] pressure - P [kW] power input - P/V _L [kW/m³] specific power input - [mmHg] oxygen partial pressure (mmHg) or - [mmol/l] dissolved oxygen (mmol/l) - [mmol/gh] specific oxygen uptake rate - q _P [mmol/gh] specific productivity - R [Nm/kgK] gas constant, R = 287.06 - RQ respiration quotient - t _c [s] liquid circulation time - T [°C or K] temperature - TCA tricarboxylic acid - u _G [cm/s] mean superficial gas velocity - v _G [m/s] gas velocity at nozzels of gas distributor - V_G [l/h] aeration rate at inlet - V [m³ or l] total volume - V _L [m³ or l] liquid volume - V _N [l/mol] gas mole volume under normal conditions, V _N = 24.4116 - X [g/l] biomass concentration - CO₂ mole fraction in the effluent gas - O₂ mole fraction in the effluent gas - inlet (above the gas distributor) - ratio of oxygen consumed through TCA cycle to the total oxygen uptake rate - [g/l or kg/m³] density - [%] degree homogeneity - outlet of fermenter or top of the dispersion phase Dedicated to the 65th birthday of Professor Fritz Wagner.We thank Dr. C. Posten and T. Gabel for support with the computer control system UBICON. T.-G. Byun gratefully acknowledges financial support by DAAD.     

Updated model of the batch acetone-butanol fermentation

Summary The recent models of the Acetone-Butanol fermentation did not adequately describe the culture inhibition by the accumulating metabolites and were unable to simulate the acidogenic culture dynamics at elevated pH levels. The present updated modification of the model features a generalised inhibition term and a pH dependent terms for intracellular conversion of undissociated acids into solvent products. The culture dynamics predictions by the developed model compared well with experimental results from an unconventional acidogenic fermentation ofC. acetobutylicum.Nomenclature A acetone concentration in the fermentation broth, [g/L] - AA total concentration of dissociated and undissociated acetic acid, [g/L] - AA _undiss concentration of undissociated acetic acid, [g/L] - APS Absolute Parameter Sensitivity - AT acetoin concentration in the fermentation broth, [g/L] - B butanol concentration in the fermentation broth, [g/L] - BA total concentration of dissociated and undissociated butyric acid, [g/L] - BA _undiss concentration of undissociated butyric acid, [g/L] - E ethanol concentration in the fermentation broth, [g/L] - f(T) inhibition function as defined in Equation (2) - k ₁ constant in Equation (4), [g substrate/g biomass] - k ₂ constant in Equation (4), [g substrate/(g biomass.h)] - k ₁ constant in Equation (5), [g substrate/(g biomass] - k ₂ constant in Equation (5), [g substrate/(g biomass.h)] - k ₃ constant in Equation (6), [g butyric acid/g substrate] - k ₄ constant in Equation (6), [g butyric acid/(g biomass.h)] - k ₅ constant in Equation (7), [g butanol/g substrate] - k ₆ constant in Equation (8), [g acetic acid/g substrate] - k ₇ constant in Equation (8), [g acetic acid/(g biomass.h)] - k ₈ constant in Equation (9), [g acetone/g substrate] - k ₉ constant in Equation (10), [g ethanol/g substrate] - k ₁₀ constant in Equation (11), [g acetoin/g substrate] - k ₁₁ constant in Equation (12), [g lactic acid/g substrate] - K _I Inhibition constant, [g inhibitory products/L] - ke maintenance energy requirement for the cell, [g substrate/(g biomass.h)] - K _AA acetic acid saturation constant, [g acetic acid/L] - K _BA butyric acid saturation constant, [g butyric acid/L] - K _S Monod's saturation constant, [g substrate/L] - LA lactic acid concentration in the fermentation broth, [g/L] - m _i ,n _i constants in Equation (14) - n empirical constant, dependent on degree of inhibition. - P concentration of inhibitory products (B+BA+AA), [g/L] - P _max maximum value of product concentration to inhibit the fermentation, [g/L] - pK_a equilibrium constant - r _A rate of acetone production, [g acetone/L.h] - r _AA rate of acetic acid production, [g acetic acid/L.h] - r _AT rate of acetoin production, [g acetoin/L.h] - r _B rate of butanol production, [g butanol/L.h] - r _BA rate of butyric acid production, [g butyric acid/L.h] - r _E rate of ethanol production, [g ethanol/L.h] - RPS Relative Parameter Sensitivity - r _LA rate of lactic acid production, [g lactic acid/L.h] - r _S dS/dt=total substrate consumption rate, [g substrate/L.h] - r _S substrate utilization rate, [g substrate/L.h] - S substrate concentration in the fermentation broth, [g substrate/L] - S ₀ initial substrate concentration, [substrate/L] - t time, [h] - X biomass concentration, [g/L] - Y _X yield of biomass with respect to substrate, [g biomass/g substrate] - Y _{P i} yield of metabolic product with respect to substrate, [g product/g substrate] Derivatives dX/dt rate of biomass production, [g biomass/L.h] - dP _i /dt rate of product formation, [g product/L.h] Greek letters specific growth rate of the culture, [h^–1] - I specific growth rate of the culture in the presence of the inhibitory products, [h^–1] - µ_max maximum specific growth rate of the culture, [h^–1]     

Slow conductance changes due to potassium depletion in the transverse tubules of frog muscle fibers during hyperpolarizing pulses

Summary When hyperpolarizing currents are applied between the inside and outside of a muscle fiber it is known that there is a slow transient decrease (300- to 600-msec time constant) in the measured fiber conductance sometimes referred to as creep which is maximal in K₂SO₄ Ringer's solutions and which disappears on disruption of the transverse tubular system. An approximate mathematical analysis of the situation indicates that these large, slow conductance changes are to be expected from changes in the K⁺ concentration in the tubular system and are due to differences in transport numbers between the walls and lumen of the tubules. Experiments using small constant-voltage and constant-current pulses (membrane p. d. changes 20 to 30 mV) on the same fibers followed by an approximate mathematical and more exact computed numerical analysis using the measured fiber parameters and published values of tubular system geometry factors showed close agreement between the conductance creep predicted and that observed, thus dispensing with the need for postulated changes in individual membrane conductances at least during small voltage pulses. It is further suggested that an examination of creep with constant-voltage and constant-current pulses may provide a useful tool for monitoring changes in tubular system parameters, such as those occurring during its disruption by presoaking the fibers in glycerol.Table of main symbols used R, T, F Gas constant, Temperature in °K and the Faraday - a Fiber radius - r Radial distance from the center of the fiber (cf. Fig. 2A) - t Time in sec - V ₁,V ₂ Voltages measured by electrodes 1 and 2 (cf. p. 248) - Longitudinal fiber space constant ( ²=R _m a/2R _i ) - R _m ,R _m (t) Total membrane resistance per unit surface area of fiber ( cm²) - R _m (0),R _m () As above att=0 (excluding capacity transient) and att= during a current or voltage pulse - G _m ,G _m (t) Total membrane conductance (mho·cm^–2) per unit area of fiber surface - G _m (0),G _m () As above att=0 (excluding the capacity transient) and att= during a current or voltage pulse - R _sm ,G _sm Surface membrane resistance ( cm²) and conductance (mho·cm^–2), respectively, excluding the TTS - R _T ,G _T Input resistance ( cm²) and conductance (mho·cm^–2) of the TTS referred to unit area of fiber surface - f _T Fraction of the K⁺ conductance in the TTS to the total K⁺ conductance of the fiber [cf. Eq. (7)] - R _i Internal resistivity of the fiber ( cm) - r _s Electrical access resistance of the TTS [ cm²;cf. Fig. 3 and Eq. (24)] - h Diffusional access resistance of the TTS [cf. Eq. (27)] - I ₀ Total current entering fiber (amp) - I _m ,i _m Total current per unit area of fiber surface (amp·cm^–2; considered positive in the hyperpolarizing direction) - i _sm Current going through the surface membrane alone (amp·cm^–2;cf. Fig. 3) - i ₀,i ₀(t) Total current entering the TTS referred to unit area of surface membrane (amp·cm^–2;cf. Fig. 3) - I _K,I _K(r) K⁺ current density crossing the equivalent TTS disc at radial distancer [cf. Fig. 2A and Eq. (23)] - i, i(r, t) Radial current in the lumen of the TTS at radial distancer and timet (cf. Fig. 2B) - C, C(r, t) K⁺ concentration within the TTS at radial distancer and timet (mEquiv·liter^–1) - C _o ,C _K Both refer to external solution and initial TTS K⁺ concentration (mEquiv·liter^–1) - V, V(r, t) The potential at radial distancer in the lumen of the TTS with respect to the external solution at timet (cf. Figs. 2 and 3) - V(a), V(a, t) The p.d. across the access resistance (cf. Figs. 3B and 3C) - V ₀,V ₀(t) The potential of the sarcoplasm with respect to the external solution (cf. Figs. 2 and 3) - E _K The K⁺ equilibrium potential between the sarcoplasm and the externa solution or across the tubular wall - t _K ^m ,t _K ^s The transport number for K⁺ in the TTS membranes and in the solution of the tubular lumen, respectively - The fraction of fiber volume occupied by tubules, and not implicitly including branches - As above but always including branches - A dimensionless network factor for the TTS - G _W Conductance per unit area of tubular wall (mho·cm^–2) - G _L Conductance of tubular lumen (mho·cm^–1) - Volume-to-surface ratio of the TTS - Effective wall conductance of TTS membranes per unit volume of fiber [mho·cm^–3;cf. Eq. (14)] - Effective radial conductance of the lumen of the TTS per unit volume of fiber [cf. Eq. (20)] - d The thickness of the equivalent disc representing the TTS [cf. Eq. (15)] - _T Space constant of the TTS [cf. Eq. (37).cp. Eq. (11)] - D _K The diffusion coefficient of K⁺ ions in the lumen of the TTS (cm² sec^–1) - The effective radial K⁺ diffusion coefficient in the TTS [cf. Eq. (28)] - J ₀,J ₁ Bessel functions of order 0 and 1, respectively - I ₀,I ₁ Modified Bessel functions of order 0 and 1, respectively - Time constants of slow conductance changes - _vc Time constant of slow conductance changes during a constant-voltage pulse - _cc Time constant of slow conductance changes during a constant-current pulse - , _m Roots of various Bessel function equations - g ₁,g ₂,g ₃,g ₄ Constants used to fit cubic equation for conductance-voltage curves [cf. Eq. (71)]     

Glutamic acid production in an airlift reactor with net draft tube

Cultivation of Brevibacterium divaricatum for glutamic acid production in an airlift reactor with net draft tube was developed. Cell concentration gave an index for adding penicillin G. On-line estimation of total sugar concentration yielded an identified model which was used for determination of the substrate addition. Fermentation for glutamic acid production requires high oxygen concentration in the broth. The proposed reactor has the capability to provide sufficient oxygen for the fermentation. Since the reactor is suitable for fed-batch culture, the cultivation of B. divaricatum for glutamic acid production in the proposed reactor is successfully carried out.List of Symbols a system parameter - b system parameter - C _c,in mole fraction carbon dioxide in the gas inlet - C _c,out mole fraction carbon dioxide in the gas outlet - C _L mole/dm³ oxygen concentration in liquid phase - C _L ^* mole/dm³ saturated oxygen concentration in liquid phase - C _0,in mole fraction of oxygen in the gas inlet - C _0,out mole fraction of oxygen in the gas outlet - CPR mole/h/dm³ carbon dioxide production rate based on total broth - E(t) error signal - F _in mole/h inlet gas flow rate - k ₁ constant defined by Eq. (4) - k ₂ constant defined by Eq. (5) - k _L a 1/h volumetric mass transfer coefficient of gas-liquid phase - OUR mole/h/dm³ oxygen uptake rate based on total broth - P atm pressure in the reactor - t h time - TS _c g total sugar consumption - TS _s g/dm³ set point of total sugar concentration - TS _* g/dm³ reference value of total sugar concentration - TS(t) g/dm³ total sugar concentration in the broth at timet - u(t) cm³/min feed rate at timet - V dm³ total broth volume - VVM (dm³/min)/dm³ flow rate per unit liquid volume - a negative constant defined by Eq. (7)     

A simulation program based on a structured population model for biotechnological yeast processes

Summary A segregated population model for budding yeasts and a simulation program based on it are presented. They enable the study of bioprocesses utilizing yeasts in steady and perturbed conditions and in particular the comparison between the model predictions and the experimental results obtained by flow cytometry, which allows the measurement of segregated parameters of cell populations.Nomenclature a genealogical age - A parameter of the budding law - CV coefficient of variation - F _in(t) volumetric input flow - F _out(t) volumetric output flow - h parameter of the division law - K _s parameter of the Monod's law - m cell mass - M _i discretized cell mass - m _b (a,s) critical mass level for budding - m _p cell mass at the time of budding - n(t) cell number per unit volume - n _p number of sub-populations - n _c number of channels - p (a, i, j, k) discrete density function - Q parameter of the budding law - s(t) substrate concentration - S _in(t) substrate concentration in the input flow - t time - T _m minimal length of the budded phase - V(t) culture volume - x(t) biomass concentration - Y yield coefficient - channel width - (s) specific growth rate - _max parameter of the Monod's law     

Inference for an age-dependent,multitype branching-process model of mast cells

We consider an age-dependent, multitype model for the growth of mast cells in culture. After a colony of cells is established by an initiator type, the two possible types of cells are resting and proliferative. Using novel inferential procedures, we estimate the generation-time distribution and the offspring distribution of proliferative cells, and the waiting-time distribution of resting cells.List of Notations B _i cumulative distribution function for the time until branching of a cell of type i - b _i probability density function for the time until branching of a cell of type i - b _i b _i (1–D _i ) - D _i cumulative distribution function for the time until death of a cell of type i - d _i probability density function for the time until death of a cell of type i - probability density function of a gamma distribution - G _i cumulative distribution function for the lifetime of a cell of type i - G _1*2 Convolution of G ₁ and G ₂ - ¯G _i 1–G _i - g _i probability density function for the lifetime of a cell of type i - L _i likelihood of a history of type i - m average number of proliferative daughters produced by dividing cells - M _ij (t) the expected number of type-j cells in a colony at time t if that colony began at time 0 with one type-i cell - M _i+ (t) M _i0 (t) + M _i 1(t) + M _i 2(t) - p _rs probability that a dividing cell produces r proliferative and s resting daughters - t _i times defining colony histories. See IV.2.1 - T ₀ time to division of an initiator cell - T ₁, T ₂ times from birth to division of the two daughters of an initiator cell - T ₍₁₎, T ₍₂₎ order statistics of T ₁ and T ₂ - minimum value of a gamma distribution - scale parameter of a gamma distribution or of an exponential distribution - probability per unit time of death for proliferative and resting cells - _rs expected value of p _rs when there is heterogeneity - shape parameter of a gamma distribution     

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     

Secretion of electrolytes,protein and urea by the mandibular gland of the common wombat (Vombatus ursinus)

Saliva was collected from the mandibular glands of anaesthetized common wombats (Vombatus ursinus) to ascertain maximal flow rates, salivary compostion and possible adaptations, particularly PO₄ ^3- secretion, to assist digestion. After temporary catheterization of the main duct through its oral opening, salivary secretion was evoked at flow rates ranging from 0.02±0.002 (±SEM) ml·min^-1 (0.7±0.07 l·min^-1·kg body weight^-1) to 0.4±0.05 ml·min^-1(14±1.9 l·min^-1·kg body weight^-1) by ipsilateral intracarotid infusion of acetylcholine. The [Na⁺] (15±5.1 to 58±8.6 mmol·l^-1) and [HCO₃ ^-] (35±1.9 to 60±1.9 mmol·l^-1) were positively correlated with salivary flow rate. The [K⁺] (58±5.2 to 30±2.4 mmol·l^-1), [Ca²⁺] (10.4±1.67 to 4.1±0.44 mmol·l^-1), [Mg²⁺] (0.94±0.137 to 0.17±0.032 mmol·l^-1), [Cl^-] (71±9.2 to 45±6.0 mmol·l^-1), [urea] (9.3±0.79 to 5.1±0.54 mmol·l^-1), H⁺ activity (29±1.6 to 17±1.6 nEq·l^-1) and amylase activity (251±57.4 to 92±23.3 kat·l^-1) were negatively correlated with flow. Both concentration and osmolality fell with increasing flow at the lower end of the flow range but osmolality always increased again by maximal flow whereas the relation between protein and flow was not consistent at the higher levels of flow and stimulation. Salivary [PO₄ ³⁺] was not correlated with flow and at 3–14% of the plasma concentration was extremely low. Thus, in contrast to its nearest relative, the koala (Phascolarctos cinereus), the wombat secretes little PO₄ ³⁺ presumably because it does not need high levels of PO₄ ³⁺ in its saliva to facilitate microbial digestion of plant fibre.Abbreviations bw body weight - ww wet weight     

Flow dynamics of immobilized enzyme reactors

Summary Enzymic conversion of glucose to fructose was carried out in a packed bed and in a fluidized bed reactor. The flow dynamics of these two flow systems, loaded with two different types of immobilized loaded with two different types of immobilized glucose isomerase particles, were studied. The theoretical RTD curve calculated from the axial dispersed plug flow model equation was matched to the experimental RTD curve by an optimization technique. The effect of fluid velocity on the extent of liquid dispersion was established. Theoretical predictions on the conversion of glucose to fructose were calculated using three mathematical models, namely, a plug flow model, a continuous stirred tank reactor (CSTR) model and an axial dispersed plug flow model. The experimental results showed that the axial dispersed plug flow model was superior in predicting the performance of both the packed bed and fluidized bed reactor.Abbreviations C Dimensionless concentration - D Dispersion coefficient [cm²/sec] - d _p Mean particle diameter [cm] - E Enzyme concentration [mol/gm] - F Fructose concentration [mol/cm³] - F _e Equilibrium fructose concentration [mol/cm³] - G Glucose concentration [mol/cm³] - G _e Equílibrium glucose concentration [mol/cm³] - G _o Initial glucose concentration [mol/cm³] - Reduced glucose concentration [mol/cm³] - K Equilibrium constant - K _mf Forward reaction rate constant [mol/cm³] - K _mr Reserve reaction rate constant [mol/cm³] - K _m Rate constant [mol/cm³] - L Total length of the reactor bed [cm] - l Length [cm] - Q Flow rate [cm³/s] - r Rate of reaction based on volume of substrate - u Superficial liquid velocity [cm/s] - v Interstitial liquid velocity [cm/s] - V Reactor bed volume [cm³] - V _mf Forward reaction rate constant [mol/s·g enzyme] - V _mr Reserve reaction rate constant [mol/s·g enzyme] - z Dimensionless distance along the reactor - Density [g/cm²]     

Modeling the cucumber fermentation: Growth ofLactobacillus plantarum

Summary Specific growth rate models of product-inhibited cell growth exist but are rarely applied to fermentations beyond ethanol and large-scale antibiotic production. The present paper summarizes experimental data and the development of a model for growth of the commercially important bacterium,Lactobacillus plantarum, in cucumber juice. The model provides an excellent correlation of data for the influence on bacterial growth rate of NaCl, protons (H⁺), and the neutral, inhibitory forms of acetic acid and the fermentation product, lactic acid. The effects of each of the variables are first modeled separately using established functional forms and then combined in the final model formulation.Nomenclature [C] inhibitory component concentration, mM - [C]_max concentration of the inhibitory component where the specific growth rate is zero, mM, determined by model fitting - [H⁺] hydrogen ion concentration, mM - [HLa] undissociated lactic acid concentration, mM - [La^–] dissociated lactic acid concentration, mM - [La_t] total lactic acid ([HLa]+[La^–]) concentration, mM - [HAc] undissociated acetic acid concentration, mM - [Ac^–] dissociated acetic acid concentration, mM - [Ac_t] total acetic acid ([HAc]+[Ac^–]) concentration, mM - [NaCl] sodium chloride concentration, %, w/v - specific growth rate, h^–1 - _max maximum specific growth rate, h^–1 - ₀ specific growth rate, h^–1, at 0 concentration of additive - K _ij inhibition coefficient - , ,K _m coefficients determined by model fitting Mention of a trademark or proprietary product does not constitute a guarantee or warranty of the product by the US Department of Agriculture or North Carolina Agricultural Research Service, nor does it imply approval to the exclusion of other products that may be suitable.     

Gas-phase influence on the mixing in a fluidized bed bio-reactor

Summary The liquid and solids mixing in fluidized bed bio-reactors containing particles with a density only slightly higher than water (1100 kg/m³) is generally consistent with the results found in previous studies for reactors with particles of higher density. The liquid mixing can be described by an axial dispersion model for a large variety of conditions while the solids follow the streamlines of the liquid. In the presence of a gas phase the degree of mixing of both the liquid and the solid phase increased. This effect became larger with increasing reactor diameter. In the extrapolation of laboratory data of three phase fluidized bed bio-reactors to pilot plant systems this effect should be taken into account. The liquid and solids mixing may have a substantial effect on overall conversion rates and on possible microbial stratification in the reactor.Nomenclature Bo Bodenstein number v L/D (-) - D _r diameter of the fluidized bed reactor (m) - D ₁ Dispersion coefficient of the liquid phase (m²/s) - D _g dispersion coefficient of the solid phase (m²/s) - E(in) normalized dye concentration function entering the ideally mixed tank reactor (-) - E(t) normalized dye concentration function as measured (-) - L length of the axial dispersed reactor (m) - t time after dye injection (s) - t _m time constant for microbial selection (s) - t _s solid mixing time constant (s) - t time interval in which a particle migrates within the bed (s) - v _t superficial gas velocity (m/s) - v _g superficial liquid velocity (m/s) - z migration distance of a particle in the bed (m) - ₁ in situ growth rate of a dominant organism (s^-1) - ₂ in situ growth rate of a recessive organism (s^-1) - average residence time in the axial dispersed reactor (s) - _t average residence time in the ideally mixed tank reactor (s)     

Simulation of glucose isomerase reactor: optimum operating temperature

A design equation for immobilized glucose isomerase (IGI) packed bed reactor is developed assuming enzyme deactivation and substrate protection. The developed equation is used to simulate the performance of the reactor at various temperatures (50–80 °C). Enzyme deactivation is significant at high temperature. Substrate protection showed to have significant effect in reducing enzyme deactivation and increasing the enzyme half-life. Factors affecting the optimum operating temperature are discussed. The optimum operating temperature is greatly influenced by the operating period and to a lesser extent with both initial glucose concentration and glucose conversion.Two modes of reactor operation are tested i.e., constant feed flow rate and constant conversion. Reactor operating at constant conversion is more productive than reactor operating at constant flow rate if the working temperature is higher than the optimum temperature. Although at lower temperatures than the optimum, the two modes of operation give the same result.List of Symbols a residual enzyme activity - E [mg/l] concentration of active enzyme - E _a [kJ/mole] activation energy - E ₀ [mg/l] initial concentration of active enzyme - k [Specific] kinetic parameter - k _d [h^–1] first order thermal deactivation rate constant - k _e equilibrium constant - k _m [mole/l] apparent Michaelis constant - k _p [mole/l] Michaelis constant for product - k _s [mole/l] Michaelis constant for substrate - k ₀ [Specific] pre-exponential factor - Q [1/h] volumetric flow rate - ¯Q [1/h] average volumetric flow rate - R [kJ/mol·k] ideal gas constant - s [mole/l] apparent substrate concentration - s [mole/l] substrate concentration - s _e [mole/l] substrate concentration at equilibrium - s ₀ [mole/l] substrate concentration at reactor inlet - p [mole/l] product concentration - p _e [mole/l] product concentration at equilibrium - P _r [mole fructose/l·h] reactor productivity - T [k] temperature - t [h] time - t _p [h] operating time - V [l] reactor volume - v [mole/l·h] reaction rate - v [mole/l] reaction rate under enzyme deactivation and substrate protection - v _m [mole/l·h] maximum apparent reaction rate - v _p [mole/l·h] maximum reaction rate for product - v _s [mole/l·h] maximum reaction rate for substrate - x substrate fractional conversion - x _e substrate fractional conversion at equilibrium Greek Symbols effectiveness factor - mean effectiveness factor - substrate protection factor - [h] residence time - [h] average residence time - ₀ [h] initial residence time     

Effects of wind on thermoregulation and energy balance in deer mice (Peromyscus maniculatus)

Summary The effects of various convective and temperature regimes on heat production, evaporative heat loss, and thermal resistance were studied in deer mice,Peromyscus maniculatus. Heat production (measured as oxygen consumption) increased with increasing wind speed (V) and decreasing ambient temperature (T _a), except atT _a=35°C which was thermoneutral for allV from 0.05 through 3.75 m/s. Evaporative water loss ( ) increased with increasingT _a, but wind had little effect on except at highT _a. In the absence of forced convection, the animals' total resistance to heat transfer (r _t) was high and stable atT _a below thermoneutrality. However, at highV ther _t increased steadily with decreasingT _a. Although deer mice rarely experience high wind speeds in natural microhabitats, the convective regime is nevertheless important in determining rates of heat loss, and must be considered in studies of ecological energetics.Symbols and Abbreviations A animal surface area - HP _n net metabolic heat production - EHL evaporative heat loss - MHP metabolic heat production - r _t total resistance to heat transfer - r _ext external resistance component of r_t - RQ respiratory quotient - pc _p volumetric specific heat of air - T _a ambient temperature - t _b body temperature - t _e operative, or equivalent blackbody temperature of the environment - T _sk skin temperature - T _es standard operative temperature - V wind speed - oxygen consumption - carbon dioxide production - evaporative water loss     

Packing requirements and short-range interactions as structure-directing forces in the intercluster compounds based on silver clusters

Although being composed of high valent ions, the crystal structures of four new supramolecular intercluster compounds, presented in this article, display a major contribution of short-range intermolecular interactions, i.e., aromatic π-π interactions. These structure-directing forces has lead to the formation of an distorted NaCl-type packing in [Ag₅(2,2′-bipyridine)₄(CCtBu)₂][PW₁₂O₄₀] and [Ag₅(2,2′-bipyridine)₄(CCtBu)₂][PMo₁₂O₄₀], and a CsCl-type packing in [Ag₈(2,2′-bipyridine)₆(CCtBu)₄(C₃H₇NO)₂][SiW₁₂O₄₀] and [Ag₈(2,2′-bipyridine)₆(CCtBu)₄(C₃H₇NO)₂][SiMo₁₂O₄₀].     

An evaluation of sensory noise in the human visual system

It is assumed that the activity of a visual channel may be represented as V(t)=g(t)+(t), where g(t) is the deterministic response of the channel due to the presentation of a stimulus and (t) is the trajectory of a wide-sense stationary Gauss process. The stimulus is detected if the event V(t)>S for at least one t[0, T] occurs. Two approximations for the probability of this event are proposed, and it is demonstrated how they may be employed to estimate (i) the value of the second spectral moment ₂ of the noise process _t , where ₂ reflects the speed of the fluctuations of the trajectories _t , and (ii) the value of the internal threshold S. The commonly made assumption of peak — detection is shown to serve as a very good first approximation in particular if the channel is of transient type or — in case of detection by a channel of sustained type — if the stimulus durations are not too long.     

Performance of an external loop air-lift bioreactor for the production of monoclonal antibodies by immobilized hybridoma cells

Summary The performance of an external loop air-lift bioreactor was investigated by assessing the inter-relationships between various hydrodynamic properties and mass transfer. The feasibility of using this bioreactor for the production of monoclonal antibodies by mouse hybridoma cells immobilized in calcium alginate gel beads and alginate/poly-l-lysine microcapsules was also examined. When the superficial gas velocity, V _g , in the 300 ml reactor was varied from 2 to 36 cm/min, the average liquid velocity increased from 3 to 14 cm/sec, the gas hold-up rose from 0.2 to 3.0%, and the oxygen mass transfer coefficient, k _L a, increased from 2.5 to 18.1 h^-1. A minimum liquid velocity of 4 cm/s was required to maintain alginate gel beads (1000 m diameter, occupying 3% of reactor volume) in suspension. Batch culture of hybridoma cells immobilized in alginate beads followed logarithmic growth, reaching a concentration of 4×10⁷ cells/ml beads after 11 days. Significant antibody production did not occur until day 9 into the culture, reaching a value of 100 g/ml of medium at day 11. On the other hand, bioreactor studies with encapsulated hybridoma cells gave monoclonal antibody concentrations of up to 800 g/ml capsules (the antibody being retained within the semipermeable capsule) and maximum cell densities of 2×10⁸ cells/ml capsule at day 11. The volumetric productivities of the alginate gel immobilized cell system and the encapsulated cell system were 9 and 3 g antibody per ml of reactor volume per day, respectively. The main advantage of the bioreactor system is its simple design, since no mechanical input is required to vary the hydrodynamic properties.     

Bioprocess engineering characteristics of the horizontal stirred tank

Bioreactor performance studies of the recently developed horizontal stirred tank with a volume of 421 have been carried out for fermentation with Trichosporon cutaneum. Quantification on the basis of measured oxygen transfer capacity and power consumption is presented and compared with data for a conventional vertical tank bioreactor.During the experiments it has been observed that two different forms of morphology of Trichosporon, i.e. the normal yeast-form (Y) with single cells and a mycelium-form (M) with filamentous cells, are present in the horizontal stirred tank when working with the original strain (DSM 70698). After separation both forms were characterized and later on used for bioreactor performance studies in the horizontal and vertical stirred tank. Results of oxygen efficiency show the drastic effect of the morphology change on bioreactor performance. Finally different bioreactors are quantitatively compared on the basis of oxygen transfer, power consumption and productivity using the reference fermentation system Trichosporon cutaneum.List of Symbols F m³/h flow rate (volumetric) - k _La1/h volumetric transfer coefficient of OTR - M Nm torque - n 1/s rotational speed - P Nm/s power - V m³ volume - V _G1/min gas flow rate - x kg/m³ biomass concentration - ^* morphology index - ^* engineering (specific) viscosity - _app Ns/m² apparent viscosity - ₀ N/m² yield stress (Casson law) - t _1/e h measured time acc. to momentum method [17] - tEh characteristic time of electrode response - t _Gh mean residence time of gas phase Abbreviations CFR completely filled reactor - CRR cyclic ring reactor (torus) - JLR jet loop reactor - HSTR horizontal stirred tank reactor - M mycelium-form of Trichosporon cutaneum - O₂-eff O₂-efficiency - OUR O₂-uptake rate - OTR O₂-transfer rate - STR stirred tank reactor - ThLR thin layer reactor - VSTR vertical stirred tank reactor - Y yeast-form of Trichosporon cutaneum The work presented in this paper was supported by an Austrian Research Grant (FFWF, Project no. 4496)