Simulation of the dynamics in the Baker's yeast process

Simulation of the dynamics in a fed batch process for production of Baker's yeast is discussed and applied. Experimental evidences are presented for a model of the energy metabolism. The model involves the concept of a maximum respiratory capacity of the cell. If the sugar concentration is increased above a critical value, corresponding to a critical rate of glycolysis and a maximum rate of respiration, then all additional sugar consumed at higher sugar concentrations is converted into ethanol.In a fed batch process with constant sugar feed the sugar concentration declines slowly. If ethanol is present when the sugar concentration declines below the critical value of 110 mg/dm³ fructose +glucose the metabolism switches rapidly into combined oxidation of sugar and ethanol. Thus, no diauxic growth is involved under process conditions. The rate of ethanol consumption is determined by the free capacity of respiration under these conditions. The invertase activity of the cells was found to be so high that mainly fructose and glucose were present in the medium, typically in the concentration range around 100 mg/dm³. These components are consumed at the same rate but with fructose at a higher concentration, indicating a higher K _s for fructose consumption.The model was used in simulation experiments to demonstrate the dynamics of the Baker's yeast process and the influence of different process conditions.List of Symbols DOT % air sat dissolved oxygen tension - F dm³/h rate of inlet medium flow - H kg/(dm³ % air sat.) oxygen solubility - K kg/m³ saturation constant specified by index - K _L a 1/h volumetric oxygen transfer coefficient - m g/(g · h) maintenance coefficient specified by index - P kg/(m³ · h) mean productivity of biomass in the process - q g/(g · h) specific consumption or production rate - S kg/m³ concentration of sugar in reactor - S ₀ kg/m³ concentration of inlet medium sugar medium t h process time - V dm³ medium volume - X kg/m³ concentration of biomass - Y g/g yield coefficient specified by index - 1/h specific growth rate Index aa anaerobic condition - c critical value - e ethanol - ec ethanol consumption - ep ethanol production - max maximum value - o oxygen - oe oxygen for growth on ethanol - os oxygen for growth on sugar - s sugar - x biomass     

Malate degradation by Schizosaccharomyces yeasts included in alginate beads

Schizosaccharomyces yeasts can be used for deacidification of grape musts. To this aim, we studied malic acid degradation by yeasts included in double layer alginate beads in a bubble column reactor. Use of immobilized micro-organisms allowed a continuous process with high dilution rates giving a deacidification capacity of 0.032 g of malate/hour/dm³/g of beads. The pneumatic agitation was very convenient in this case.List of Symbols D h^–1 Dilution rate for continuous culture - h Residence time for continuous culture - dM/dt kg/(m³ · h) Rate of degradation of malic acid - dS/dt kg/(m³ · h) Rate of consumption of glucose - _max h^–1 Maximal specific rate of growth     

Modelling of alcohol fermentation in a tubular reactor with high biomass recycle

Fermentation in tubular recycle reactors with high biomass concentrations is a way to boost productivity in alcohol production. A computer model has been developed to investigate the potential as well as to establish the limits of this process from a chemical engineering point of view. The model takes into account the kinetics of the reaction, the nonideality of flow and the segregation in the bioreactor. In accordance with literature, it is shown that tubular reactors with biomass recycle can improve productivity of alcohol fermentation substantially.With the help of the computer based reactor model it was also possible to estimate the detrimental effects of cell damage due to pumping. These effects are shown to play a major role, if the biomass separation is performed by filtration units which need high flow rates, e.g. tangential flow filters.List of Symbols Bo d Bodenstein number - c kg/m³ concentration of any component - CPFR continuous plug flow reactor - CSTR continuous stirred tank reactor - d _h m hydraulic diameter - D _eff m²/s dispersion coefficient - f residence time distribution function - K _s kg/m³ monod constant for biomass production - K _s kg/m³ monod constant for alcohol production - p kg/m³ product concentration - P _i kg/m³ lower inhibition limit concentration for biomass production - p _i kg/m³ lower inhibition limit concentration for alcohol production - p _m kg/m³ maximum inhibition limit concentration for biomass production - p _m kg/m³ maximum inhibition limit concentration for alcohol production - q _p h^–1 specific production rate - q _p,max h^–1 maximum specific production rate for alcohol production - q _s h^–1 specific substrate consumption rate - Q _L m _gas ³ /m³h specific gas rate - r _p , r _s , r _x kg/(m³ · h) reaction rate for ethanol production substrate consumption and cell growth, respectively - S _F kg/m³ substrate concentration in feed stream - s kg/m³ substrate concentration - t h time - x kg/m³ biomass concentration - x _max kg/m³ maximum biomass concentration for biomass production - Y _p/s yield coefficient - h^–1 specific growth rate - _max h^–1 maximum specific growth rate - dimensionless time (t/) - h mean residence time - _s glucose conversion     

High yeast concentration in continuous fermentation with cell recycle obtained by tangential microfiltration

Summary Continuous fermentation fed by 150 kg/m³ of glucose with total cell recycling by tangential microfiltration enabled yeasts concentration of 300 kg/m³ (dry weight) to be reached with a dilution rate of 0,5h^–1 and a cell viability greater than 75%. The stability of this system was tested for 50 residence times of the permeate. The method can be used both for the production of cell concentrates and for high rates of metabolite production.Nomenclature D. W. dry weight - X_T (kg/m³) total cell concentration D.W. - X_V (kg/m³) viable cell concentration D.W. - V viability of cell culture in per cent of total cell concentration - S (kg/m³) glucose concentration - P (kg/m³) ethanol concentration - D (h) dilution rate - R (kg/kg) fermentation yield - (h) specific growth rate - vp(kg/kg/h) specific alcohol production rate - (m) yeast size - (kg/kg) kg of intracellular water per kg of dry cells     

Integration of continuous production and recovery of solvents from whey permeate: use of immobilized cells of Clostridium acetobutylicum in a flutilized bed reactor coupled with gas stripping

An investigation was performed into the operation of an integrated system for continuous production and product recovery of solvents (acetone-butanol-ethanol) from the ABE fermentation process. Cells of Clostridium acetobutylicum were immobilized by adsorption onto bonechar, and used in a fluidized bed reactor for continuous solvent production from whey permeate. The reactor effluent was stripped of the solvents using nitrogen gas, and was recycled to the reactor. This relieved product inhibition and allowed further sugar utilization. At a dilution rate of 1.37 h^–1 a reactor productivity of 5.1 kg/(m³ · h) was achieved. The solvents in the stripping gas were condensed to give a solution of 53.7 kg/m³. This system has the advantages of relieving product inhibition, and providing a more concentrated solution for recovery by distillation. Residual sugar and non-volatile reaction intermediates are not removed by gas stripping and this contributes to high solvent yields.List of Symbols C kg/m³ Lactose concentration in reactor effluent - C _b kg/m³ Lactose concentration in bleed stream - C _c kg/m³ Lactose concentration in whey permeate feed - C _i kg/m³ Lactose concentration at reactor inlet - C _p kg/m³ Lactose concentration in condensed solvent stream (=0) - C _r kg/m³ Lactose concentration in recycle line (C _b=C _r) - C kg/h Amount of lactose utilized during certain time period - D h¹ Dilution rate of reactor, F _i/D=F/D - F dm³/h, m3/h F _i = Rate of feed flow to the reactor - F _b dm ³/h, m³/h Rate of bleed - F _c dm³/h, m³/h Rate of feed of whey permeate solution - F _p dm³/h, m³/h Rate of concentrated product removal - F _r dm³/h, m³/h Rate of recycle of stripped effluent to the reactor - P _l % Percent lactose utilization - R _l kg/(m³ · h) Overall lactose utilization rate - R _p kg/(m³ · h) Overall reactor (solvent) productivity - R _sl kg/h Rate of solvent loss - S kg/m³ Solvent concentration in reactor effluent - S _b kg/m³ Solvent concentration in bleed - S _c kg/m³ 0; Solvent concentration in concentrated whey permeate solution - S _i kg/m³ Solvent concentration at inlet of reactor - S _p kg/m³ Solvent concentration in concentrated product stream - S _r kg/m³ Solvent concentration in stripped effluent, S _r=S_b - S kg/h Amount of solvent produced from C amount of lactose in a particular time - ds/dt kg/(m³ · h) Rate of accumulation of solvents in the stripper - t h Time - V dm³, m³ Total reactor volume - V ¹ dm³, m³ Liquid volume in stripper - Y _P/S Solvent yield     

Simulation and optimization of fed-batch culture for ethanol production from molasses

Two simulation methods for ethanol production from molasses by a flocculating yeast, Saccharomyces cerevisiae AM12, were investigated and molasses feeding was optimized. The first method was based on a deterministic model with fixed kinetic parameters and the second was based on regression analysis. The amount of ethanol produced in a fed-batch culture with multiple additions of molasses was simulated by both of these two methods. Simulated results of a fed-batch culture were compared with those of a simple batch culture by a model of regression analysis. The intermittent addition of molasses gave better production than a single addition at the beginning; more frequent addition may further improve production. The experimental results suggested the same. The effect of the amount of the added molasses on ethanol production was investigated by simulation. Repeated batch culture with and without intermittent addition of molasses in each batch was also done.List of Symbols C _e deviation of calculated results from experimental results - F m³ volume of feed medium added to the fermentor - P kg/m³ concentration of ethanol - P _M kg total amount of ethanol - S kg/m³ concentration of sugar - S ₀ kg/m³ concentration of sugar in the molasses feed medium - S _M kg total amount of sugar - V m³ culture volume - X kg/m³ concentration of cells - X _M kg total amount of cells - x _c calculated data - x _e experimental data - h^–1 specific rate of growth - kg-sugar/(kg-cell h) specific rate of sugar consumption - kg-ethanol/(kg-cell h) specific rate of ethanol production     

Mathematical modeling of production of microbial lipids

In the microbial lipid production system using the yeast Rhodotorula gracilis, CFR-1, kinetics of lipid accumulation and substrate utilisation at initial substrate concentrations in the range of 20–100 kg/m³ were investigated using shake flask experiments. A mathematical representation based on logistic model for biomass and Luedeking-Piret model for lipid accumulation gave reasonably good agreement between the theoretical and experimental values for substrate concentration less than 60 kg/m³. The kinetic expressions and parameters obtained through shake flask studies were directly applied to experiments in the laboratory fermentors also and the models were found to hold good for the prediction of the change of biomass, product as well as substrate with time. The attainment of a saturation in the intracellular lipid accumulation with time, however, was not predicted by the model which was shown to be an inherent feature of the Luedeking-Piret model.List of Symbols S ₀, P ₀ kg/m³ Initial concentrations of sugar and lipid respectively - S, S(t) kg/m³ Concentrations of sugar and lipid respeclively at any timet - p,p(t) L kg/m³ Maximum concentration of lipid produced - E % Maximum sugar utilised - dP/dt kg/(m³ · h) Rate of lipid production - -dS/dt kg/(m³ · h) Rate of sugar utilisation - _max h^–1 Maximum specific growth rate - X _max kg/m³ Maximum biomass reached in a run - P _max kg/m³ Maximum product concentration - m, n Constants used in Luedeking-Piret model in eq. (7) - , Constants used to predict residual sugar - k _e maintainance coefficient - Y _x g/g Biomass yield based on sugar consumed - Y _p g/g Lipid yield based on sugar consumed - (dP/d t)_stat kg/(m³ · h) Rate of lipid production at stationary phase - (dS/dt)_stat kg/(m³ · h) Rate of sugar utilisation at stationary phase     

Process kinetic analysis of pleuromulin fermentation

The industrial production of pleuromulin, an example for the production of secondary metabolites, has been carried out of the corporation Biochemie Kundl/Austria. The results of the experiments, especially the process kinetics are analyzed following the principles of the integrating strategy, which has been developed by the authors.Experiments on technical and laboratory scale were carefully designed and evaluated in order to allow an adequate insight in to this complex bioprocess. Typical plots are presented to illustrate the general macroscopic behaviour which serve as a reliable basis for process quantification.Mathematic modelling of this bioprocess will be presented in a following paper.List of Symbols n l/min rotational speed - P kg/m³ pleuromulin concentration - q _P h^–1 specific production rate - q _S1 h^–1 specific rate of glucose consumption - q _S2 h^–1 specific rate of oil consumption - r _P kg/m³ · h product formation rate - r _S1 kg/m³ · h glucose consumption rate - r _X kg/m³ · h growth rate - S ₁ kg/m³ glucose concentration - S ₂ kg/m³ oil concentration - T °C temperature - t h, d time - _g m³/h gas flow rate - X kg/m³ mycelium dry weight - Y _i/j yield coefficient - _app mPa s apparent viscosity - h^–1 specific growth rate The authors extend their gratitude to Biochemie Kundl Gmbh, where the experimental work has been carefully carried out     

Production of glutamine by micro-gel bead-immobilized Corynebacterium glutamicum 9703-T cells in a stirred tank reactor

The effectiveness of using micro-gel bead-immobilized cells for aerobic processes was investigated. Glutamine production by Corynebacterium glutamicum, 9703-T, cells was used as an example. The cells were immobilized in Sr-alginate micro-gel beads 500 m in diameter and used for fermentation processes in a stirred tank reactor with a modified impeller at 400 min^–1. Continuous production of glutamine was carried out for more than 220 h in this reactor and no gel breakage was observed. As a result of the high oxygen transfer capacity of this system, the glutamine yield from glucose was more than three times higher, while the organic acid accumulation was more than 24 times lower than those obtained with 3.0 mm-gel bead-immobilized cells in an airlift fermentor under similar experimental conditions. During the continuous fermentations there was evolution and proliferation of non-glutamine producing strains which led to a gradual decrease in the productivity of the systems. Although a modified production medium which suppresses cell growth during the production phase was effective in maintaining the productivity, the stability of the whole system was shortened due to high cell deactivation rate in such a medium.List of Symbols C kg/m³ glutamine concentration - C _A mol/m ³ local oxygen concentration inside the gel beads - C _AS mol/m ³ oxygen concentration at the surface of the gel beads - De m²/h effective diffusion coefficient of oxygen in the gel bead - DO mol/m³ dissolved oxygen concentration - F dm³/h medium flow rate - K h^–1 glutamine decomposition rate constant - Km mol/m³ Michaelis Menten constant - QO _2max mol/(kg · h) maximum specific respiration rate - R m radius of the gel beads - r m radial distance - t h time - V _C dm ³ volume of the gel beads - V _L dm ³ liquid volume in the reactor - Vm mol/(m³ · h) maximum respiration rate - X kg/m³ cell concentration - x r/R - y C _A /C_AS - h^–1 cell deactivation rate constant - Thiele modulus defined by R(Vm/De Km) ^1/2 - C _AS /Km - _C kg/(m³-gel · h) specific glutamine formation rate - _c dm³-gel/dm³ V _C /V _L      

Performance of cell immobilized packed bed bioreactor for continuous fermentation of alcohol

Experiments were conducted in a packed bed bio-reactor consisting of entrapped yeast cells in alginate matrix for continuous production of alcohol. The variables include initial substrate level, reactor diameter, diameter of the bead and residence time. The influence of these parameters on the conversion of substrate was studied. The film and pore diffusional effects were observed by varying the column and bead diameters, respectively. The pseudo first order reaction rate constant was calculated and correlated with the bead diameter. The effectiveness factor and the Thiele modulus were estimated. A correlation was proposed for fractional conversion in terms of operating variables. It is possible to predict the residence time required and volumetric productivity achieved in a bioreactor for any given initial substrate concentration at any fractional conversion obtained.List of Symbols a _m m²/kg surface are per unit mass of catalyst particle - D m diameter of the reactor - D _e m²/s effective diffusivity - d m particle diameter - h m bed height - k m/s first order reaction rate constant - k m³/(kg · s) pseudo first order reaction rate constant - k _in m³/(kg · s) intrinsic reaction rate constant, (=K/gh) - k _m m/s mass transfer coefficient - P kmol/(m³ · s) volumetric productivity - Q m³/s flow rate of the feed - S kmol/m³ substrate concentration at any time - S _o kmol/m³ initial substrate concentration - S _p kmol/m³ substrate concentration on the gel bead surface - t s reaction time - T (kg · cat · s)/m³ space time (weight of the biocatalyst/flow rate of the feed) - v kmol/(kg · cat · s) reaction rate - V _pfr m³ volume of the packed bed reactor - X [1-(S/S _o)] fraction of the substrate converted in to product Greek Symbols effectiveness factor - Thiele modulus - kg/m³ density of the catalyst particle - s residence time, (= D² h/4Q) - voidage     

Effects of dissolved oxygen concentration on lipase production by Rhizopus delemar

Summary The influence of the concentration of oxygen on lipase production by the fungus Rhizopus delemar was studied in different fermenters. The effect of oxygen limitation ( 47 mol/l) on lipase production by R. delemar is large as could be demonstrated in pellet and filamentous cultures. A model is proposed to describe the extent of oxygen limitation in pellet cultures. Model estimates indicate that oxygen is the limiting substrate in shake flask cultures and that an optimal inoculum size for oxygen-dependent processes can occur.Low oxygen concentrations greatly negatively affect the metabolism of R. delemar, which could be shown by cultivation in continuous cultures in filamentous growth form (D_optimal=0.086 h^-1). Continuous cultivations of R. delemar at constant, low-oxygen concentrations are a useful tool to scale down fermentation processes in cases where a transient or local oxygen limitation occurs.Symbols and Abbreviations CO Oxygen concentration in the gas phase at time = 0 (kg·m^-3) - C_{O 2i} Oxygen concentration at the pellet liquid interface (kg·m^-3) - C_{O 2i} Oxygen concentration in the bulk (kg·m^-3) - D Dilution rate (h^-1) - ID_{O 2} Diffusion coefficient for oxygen (m²·s^-1) - dw Dry weight of biomass (kg) - f Conversion factor (rs _{O 2} to oxygen consumption rate per m³) (-) - k Radial growth rate (m·s^-1) - K Constant - kla Volumetric mass transfer coefficient (s^-1) - klA Oxygen transfer rate (m^-3·s^-1) - kl Mass transfer coefficient (m·s^-1) - K _{O 2} Affinity constant for oxygen (mol·m^-3) - K _w Cotton plug resistance (m^-3·s^-1) - M Henry coefficient (-) - NV Number of pellets per volume (m^-3) - R Radius (m) - RO Radius of oxygen-deficient core (m) - RQ Respiration quotient (mol CO₂/mol O₂) - rs _{O 2} Specific oxygen consumption rate per dry weight biomass (kg O₂·s^-1[kg dw]^-1) - rX Biomass production rate (kg·m^-3·s^-1) - SG Soytone glucose medium (for shake flask experiments) - SG ₄ Soytone glucose medium (for tower fermenter and continuous culture experiments) - V Volume of medium (m^-3) - X Biomass (dry weight) concentration (kg·m^-3) - XR _o Biomass concentration within RO for a given X (kg·m^-3) - Y _{O 2} Biomass yield calculated on oxygen (kg dw/kg O₂) - Thiele modulus - Efficiency factor =1-(RO/R)³ (-) - Growth rate (m^-1·s^-1·kg^1/3) - Dry weight per volume of pellet (kg·m^-3)     

Theoretical analysis of the baker's yeast production: An experimental verification at a laboratory scale

The scale-down procedure can be used to optimize and scale up fermentation processes. The first step in this procedure, a theoretical analysis of the process at a large scale, must give information about the regime, or bottle necks, ruling the process. In order to verify the theoretical results the process analysis has been applied to the fed-batch baker's yeast production at a laboratory scale. The results of this analysis are compared with results from fed-batch experiments. It was concluded that if only one mechanism is ruling the process, for instance mass transfer, the results of the analysis are quite clear. If more than one mechanism is important, for example mass transfer and liquid mixing, additional knowledge is needed to predict the behaviour of the process.Concerning the baker's yeast production, it was concluded that if oxygen limitation occurs, liquid mixing is of little importance.List of Symbols C kg/m³ concentration - C ^* kg/m³ saturation concentration - D m diameter - D _E m²/s effective dispersion coefficient - d m holes of the sparger - F _sm³/s substrate flow to the fermentor - g m/s² gravitational acceleration - H m height - k _La s^–1 volumetric mass transfer coefficient based on the liquid volume - L m length - m _skg/(kg·s) maintenance coefficient - OTR kg/(m³·s) oxygen transfer rate - OUR kg/(m³·s) oxygen uptake rate - r kg/(m³·s) reaction rate - t s time - V m³ volume - v m/s velocity - v _sm/s superficial gas flow rate - y _ijkg/kg yield of componentj oni - s^–1 specific growth rate - s time constant - _gm³/s gas flow rate Indices 0 value att=0 - cir liquid circulation - e ethanol - f feed concentration - g gas phase - in flow going to the fermentor - l liquid phase - m mixing - mt mass transfer - o, O₂ oxygen - oc oxygen consumption - out flow coming out the fermentor - s substrate - sa substrate addition - sc substrate consumption - x biomass     

Penicillin acylase production by E. coli

Enzyme production with E. coli ATCC 11105, in a complex medium using phenylacetic acid as inducer is carried out in a stirred-tank reactor of 10 dm³ and an airlift tower-loop reactor of 60 dm³ with outer loop at a temperature of 27 °C. The optimum inducer concentration was 0.8 kg/m³, which was kept constant by fed-batch operation. The optimum of the relative dissolved O₂-concentration with regard to saturation is below 10% in a stirred-tank reactor and at 35% in a tower-loop reactor. It was kept constant by parameter-adaptive control of the aeration rate. In a stirred-tank enzyme productivity is slightly higher than in a tower-loop reactor, and much higher than in a bubble column reactor.List of Symbols CPR kg/(m³ h) CO₂-production rate - OTR kg/(m³ h) O₂-transfer rate - OUR kg/(m³ h) O₂-utilization rate - PAA phenylacetic acid (inducer) - RQ = CPR/OUR respiratory quotient - X kg/m³ cell mass concentration - _m h^–1 maximum specific growth rate     

Model aided design of repeated fed-batch penicillin fermentation

Structured models of antibiotic fermentation that quantify maturation and aging of product forming biomass are fitted to experimental data. Conditions of superiority of repeated fed batch cultivation are characterized on the basis of a performance criterion that includes penicillin productivity and costs of operation. Emphasis is placed on the relevance of such research to the model aided design of optimal cyclic operation.List of Symbols c IU/mg cost factor - D s^–1 dilution rate - J IU · cm^–3 · h^–1 net productivity - k _p IU · mg^–11 · h^–1 specific product formation rate - k _pm IU · mg^–1 · h^–1 maximum specific product formation rate - p IU/cm³ concentration of penicillin - T s final time of fermentation - t s fermentation time - X kg/m³ concentration of biomass dry weight - X ₁kg/m³ concentration of young, immature biomass - X ₂ kg/m³ concentration of mature product forming biomass - X _c kg/m³ biomass concentration of the end of growth phase - X _mkg/m³ maximum biomass concentration Greek Letters s^–1 specific maturation rate - s^–1 specific aging rate - s^–1 specific growth rate - _m s^–1 maximum specific growth rate - _p s^–1 specific growth rate during the product formation phase - s cycle time - % volume fraction of draw-off Abbreviations CC chemostat culture - RFBC repeated fed batch culture - RBC repeated batch culture     

Process engineering aspects of the bioleaching of copper ores

The bioleaching of minerals is a complex process that is affected by a number of biological, mineralogical, electrochemical and engineering factors. This work presents and discusses the most significant process engineering aspects involved in the bacterial leaching of copper ores, i.e. bacterial population, type of mineral and particle size, nutrients and inhibitors, oxygen and carbon dioxide, temperature and pH, leaching kinetics and operation mode.It is concluded that more work is needed in this area in order to gain a deeper insight in the many factors that govern this process. This would allow to significantly improve its overall productivity.List of Symbols C _L kg/m³ dissolved oxygen concentration - C ^* kg/m³ equilibrium oxygen concentration - d, e, f, g % percentage of C, H, O and N in the cell - D m impeller diameter - K consistency index - K _S, K₁, K_c constants - k _La h^–1 volumetric oxygen transfer coefficient - M _b mol/kg biomass apparent molecular weight - N s^–1 rotation frequency - n behavior index - P kg/m³ ungassed agitation power, product concentration - P _g kW/m³ gassed agitation power - p % pulp density - Q m³/h air flow rate - S kg/m³ limiting substrate concentration - W kg/(m³ · h) mass transfer rate per unit volume - X cells/cm³ biomass concentration - Y _o g cells/g Fe oxygen cell yield - Y _x g cells/g Fe substrate cell yield - h^–1 specific growth rate - _m h^–1 maximum specific growth rate     

A new technique for continuous measurement of the heat of fermentation

Summary A special temperature control system has been developed and applied to continuous measuring of the heat evolved during a fermentation process. In this system, the fermentation broth was overcooled by a given constant cooling water flow. The excess heat removed from the fermentor was then made up by an immersion electrical heater. The action of the temperature controller was precisely monitored as it varied in response to the amount of heat produced by the microbial activities.The technique was used for determining the heat evolution byEscherichia coli grown on glucose. The ratio between quantities of total heat release and total oxygen consumption has been determined to be 0.556 MJ/mol O₂.The newly developed technique can be employed as an online sensor to monitor the microbial activities of either aerobic or anaerobic fermentation systems.Symbols C_c Heat capacity of cooling water (MJ/kg · °C) - C_p Heat capacity (MJ/kg · °C) - I Current of immersion heater (A) - K Constant in Equation (2) (h) - K Constant in Equation (13) (m³ · h · °C/MJ) - Q_c Flow rate of cooling water (m³/h) - Heat of agitation (MJ/m³ · h) - Heat dissipated by the bubbling gas (MJ/m³ · h) - Heat removal by the action of controller (MJ/m³ · h) - Heat of fermentation (MJ/m³ · h) - Heat loss to the surroundings (MJ/m³ · h) - Q_pass Constant average power dissipated by the immersion heater (MJ/m³ · h) - Fluctuating power dissipated by the immersion heater (MJ/m³ · h) - Power dissipated by the immersion heater (MJ/m³ · h) - T Temperature of fermentation broth (°C) - Constant average temperature of fermentation broth (°C) - Fluctuating temperature of fermentation broth (°C) - T_a Temperature of the ambient air (°C) - T_c Inlet temperature of cooling water (°C) - U₁A₁ Specific heat transfer coefficient for determination of heat loss to the surroundings (MJ/m³ · h · °C) - U₂A₂ Specific heat transfer coefficient for cooling surfaces (MJ/m³ · h · °C) - U₃A₃ Constant in Equation (16) (MJ/m³ · h · °C) - V Voltage of immersion heater (V) - V_L Liquid volume (m³) - OUR Oxygen uptake rate (mol O₂/m³ · h) Greek Letters H_fo The ratio between the total heat release and the total oxygen uptake (MJ/mol O₂) - _c Density of cooling water (kg/m³) - Time constant defined in Equation (6) (h) - _iM_iC_pi Heat capacity of system components (fermentation broth + fermentor jar + stainless steel) (MJ/m³ · °C)     

Strain selection,medium development and scale-up of toyocamycin production by streptomyces chrestomyceticus

The batch productivity (Q _TM) of the production of the nucleoside antibiotic toyocamycin (TM) by Streptomyces chrestomyceticus was increased ten-fold by selection of a UV generated mutant, optimization of pH, increasing incubation temperature from 28 °C to 36 °C, and addition of soy oil. Initial high oxygen transfer rates stimulated Q _TM maxima two-fold. Antibiotic production by the mutant strain, U190, however, appeared more shear sensitive than the parent culture FCRF 341 with maximum antibiotic titer being inversely related to impellor tip velocity, T _v . For this reason, scale-up could not be done at constant P/V or constant volumetric oxygen transfer. Instead, programming of impeller speed was evaluated in order to maintain optimal impeller tip velocity during scale-up. It was found that a low constant T _v maintained in scale-up in geometrically similar vessels was most beneficial for duplication of optimal antibiotic productivity, Q _TM. Pilot fermentations (120 dm³ scale) were used to determine coefficients of Q _TM variation from oxygen uptake rate (OUR) and total CO₂ evolution data for monitoring of Q _TM variation during scale-up to the 12,000 dm³ scale. This technique allowed for on-line prediction of antibiotic titer and Q _TM from fermentor exhaust gas data.List of Symbols A scale constant - B shape constant - C location of maximum constant - D m impeller diameter (m) - H m liquid height (m) - OTR MmolO₂·(dm³)^–1min^–1 oxygen transfer rate - OUR MmolO₂·(dm³)^–1min^–1 oxygen uptake rate - PCV cm³ packed cell volume - P/V watts/dm³ volumetric power consumption - Q 1 · min^–1 corrected to standard conditions of temperature, pressure aeration rate - Q _TM g/(cm³ · h) or kg/(m³ · h) antibiotic productivity - T m tank diameter - T _mix s mixing time - T _v cm · s^–1 impeller tip velocity - TM g/cm³ Toyocamycin concentration - TNP Tricyclic nucleoside phosphate     

Growth of E. coli in a stirred tank and in an air lift tower reactor with an outer loop

E. coli ATCC 11105 was cultivated in a 10-1 stirred tank reactor and in a 60-1 tower loop reactor in batch and continuous operation. By on-line measurements of O₂ and CO₂ concentrations in the outlet gas, pH, temperature, cell mass concentration X as well as dissolved O₂ concentration along the tower in the broth, gas holdup, broth recirculation rate through the loop and by offline measurements of substrate concentration DOC and cell mass concentration along the tower, the maximum specific growth rate _m , yield coefficients Y _X/S. Y _X/DOC and were evaluated in stirred tank and tower loop in batch and continuous cultures with and without motionless mixers in the tower and at different broth circulation rates through the loop. To control the accuracy of the measurements the C balance was calculated and 95% of the C content was covered.The biological parameters determined depend on the mode of operation as well as on the reactor used. Furthermore, they depend on the recirculation rate of the broth and built-ins in the tower. The unstructured cell and reactor models are unable to explain these differences. Obviously, structured cell and reactor models are needed. The cell mass concentration can be determined on line by NADH fluorescence in balanced growth, if the model parameters are determined under the same operational conditions in the same reactor.List of Symbols a, b empirical parameters in Eq. (1) - CPR kg/(m³ h) CO₂ production rate - C kg/m³ concentration - D l/h dilution rate - DOC kg/m³ dissolved organic carbon - I net. fluorescence intensity - K _S kg/m³ Monod constant - k _L a l/h volumetric mass transfer coefficient - OTR kg/(m³ h) oxygen transfer rate - OUR kg/(m³ h) oxygen utilization rate - RQ = CPR/OUR respiratory quotient - S kg/m³ substrate concentration - t h,min, s time - t _u min recirculation time - t _M min mixing time - v m³/h volumetric flow rate through the loop - X kg/m³ (dry) cell mass concentration - Y _X/S yield coefficient of cell mass with regard to the consumed substrate - Y _X/DOC yield coefficient of the cell mass with regard to the consumed DOC - Y _X/O yield coefficient of the cell mass with regard to the consumed oxygen - Z relative distance in the tower from the aerator with regard to the height of the aerated broth - l/h specific growth rate - _m l/h maximum specific growth rate Indices f feed - e outlet     

Cross-flow filtration as a method of separating fungal cells and purifying the polysaccharide produced

Cross-flow filtration (CFF) has been investigated as a method of separating filamentously growing fungal cells and purifying the polysaccharide produced. The effects of transmembrane pressure, module geometry (e.g. channel height or tube diameter), tangential feed velocity and cell as well as polysaccharide concentration are discussed. Apart from these experiments, influences by the recirculation pump used are shown.List of Symbols b _f fouling index - b factor refering to the behaviour of the sublayer - C kg · m^–3 concentration - C _g kg · m^–3 solute concentration at the membrane - C _b kg · m^–3 solute concentration in the bulk phase - D s_-1 shear rate - k m · s^–1 mass-transfer coefficient - K mPa · sⁿ consistency index - n flow behaviour index - P _w m³ · s^–1 · m^–2 rate of permeation - P _w1 m³ · s^–1 · m^–2 rate of permeation at 1 minute - P _w m³ · s^–1 · m^–2 rate of permeation at the beginning - p Pa pressure - Q m² largest cross-section of a particle - q m² smallest cross-section of a particle - Re Reynolds number - R _f ^–1 fouling resistance - R _m m^–1 membrane resistance - t s time - w m · s^–1 tangential feed velocity Greek Symbols friction factor - pTM Pa transmembrane pressure - mPa · s shear viscosity - _sp specific viscosity (rel. increase of viscosity _sp=_rel-1) - [] m³· kg^–1 intrinsic viscosity - _w m² · s^–1 kinematic viscosity - kg · m^–3 density Indices b bulk - cell cells - f fouling - g gelling - PS polysaccharide - rel relative - sp specific - w water     

Fed-batch culture of hybridoma cells: comparison of optimal control approach and closed loop strategies

Control of fed-batch culture of hybridoma cells was investigated based on two approaches optimal control theory and feedback control. Experiments were conducted for both approaches-with a feed enriched in glutamine. The optimal feed trajectory, a decreasing one, yielded a final monoclonal antibody (MAb) concentration of 170 mg/l, a three-fold increase compared to a typical batch operation.The feedback strategy relied on the on-line estimation of the net specific growth rate of cells from the measurement of the CO₂ production rate with a mass-spectrometer. A PI controller was then used to maintain the growth rate at a desired value by adjusting the dilution rate to the reactor. For the chosen set-point (0.1 d^–1), the final MAb concentration achieved was about 100 mg/1. It was found that there was a delay in the assimilation of the glutamine that should be included in the model to explain the lower MAb production in feedback mode. A higher production can be expected also for a lower set-point in feedback operation.List of Symbols Amm mM ammonia concentration - CPR l/(ld) carbon dioxide production rate - D _t l/d dilution rate - e _t l/d control error - F L/d feed flow rate - Glc mM glucose concentration - Gln mM glutamine concentration - Lac mM lactate concentration - I mg performance index - k _d l/d specific death rate - K _damm l/(mM · d) kinetic parameter for death rate - K _dgln mM kinetic parameter for death rate - K _dlac l/(mM·d) kinetic parameter for death rate - K _c l controller gain - K _glc mM kinetic parameter for growth rate - K _gln mM kinetic parameter for growth rate - K _tr L/(cell·d) transport coefficient - K l/d kinetic parameter for Mab production - m _glc mM/(cell·d) maintenance coefficient - M _Ab mg/l monoclonal antibody concentration - P _t covariance matrix - q _glc l/(l·cell·d) specific CO₂ production rate - q _glc mM/(cell·d) specific glucose uptake rate - q _gln mM/(cell·d) specific glutamine uptake rate - q _Mab mg/(l·cell·d) specific monoclonal antibody production - t _f d final culture time - T d sampling rate - u control input - V l reactor volume - X cell/l total cells concentration - X _v cell/l viable cells concentration - Y yield coefficient Greek mg/cell variable yield coefficient - ₀ mg/(cell·d) growth-associated kinetic parameter - mg/(cell·d) non growth-associated kinetic parameter - _t+1 defined by Eq. (19) - forgetting factor - l/d specific growth rate - _max l/d specific growth rate - _i d controller integral time constant