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     

Mathematical modeling of production of microbial lipids

As a part of the investigations on the microbial lipid production using the yeast Rhodotorula gracilis, CFR-1, kinetics of the biomass synthesis has been studied using shake flask experiments. Using a medium containing a carbon to nitrogen ratio of 701, the rates of biomass production were followed at different initial substrate concentrations in the range of 20–100 kg/m³. A logistic model was found to be reasonably adequate to describe the kinetics of the growth of biomass; the maximum specific growth rate of 0.105 h^–1 was applicable for substrate concentrations less than 60 kg/m³, which gave reasonable agreement between predicted and actual biomass concentration values.List of Symbols S ₀, X ₀ kg/m³ Initial concentrations of sugar, non lipid biomass respectively - X, X(t) kg/m³ Concentrations of non lipid biomass at any time t - dX/dt kg/(m³ · h) Rate of biomass growth - h^–1 Specific growth rate - _max h^–1 Maximum specific growth rate - K _s mol/dm³ Monods constant - X _max kg/m³ Maximum biomass reached in a run     

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 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     

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     

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     

Ethanol production with Zymomonas mobilis with synthetic medium in batch operation

Ethanol was produced with Zymomonas mobilis Z6 (ATCC 29191), in batch culture with synthetic medium on glucose as substrate and in the presence of aspartate. The concentrations of glucose, phosphate, ammonium, ethanol and dissolved O₂ and CO₂ in the medium and O₂ and CO₂ in the outlet gas as well as the cell mass by culture fluorescence were measured on-line. Cell mass, glucose and aspartate concentrations were measured off-line. In the presence of a sufficient amount of aspartate, the ethanol inhibition effect can be reduced considerably. However, the improvement with yeast extract is more incisive. The relationship between the intensity of culture fluorescence and cell mass concentration is linear, if sufficient aspartate is present.List of Symbols ASP kg/m³ aspartate concentration - CTR kg/(m³ · h) CO₂ transfer rate - N, NH₄ kg/m³ nitrogen concentration from NH ₄ ⁺ - P kg/m³ product (ethanol) concentration - p% product (ethanol) yield - PO₄ kg/m³ phosphate concentration - Q _E kg/(kg · h) specific ethanol production rate - kg/(kg · h) specific nitrogen uptake rate from NH ₄ ⁺ - Q _P kg/(kg · h) specific phosphate uptake rate - Q _s kg/(kg · h) specific substrate (glucose) uptake rate - S kg/m³ glucose concentration - S _O kg/m³ initial glucose concentration - Y _x/s kg/kg yield coefficient - h^–1 specific growth rate     

The fermentation of hexose and pentose sugars by Candida shehatae and Pichia stipitis

Summary The fermentation by Candida shehatae and Pichia stipitis of xylitol and the various sugars which are liberated upon hydrolysis of lignocellulosic biomass was investigated. Both yeasts produced ethanol from d-glucose, d-mannose, d-galactose and d-xylose. Only P. stipitis fermented d-cellobiose, producing 6.5 g·l^-1 ethanol from 20 g·l^-1 cellobiose within 48 h. No ethanol was produced from l-arabinose, l-rhamnose or xylitol. Diauxie was evident during the fermentation of a sugar mixture. Following the depletion of glucose, P. stipitis fermented galactose, mannose, xylose and cellobiose simultaneously with no noticeable preceding lag period. A similar fermentation pattern was observed with C. shehatae, except that it failed to utilize cellobiose even though it grew on cellobiose when supplied as the sole sugar. P. stipitis produced considerably more ethanol from the sugar mixture than C. shehatae, primarily due to its ability to ferment cellobiose. In general P. stipitis exhibited a higher volumetric rate and yield of ethanol production. This yeast fermented glucose 30–50% more rapidly than xylose, whereas the rates of ethanol production from these two sugars by C. shehatae were similar. P. stipitis had no absolute vitamin requirement for xylose fermentation, but biotin and thiamine enhanced the rate and yield of ethanol production significantly.Nomenclature _max Maximum specific growth rate, h^-1 - Q _p Maximum volumetric rate of ethanol production, calculated from the slope of the ethanol vs. time curve, g·(l·h)^-1 - q _p Maximum specific rate of ethanol production, g·(g cells·h) - Y _p/s Ethanol yield coefficient, g ethanol·(g substrate utilized)^-1 - Y _x/s Cell yield coefficient, g biomass·(g substrate utilized)^-1 - E Efficiency of substrate utilization, g substrate consumed·(g initial substrate)^-1·100     

Organic reduction of tapioca wastewaters using modified RBC

A modified Rotating Biological Contactor (RBC) was used for the treatability studies of synthetic tapioca wastewaters. The RBC used was a four stage laboratory model and the discs were modified by attaching porous nechlon sheets to enhance biofilm area. Synthetic tapioca wastewaters were prepared with influent concentrations from 927 to 3600 mg/l of COD. Three hydraulic loads were used in the range of 0.03 to 0.09 m³·m^–2·d^–1 and the organic loads used were in the range of 28 to 306 g COD· m^–2·d^–1. The percentage COD removal were in the range from 97.4 to 68. RBC was operated at a rotating speed of 18 rpm which was found to be the optimal rotating speed. Biokinetic coefficients based on Kornegay and Hudson models were obtained using linear analysis. Also, a mathematical model was proposed using regression analysis.List of Symbols A m² total surface area of discs - d m active depth of microbial film onany rotating disc - K _s mg ·l^–1 saturation constant - P mg·m^–2·^–1 area capacity - Q l·d^–1 hydraulic flow rate - q m³·m^–2·d^–1 hydraulic loading rate - S ₀ mg·l^–1 influent substrate concentration - S _e mg·l^–1 effluent substrate concentration - w rpm rotational speed - V m³ volume of the reactor - X _f mg·l^–1 active biomass per unit volume ofattached growth - X _s mg·l^–1 active biomass per unit volume ofsuspended growth - X mg·l^–1 active biomass per unit volume - Y _s yield coefficient for attachedgrowth - Y _A yield coefficient for suspendedgrowth - Y yield coefficient, mass of biomass/mass of substrate removed Greek Symbols hr mean hydraulic detention time - (_max)_A d^–1 maximum specific growth rate forattached growth - (_max)_s d^–1 maximum specific growth rate forsuspended growth - _max d^–1 maximum specific growth rate - d^–1 specific growth rate - _v mg·l^–1·hr^–1 maximum volumetric substrateutilization rate coefficient     

Kinetics of inhibition in propionic acid fermentation

The inhibitory effect of propionic acid P and biomass concentration X is studied in batch and continuous fermentations with cell recycle.In batch fermentations, the specific growth rate decreases and cancels out at a critical propionic acid concentration Pc ₁; the formerly decreasing specific production rate becomes constant after Pc ₁ and cancels out when a second critical propionic acid concentration Pc ₂ is reached.In continuous fermentation with cell recycle, a similar inhibition is observed with biomass. The specific rates decrease and become constant at a critical biomass concentration Xc. They cancel out at different high biomass concentrations.In both cases, the specific production rate can be related to the specific growth rate by the Luedeking and Piret expression: =+, [1], where the constants and are determined by the fermentation parameters.List of Symbols t h time - X kg/m³ biomass concentration - P kg/m³ propionic acid concentration - A kg/m³ acetic acid concentration - S kg/m³ lactose concentration - dX/dt kg/(m³h) instantaneous rate of cell growth - dP/dt kg/(m³h) instantaneous rate of propionic acid production - h^–1 specific growth rate - h^–1 specific propionic acid production rate - D h^–1 dilution rate     

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     

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     

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

The scale-down procedure seems an adequate tool in the design, optimization and scale-up fermentation processes. The first step in this procedure is a theoretical analysis, called process analysis, which is based on characteristic times of the mechanisms which may influence the performance of the bioreactor. This analysis must give information about the behaviour of large and small scale fermentation processes. At a small scale a verification of the results of such an analysis of the fed-batch baker's yeast production is carried out.In this paper a comparison of calculated and measured characteristic times of liquid mixing and mass transfer is presented. It was concluded that the literature correlations give a rough estimation of the characteristic times and can be used in the process analysis. Depending on the kind of sparger, the medium and the scale of the reactor, more knowledge is needed about bubble coalescence in fermentation media.The volumetric oxygen transfer coefficient increased when the biomass concentration increased. Probably this is caused by the interaction between biomass and the anti-foaming agent used.List of Symbols C kg/m³ concentration - D m diameter - m²/s effective dispersion coefficient - d m holes of the sparger - g m/s² gravitational acceleration - H m height - k _L a s^–1 volumetric mass transfer coefficient based on the liquid volume - L m length - m kg/kg gas liquid distribution coefficient - OTR kg/(m³ · s) oxygen transfer rate - OUR kg/(m³ · s) oxygen uptake rate - t s time - ^s m/s superficial gas flow rate - m length - s time constant - _g m³/s gas flow rate Indices 0 value at t=0 - cal calculated - e value at t=t (end) - g gas phase - in flow going to the fermentor - l liquid phase - m mixing - mt mass transfer - O ₂ oxygen - out flow coming out the fermentor     

Intraparticle mass-transfer resistance and apparent time stability of immobilized yeast alcohol dehydrogenase

The stability and, consequently, the lifetime of immobilized enzymes (IME) are important factors in practical applications of IME, especially so far as design and operation of the enzyme reactors are concerned. In this paper a model is presented which describes the effect of intraparticle diffusion on time stability behaviour of IME, and which has been verified experimentally by the two-substrate enzymic reaction. As a model reaction the ethanol oxidation catalysed by immobilized yeast alcohol dehydrogenase was chosen. The reaction was performed in the batch-recycle reactor at 303 K and pH-value 8.9, under the conditions of high ethanol concentration and low coenzyme (NAD⁺) concentration, so that NAD⁺ was the limiting substrate. The values of the apparent and intrinsic deactivation constant as well as the apparent relative lifetime of the enzyme were calculated.The results show that the diffusional resistance influences the time stability of the IME catalyst and that IME appears to be more stabilized under the larger diffusion resistance.List of Symbols C _A, C_B, C_E mol · m^–3 concentration of coenzyme NAD⁺, ethanol and enzyme, respectively - C _p mol · m³ concentration of reaction product NADH - d _p mm particle diameter - D _eff m² · s^–1 effective volume diffusivity of NAD⁺ within porous matrix - k _d s^–1 intrinsic deactivation constant - K _A, K_A, K_B mol · m^–3 kinetic constant defined by Eq. (1) - K _A ^x mol · m^–3 kinetic constant defined by Eq. (5) - r _A mol · m^–3 · s^–1 intrinsic reaction rate - R m particle radius - R _v mol · m^–3 · s^–1 observed reaction rate per unit volume of immobilized enzyme - t _E s enzyme deactivation time - t _r s reaction time - V mol · m^–3 · s^–1 maximum reaction rate in Eq. (1) - V ^x mol · m^–3 · s^–1 parameter defined by Eq. (4) - V _f m³ total volume of fluid in reactor - w _s kg mass of immobilized enzyme bed - factor defined by Eqs. (19) and (20) - kg · m^–3 density of immobilized enzyme bed - unstableness factor - effectiveness factor - Thiele modulus - relative half-lifetime of immobilized enzyme Index o values obtained with fresh immobilized enzyme     

A mathematical model for solid state fermentation in tray bioreactors

A simple mathematical model for the interaction of mass transport with biochemical reaction in solid state fermentations (SSF) in static tray type bioreactors under isothermal conditions has been developed. The analysis has enabled scientific explanations to a number of practical observations, through the concept of critical substrate bed thickness. The model will be most useful in the prediction of the concentration gradients as also in efficient design of these bioreactors.List of Symbols C g/cm³ Oxygen concentration in the bed - C _g g/cm³ Atmospheric oxygen concentration - C ^* Dimensionless oxygen concentration, C/C _g - D _e cm²/h Effective diffusivity - H cm Bed thickness or height - H _c cm Critical bed thickness or height - H _m cm Maximum height of zone of zero oxygen concentration - p _i mg/(g · h) Productivity (Eq. 13) - R g/(cm³ · h) Biochemical reaction rate - t h Fermentation time - t ^* Dimensionless time, D _e t/H² - X mg/cm³ Biomass concentration - X _max mg/cm³ Maximum biomass concentration - y Dimensionless thickness or height, (y = z/H) - y cm Thickness of zone of zero oxygen concentration (Eq. 12) - Y Yield coefficient - z cm Bed thickness or height along tray axis - Bed void fraction - _max h^–1 Specific growth rate - Thiele modulus      

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     

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     

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      

Simultaneous hydrolysis of tri- and tetrasaccharides by industrial mixtures of glucoamylase and α-amylase: kinetics and thermodynamics

The present paper deals with the study of the kinetics and thermodynamics of batch enzymic hydrolysis of multisubstrate media in the presence of more than one glucanase. A kinetic model, similar to the one proposed for competitive inhibition, is presented to describe the competition between two substrates, tri- and tetrasaccharides, for the same enzyme, glucoamylase. A literature research on the most recurrent values of the Michaelis-Menten constant also shows a linear relationship between this parameter and the molecular weight of the sugar substrate for a given glucanase.List of Symbols a dimensionless empirical parameter in Eq. (1) - b dimensionless empirical parameter in Eq. (1) - B dimensionless empirical parameter in Eq. (6) - E kg/m³ enzyme concentration - E _a kcal/mol activation energy - kc kg/m³ competition constant - k _M kg/m³ Michaelis-Menten constant - MW kg·10^–3 molecular weight - r mol/(min·kg) specific hydrolysis rate of glucoamylase - R cal/(°K·mol) ideal gas constant - S kg/m³ substrate concentration - T °K absolute temperature - v kg/(m³·h) hydrolysis rate of glucoamylase Indices 1 values referring to trisaccharides - 2 values referring to tetrasaccharides - 0 starting values - max maximum values     

Verification studies of a simplified model for the removal of dichloromethane from waste gases using a biological trickling filter

The removal of dichloromethane from waste gases in a biological trickling filter was studied experimentally as well as theoretically within the concentration range of 0–10,000 ppm. A stable dichloromethane elimination performance was achieved during two years of operation, while the start-up of the system only amounted to several weeks at constant inlet concentrations. The trickling filter system was operated co-currently as well as counter-currently.However, experimental and theoretical results revealed that the relative flow direction of the mobile phases did not significantly affect the elimination performance. Moreover, it was found that the gas-liquid mass-transfer resistance in the trickling filter bed applied was negligible, which leaves the biological process inside the biofilm to be the rate limiting step.A simplified model was developed, the Uniform-Concentration-Model, which showed to predict the filter performance close to the numerical solutions of the model equations. This model gives an analytical expression for the degree of conversion and can thus be easily applied in practice.The dichloromethane eliminating performance of the trickling filter described in this paper, is reflected by a maximum dichloromethane elimination capacity EC _max=157 g/(m³ · h) and a critical liquid concentration C _lcr=45 g/m³ at a superficial liquid velocity of 3.6 m/h, inpendent of the gas velocity and temperature.List of Symbols a _s m²/m³ specific area - a _w m²/m³ specific wetted area - A m² cross-sectional area - C _g g/m³ gas phase concentration - C _go g/m³ inlet gas phase concentration - C _gocr g/m³ critical gas phase concentration - C _g ^* C_g/C_go dimensionless gas concentration - C _l g/m³ liquid concentration - C _lcr g/m³ critical liquid concentration - C _lcr ^* mC_lcr/C_go dimensionless critical concentration - c _li g/m³ substrate concentration at liquid-biofilm interface - C _l ^* mC_l/C_go dimensionless liquid concentration - C _o g/m³ oxygen concentration inside the biofilm - C _oi g/m³ oxygen concentration at liquid-biofilm interface - C_s g/m³ substrate concentration inside the biofilm - C _si g/m³ substrate concentration at liquid-biofilm interface - D _eff m²/h effective diffusion coefficient in the biofilm - D _o m²/h effective diffusion coefficient for oxygen in the biolayer - E mu_g/u_l extraction factor - E _act kJ/mol activation energy for the biological reaction - EC g/(m³· h) K _o a _w : elimination capacity, or the amount of substrate degraded per unit of reactor volume and time - EC _max g/(m³ · h) K _o a_w: maximum elimination capacity - f degree of conversion - h m coordinate in height - H m height of the packed bed - K ₀ g/(m³ · h) _maxX_b/Y zeroth order reaction defined per unit of biofilm volume - k _og m/h overall gas phase mass transfer coefficient - K ^* dimensionless constant given by Eq. (A.5) - K _l ^* dimensionless constant given by Eq. (A.6) - K ₂ ^* dimensionless constant given by Eq. (A.6) - m C _g /C_l gas liquid distribution coefficient - N g/(m² · h) liquid-biofilm interfacial flux of substrate - N _og k_oga_wH/u_g number of gas phase transfer units - N _r k_o a_w H/u_g C_go number of reaction units - OL g/(m³· h) u _g C _go /H organic load - r _s g/(m³ ·h) zeroth order substrate degradation rate given by Eq. (1) - R _s g/(g TSS ·h) specific activity - T K absolute temperature - u _g m/h superficial gas velocity - u _t m/h superficial liquid velocity - X _b g TSS/m₃ biomass concentration inside biofilm - X _s g TSS/m₃ liquid suspended biomass concentration - x m coordinate inside the biofilm - Y g TSS/(g_DCM) yield coefficient Greek Symbols dimensionless parameter given by Eq. (2) - m averaged biofilm thickness - biofilm effectiveness factor given by Eqs. (7a)–(7c) - m penetration depth of substrate into the biofilm - _max d^–1 microbiological maximum growth rate - v _o stoichiometric utilization coefficient for oxygen - v _s stoichiometric utilization coefficient for substrate - dimensionless height in the filter bed - h H/u _g superficial gas phase contact time - _o (K ₀ /DC _ii )^1/2 - _o C _o /C _oi dimensionless oxygen concentration inside the biofilm - _s C _s /C _si dimensionless substrate concentration inside the biofilm Experimental results, verifying the model presented will be discussed Part II (to be published in Vol. 6, No. 4)