Anaerobic digestion of palm oil mill effluent and condensation water waste: an overall kinetic model for methane production and substrate utilization

The process of anaerobic digestion is viewed as a series of reactions which can be described kinetically both in terms of substrate utilization and methane production. It is considered that the rate limiting factor in the digestion of complex wastewaters is hydrolysis and this cannot be adequately described using a Monod equation. In contrast readily assimilable wastewaters conform well to this approach. A generalized equation has thus been derived, based on both the Monod and Contois equations, which serves extreme cases. The model was verified experimentally using continuous feed anaerobic digesters treating palm oil mill effluent (POME) and condensation water from a thermal concentration process. POME represents a complex substrate comprising of unhydrolyzed materials whereas the condensation water is predominantly short chain volatile fatty acids. Substrate removal and methane production in both cases could be predicted accurately using the generalized equation presented.List of Symbols A (=K_skY/K_h) Kinetic parameter - B Specific methane yield, 1 of CH₄/g of substrate added B₀ Maximum specific methane yield, 1 of CH₄/g of substrate added at infinity - C Empirical constant in Contois equation - F Volumetric substrate removal rate, g/l day - k Hydrolysed substrate transport rate coefficient, 1/days - K (=YC) Kinetic parameter in Chen-Hashimoto equation - K _h Substrate hydrolysis rate coefficient, 1/days - K _s Half-saturation constant for hydrolysed substrate, g/l - M _v Volumetric methane production rate, 1 of CH₄/l day - MS Mineral solids, g/l - MSS Mineral suspended soilds, g/l - POME Palm oil mill effluent - R (=S_r/S_T0) Refractory coefficient - S _h Concentration of hydrolysed substrate, g/l - S _u Intracellular concentration of hydrolysed substrate, g/l - S ₀ Input biodegradable substrate concentration, g/l - S Biodegradable substrate concentration in the effluent or in the digester, g/l - S _r Refractory feed substrate concentration, g/l - S _T0 (=S₀+S_r) Total feed substrate concentration, g/l - S _T (S+S_r) Total substrate concentration in the effluent, g/l - TS Total solids, g/l - TSS Total suspended solids, g/l - VFA Total volatile fatty acids, g/l - VS Volatile solids, g/l - VSS Volatile suspended solids, g/l - X Biomass concentration, g/l - Y Biomass yield coefficient, biomass/substrate mass - Hydraulic retention time, days. - Specific growth rate of microorganisms, l/days - _m Maximum specific growth rate of microorganisms, l/days The authors wish to express their gratitude to the Departamento de Postgrado y Especialización del CSIC and to the Consejería de Educación y Ciencia de la Junta de Andalucia for their financial support of this work.     

A dynamic river model for biodegradability studies

The objective of this publication is to present a new dynamic aerobic biodegradation test method simulating a river. A laboratory cascade test system and standardized batch shake flask tests were used for biodegradation studies with the non-volatile and non-sorbing model compounds 2,4-dinitrophenol, naphthalene-1-sulphonic acid and sulphanilic acid. To be closer to the often very low concentrations of substances in the environment the concentrations of the compounds used were standard test concentrations and lower. ¹⁴C labelled compounds were measured at 50 g/l, capillary electrophoresis at 5000 g/l and the removal of dissolved organic carbon at 50000 g/l. The test results obtained confirmed the known ultimate biodegradability of the test compounds and showed that biodegradation degrees, rates and degradation durations depended on the test systems, the concentrations of test compounds and the inocula. The river model is a suitable simulation test for natural dynamic surface waters which can be used to perform biodegradability studies at low test concentrations if adequate analytical tools, preferably radioactive-labelled substances, are available.Abbreviations BOD biochemical oxygen demand - DAWT DOC-die-away test - DNP 2,4-dinitrophenol - DOC dissolved organic carbon - E effluent of laboratory wastewater treatment plants - MOST modified OECD screening test - NSA naphthalene-1-sulphonic acid - P pond water - SAA sulphanilic acid (=4-amino benzene sulphonic acid)     

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     

Biodegradation of phenol by arthrobacter and modelling of the kinetics

The toxic effects of phenol, a common constituent of many industrial effluents, necessitates treatment of the polluted streams. Biodegradation is a popular technique and enjoys many advantages. The degradation of phenol with Arthrobacter species is studied in batch cultures and it is observed that the substrate is inhibiting. The fit of various models, including the model proposed earlier by us [17], to the experimental data is studied. The model is used to fit available data in literature, which unfortunately is very sparse. In all the cases the present model fits the data best.List of Symbols S mg/l substrate concentration - S ₀ mg/l threshold substrate concentration - K _I mg/l inhibition constant - K _m , K _s mg/l half saturation constant of growth kinetics - m, n constants - 1/h specific growth rate - _m 1/h maximal specific growth rate - X mg/l biomass concentration at time t - X ₀ mg/l initial biomass concentration Abbreviations MTCC Microbial Type Culture Collection - IMTECH Institute of Microbial Technology The cooperation of the staff of the Biosciences and Biotechnology Center, I.I.T. Madras is greatly appreciated.     

A modified unstructured mathemathical model for the penicillin G fed-batch fermentation

Summary In this paper, an updated unstructured mathematical model for the penicillin G fed-batch fermentation is proposed, in order to correct some physical and biochemical shortcomings in the model of Heijnen et al. (1979,Biotechnol. Bioeng.,21, 2175–2201) and the model of Bajpai and Reuß (1980,J. Chem. Tech. Biotechnol.,30, 332–344). Its main features are the consistency for all values of the variables, and the ability to adequately describe different metabolic conditions of the mould. The model presented here can be considered as the translation of the latest advances in the biochemical knowledge of the penicillin biosynthesis.Nomenclature t time (h) - S amount of substrate in broth (g) - X amount of cell mass in broth (g) - P amount of product in broth (g) - V fermentor volume (L) - F input substrate feed rate (L/hr) - C _s S/V substrate concentration in broth (g/L) - C _x X/V cell mass concentration in broth (g/L) - C _P P/V product concentration in broth (g/L) - s _F substrate concentration in feed stream (g/L) - E _m parameter related to the endogenous fraction of maintenance (g/L) - E _p parameter related to the endogenous fraction of production (g/L) - K _x Contois saturation constant for substrate limitation of biomass production (g/g DM) - K _s Monod saturation constant for substrate limitation of biomss production (g/L) - K _p saturation constant for substrate limitation of product formation (g/L) - K _i substrate inhibition constant for product formation (g/L) - m _s maintenance constant (g/g DM hr) - k _h penicillin hydrolysis or degradation constant (hr^–1) - Y _x/s cell mass on substrate yield (g DM/g) - Y _p/s product on substrate yield (g/g) - specific substrate consumption rate (g/g DM hr) - specific growth rate (hr^–1) - _substr specific substrate to biomass conversion rate (hr^–1) - _x maximum specific substrate to biomass conversion rate (hr^–1) - specific production rate (g/g DM hr) - _p specific production constant (g/g DM hr)     

Studies on the formation of long-chain dicarboxylic acids from puren-alkanes by a mutant ofCandida tropicalis

Summary Individualn-alkanes, from C₁₁–C₁₆, were metabolized by a mutant ofCandida tropicalis. This strain was selected for its inability to grow in the presence of dodecanedioic acid and dodecane as the sole carbon source. Transformations were studied in fed-batch cultures. Undecane was only poorly transformed, but from dodecane to hexadecane high transformation yields were achieved. Maximum yield of acid-precipitable long-chain dioic acids was obtained with tridecane as substrate. All the products were mixtures of different acids. Besides the ,-alkanedioic acids, the 3-hydroxy derivatives of long-chain ,-alkanedioic acids and dioic acids with a shortened carbon chain were found.     

Enzyme production in a cell recycle fermentation system evaluated by computer simulations

Enzyme production in a cell recycle fermentation system was studied by computer simulations, using a mathematical model of -amylase production by Bacillus amyloliquefaciens. The model was modified so as to enable simulation of enzyme production by hypothetical organisms having different production kinetics at different fermentation conditions important for growth and production. The simulations were designed as a two-level factorial assay, the factor studied being fermentation with or without cell recycling, repression of product synthesis by glucose, kinetic production constants, product degradation by a protease, mode of fermentation, and starch versus glucose as the substrate carbon source.The main factor of importance for ensuring high enzyme production was cell recycling. Product formation kinetics related to the stationary growth phase combined with continuous fermentation with cell recycling also had a positive impact. The effect was greatest when two or more of these three factors were present in combinations, none of them alone guaranteeing a good result. Product degradation by a protease decreased the amount of product obtained; however, when combined with cell recycling, the protease effect was overshadowed by the increased production. Simulation of this type should prove a useful tool for analyzing troublesome fermentations and for identifying production organisms for further study in integrated fermentation systems.List of Symbols a proportionality constant relating the specific growth rate to the logarithm of G (h) - a ₁ reaction order with respect to starch concentration - a ₂ reaction order with respect to glucose concentration - c starch concentration (g/l) - c ₀ starch concentration in the feed (g/l) - D dilution rate (h^–1) - e intrinsic intracellular amylase concentration (g product/g cell mass) - E extracellular amylase concentration (g/l) - F volumetric flow rate (l/h) - G average number of genome equivalents of DNA/cell - K ₁ intracellular repression constant - K ₂ intracellular repression constant - K _s Monod saturation constant (g/l) - k ₃ product excretion rate constant (h^–1) - k _I translation constant (g product/g mRNA/h) - k _d first order decay constant (h^–1) - k _dw first order decay constant (h^–1) - k _gl rate constant for glucose production (g/l/h) - k _{m, dgr} saturation constant for product degradation (g/l) - k _st rate constant for starch hydrolysis (g/l/h) - k _t1 proportionality constant for amylase production (g mRNA/g substrate) - k _t2 proportionality constant for amylase production (g mRNA *h/g substrate) - k _w protease excretion rate constant (h^–1) - k _wt1 proportionality constant for protease production (g mRNA/g substrate) - k _wt2 proportionality constant for protease production (g mRNA *h/g substrate) - k _wI translation constant (g protease/g mRNA/h) - m maintenance coefficient (g substrate/g cell mass/h) - n number of binding sites for the co-repressor on the cytoplasmic repressor - Q repression function, K₁/K₂ less than or equal to 1.0 - Q _w repression function, K₁/K₂ less than or equal to 1.0 - r intrinsic amylase mRNA concentration (g mRNA/g cell mass) - r _m intrinsic protease mRNA concentration (g mRNA/g cell mass) - R _ex retention by the filter of the compounds x=: C starch, E amylase, or S glucose - R _t amylase transport rate (g product/g cell mass/h) - R _wt protease transport rate (g protease/g cell mass/h) - R _s rate of glucose production (g/l/h) - R _c rate of starch hydrolysis (g/l/h) - S ₀ feed concentration of free reducing sugar (g/l) - s extracellular concentration of reducing sugar (g/l) - t time (h) - V volume (1) - w intracellular protease concentration (g/l) - W extracellular protease concentration (g/l) - X cell mass concentration (dry weight) (g/l) - Y yield coefficient (g cell mass/g substrate) - substrate uptake (g substrate/g cell mass/h) - specific growth rate of cell mass (h^–1) - _d specific death rate of cells (h^–1) - _m maximum specific growth rate of cell mass (h^–1) - _m,dgr maximum specific rate of amylase degradation (h^–1) This study was supported by the Nordic Industrial Foundation Bioprocess Engineering Programme and the Center for Process Biotechnology, The Technical University of Denmark.     

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

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

Optimal control of continuous fermentation processes

Three layer control structure is proposed for optimal control of continuous fermentation processes. The start-up optimization problems are solved as a first step for optimization layer building. A steady state optimization problem is solved by a decomposition method using prediction principle. A discrete minimum time optimal control problem with state delay is formulated and a decomposition method, based on an augmented Lagrange's function is proposed to solve it. The problem is decomposed in time domain by a new coordinating vector. The obtained algorithms are used for minimum time optimal control calculation of Baker's Yeast fermentation process.List of Symbols x(t) g/l biomass concentration - s(t) g/l limiting substrate concentration - x ⁰ g/l inlet biomass concentration - s ⁰(t) g/l inlet substrate concentration - D(t) h^–1 dilution rate - (t) h^–1 specific growth rate - Y g/g yield coefficient - (t) h^–1 specific limiting substrate consumption rate - k _D h^–1 disappearing constant - w ₁, w ₂ known constant or piece-wise disturbances - _m h^–1 maximum specific growth rate - k _s g/l Michaelis-Menten's parameter - h time delay - x ₀, s ₀ g/l initial concentrations - ¯x, ¯s, ¯D optimal steady state value - V _min , V _max , v=x,s,d,t bounds of variables - t h sampling period - K number of steps in the optimization horison - Js, J _d performance indexes - L _s Lagrange's function - L _d Lagrange's functional - ₀ weighting coefficient for the amount of the limiting substrate throwing out of the fermentor - ₁, ₂ dual variables of Lagrange's function - steps in steady state coordination procedure - errors values for steady state coordination process - _v , v=x, s conjugate variables of Lagrange's functional - _v , v=x,s penalty coefficients of augmented Lagrange's functional - _v , v=x, s interconnections of the time - e _v , v=x,s, D, _x , _s gradients of Lagrange's functional - j, l indexes of calculation procedures - values of errors in calculations The researches was supported by National Scientific Research Foundation under grants No NITN428/94 and No NITN440/94     

Effect of time delay in specific growth rate on the periodic operation of bioreactors with input multiplicities

The effect of time delay in specific growth rate () on the periodic operation of bioreactors with input multiplicities is theoretically analyzed for productivity improvement. A periodic rectangular pulse is applied either in feed substrate concentration (S_f) or in dilution rate (D). Periodic operation under feed substrate concentration cycling gives improvement in productivity at lower value of ¯S_f of the two steady-state multiplicities of S_f only when the time delay in is larger. Whereas the larger value of ¯S_f gives improvement in average productivity for all values of time delay. Dilution rate (D) cycling gives an improvement in average productivity particularly for larger time delay in . This improvement in average productivity is obtained only at smaller value of dilution rate out of the two steady-state input multiplicities of D.List of Symbols D 1/h dilution rate - F memory function - g dummy variable - K_i g/l substrate inhibition constant - K_m g/l substrate saturation constant - P g/l product concentration - P_m g/l product saturation constant - Q g/(hl) product cell produced per unit time - S g/l substrate concentration - S_f g/l feed substrate concentration - S_f,p g/l feed substrate concentration during fraction of a period - X g/l biomass concentration - Y_X/S g/g cell mass yield - w variable either S or Z - Z g/l weighted average of substrate concentration Greek Letters 1/h time delay parameter - ₁ , ₂ product yield parameters, g/g and 1/h - pulse width expressed as a fraction of a period - 1/h specific growth rate - _m 1/h maximum specific growth rate - h period of oscillation - – average value     

Lactic acid production by batch fermentation of whey permeate: A mathematical model

Summary The batch fermentation of whey permeate to lactic acid was improved by supplementing the broth with enzyme-hydrolyzed whey protein. A mathematical model based on laboratory results predicts to a 99% confidence limit the kinetics of this fermentation. Cell growth, acid production and protein and sugar use rates are defined in quantifiable terms related to the state of cell metabolism. The model shows that the constants of the Leudeking-Piret model are not true constants, but must vary with the medium composition, and especially the peptide average molecular weight. The kinetic mechanism on which the model is based also is presented.Nomenclature K _i lactic acid inhibition constant (g/l) - K _pr protein saturation constant during cell growth (g/l) - K _pr protein saturation constant during maintenance (g/l) - K _s lactose saturation constant (g/l) - [LA] lactic acid concentration (g/l) - [PR] protein concentration (g/l) - [S] lactose concentration (g/l) - t time (h) - [X] cell mass concentration (g/l) - , fermentation constants of Leudeking and Piret - specific growth rate (l/h) - Y _{g, LA/S} acid yield during cell growth (g acid/g sugar) - Y _{m, LA/S} acid yield during maintenance (g acid/g sugar) - Y _x/pr yield (g cells/g protein) - specific sugar use rate during cell growth (g sugar/h·g cell) - specific sugar use rate during maintenance (g sugar/h·cell)     

Batch xylitol production by<Emphasis Type="Italic"> Candida guilliermondii</Emphasis> FTI 20037 from sugarcane bagasse hemicellulosic hydrolyzate at controlled pH values

Batch fermentation of sugarcane bagasse hemicellulosic hydrolyzate by the yeast Candida guilliermondii FTI 20037 was performed using controlled pH values (3.5, 5.5, 7.5). The maximum values of xylitol volumetric productivity (Q _p=0.76 g/l h) and xylose volumetric consumption (Q _s=1.19 g/l h) were attained at pH 5.5. At pH 3.5 and 7.5 the Q _p value decreased by 66 and 72%, respectively. Independently of the pH value, Y _x/s decreased with the increase in Y _p/s suggesting that the xylitol bioconversion improves when the cellular growth is limited. At the highest pH value (7.5), the maximum specific xylitol production value was the lowest (q _pmax=0.085 g/l h.), indicating that the xylose metabolism of the yeast was diverted from xylitol formation to cell growth.List of symbols P _max xylitol concentration (g/l) - Q _x volumetric cell production rate (g/l h) - Q _s volumetric xylose uptake rate (g/l h) - Q _p volumetric xylitol production rate (g/l h) - q _pmax specific xylitol production (g/g h) - q _smax specific xylose uptake rate (g/g h) - _max specific cell growth rate (h^–1) - Y _p/s xylitol yield coefficient, g xylitol per g xylose consumed (g/g) - Y _p/x xylitol yield coefficient, g xylitol per g dry cell mass produced (g/g) - Y _x/s cell yield coefficient, g dry cell mass per g xylose consumed (g/g) - _cell percentage of the cell yield from the theoretical value (%) - _xylitol percentage of xylitol yield from the theoretical value (%)     

Fermentation ofd-xylose,d-glucose andl-arabinose mixture byPichia stipitis Y 7124: sugar tolerance

Summary The effect of substrate concentration (S ₀) on the fermentation parameters of a sugar mixture byPichia stipitis Y 7124 was investigated under anaerobic and microaerobic conditions. Under microaerobiosisP. stipitis maintained high ethanol yield and productivity when initial substrate concentration did not exceed 150 g/l; ethanol yield of about 0.40 g/g and volumetric productivity up to 0.39 g/l per hour were obtained. Optimal specific ethanol productivity (0.2 g/g per hour) was observed withS ₀=110 g/l. Under anaerobic conditionsP. stipitis exhibited the highest fermentative performances atS ₀=20 g/l; it produced ethanol with a yield of 0.42 g/g, with a specific rate of 1.1 g/g per day. When the initial substrate level increased, specific ethanol productivity declined gradually and ethanol yield was dependent on the degree of utilization of each sugar in the mixture.Abbreviations E _m maximum produced ethanol (g/l) - E ₀ initial ethanol (g/l) - E _v evaporated ethanol (g/l) - Q _p volumetric productivity of ethanol (g ethanol/l per hour or g/l per day) - q _p specific productivity of ethanol (g ethanol/g cells per hour) - q _pm maximum specific productivity of ethanol (g/l per hour) - S ₀ initial substrate concentration (g/l) - t _f time at which produced ethanol is maximum (h) - Y _p/s ethanol yield (g ethanol produced/g substrate utilized) - Y _x/s cell yeild (g cells produced/g substrate utilized) - Y _xo/xy xylitol yield (g xylitol produced/g xylose utilized) - probability coefficient - specific growth rate coefficient (h^-1 or d^-1)     

Enhancement of protein precipitate strength and density by low-frequency conditioning

The use of a continuous, low-frequency conditioning process to alter the structure of protein precipitate aggregates is examined. An increase in the density of aggregates is correlated with the levels of fluid acceleration and hence hydrodynamic stress to which the aggregates are exposed during conditioning. A combination of low-frequency conditioning followed by shear break-up (as in the feed zone to a high-speed disk-stack centrifuge) is shown to result in a precipitate suspension of increased particle size at the fine end of the distribution, and having a greater sedimentation velocity. The resistance of large aggregates to shear disruption is increased by low-frequency conditioning.List of Symbols CR conditioning ratio - CRS conditioning ratio after shearing - d m amplitude of displacement - D m particle size - D _c m critical size for centrifuge recovery - f s^–1 frequency of vibration - G s^–1 mean velocity gradient - Q m³/s volumetric throughput - SR shear ratio - t s ageing time Greek Symbols s^–1 mass-average shear rate - K sedimentation shape factor - _a kg/m³ aggregate density - _f kg/m³ fluid density - _s kg/m³ solids density - kg/m³ aggregate-suspension density difference - Ns/m² kinematic viscosity - amplitude of pulse ratio (ref. 23, 9) - s mean residence time - _s solids volume fraction     

Effect of growth temperature on the morphology and performance ofZymomonas mobilis ATCC 29 191 in batch and continuous culture

Summary Increasing the temperature in chemostat culture ofZymomonas mobilis ATCC 29 191 with low and high glucose concentrations was found to result in a decreasing frequency of septation leading to the formation of long filaments and in increasing outer membrane blebbing. Whether this effect is strain specific or universal inZymomonas is, unknown. Improvements in the fermentation kinetics could be achieved at elevated temperatures, with an optimum at 33°C. Temperatures >30°C induced uncoupled growth in chemostat cultures ofZ. mobilis ATCC 29 191. The results of this study emphasize the importance of temperature regulation in optimizing the performance of continuous fermentations withZymomonas.Nomenclature D Dilution rate, 1/h - _max Maximum specific growth rate, 1/h - S _R Initial substrate concentration, g glucose/1 - S Amount of glucose consumed, g glucose/1 - S ₀ Effluent substrate concentration, g glucose/1 - X Biomass concentration - g cells/1 - [P] Amount of product formed, g ethanol/1 - [P] Product concentrations, g ethanol/l - Y _x/s Growth yield, g cells/g glucose used - Y _p/s Product yield, g ethanol/g glucose used - O _s Specific rate of glucose uptake, g glucose/g cells/h - Q _p Specific rate of ethanol formation, g ethanol/g cells/h - VP Volumetric productivity, g ethanol/1/h - t Fermentation time, h Corresponding author     

A kinetic model and simulation of starch saccharification and simultaneous ethanol fermentation by amyloglucosidase and Zymomonas mobilis

A mathematical model is described for the simultaneous saccharification and ethanol fermentation (SSF) of sago starch using amyloglucosidase (AMG) and Zymomonas mobilis. By introducing the degree of polymerization (DP) of oligosaccharides produced from sago starch treated with -amylase, a series of Michaelis-Menten equations were obtained. After determining kinetic parameters from the results of simple experiments carried out at various substrate and enzyme concentrations and from the subsite mapping theory, this model was adapted to simulate the SSF process. The results of simulation for SSF are in good agreement with experimental results.List of Symbols g/g rate coefficient of production - _max 1/h maximum specific growth rate - E %, v/w AMG concentration - G ₁ mmol/l glucose concentration - G _c mmol/l glucose concentration consumed - G _f mmol/l glucose concentration formed - G _n mmol/l n-mer maltooligosaccharide concentration - K _i g/l ethanol inhibition constant for ethanol production - K _g mmol/l glucose inhibition constant for glucose production - K _p mmol/l glucose limitation constant for ethanol production - K _x mmol/l glucose limitation constant for cell growth - K _m,n mmol/l Michaelis-Menten constant for n-mer oligosaccharide - k _e %, v/w enzyme limitation constant - k _es proportional constant - k _{max, n} 1/s maximal velocity for n-mer digestion - k _s g/l substrate limitation constant - m _s g/g maintenance energy - MW _n g/mol molecular weight of n-mer oligosaccharide - P g/l ethanol concentration - P ₀ g/l initial ethanol concentration - P _m g/l maximal ethanol concentration - Q _pm g/(g · h) maximum specific ethanol production rate - S _n mmol/h branched n-mer oligosaccharide concentration - S ₀ g/l initial starch concentration - S _sta g/l starch concentration - S _tot g/l total sugar concentration - V _{max, n} 1/h maximum digestion rate of n-mer oligosaccharide - V ₀ g/(l · h) initial glucose formation rate - X g/l cell mass - X ₀ g/l initial cell mass - Y _p/s g/g ethanol yield - Y _x/s g/g cell mass yield     

External factors involved in the regulation of an extracellular proteinase synthesis in Bacillus megaterium

Summary A mathematical model was formulated to describe the kinetics and stoichiometry of growth and proteinase production in Bacillus megaterium. Synthesis of the extracellular proteinase in a batch culture is repressed by amino acids. The specific rate of formation of the enzyme (r _E) can be described by the formula {ie373-1}, where k ₂ and k ₃ stand for the non-repressible and repressible part of enzyme synthesis respectively, k _{S 2} is a repression coefficient and S ₂ indicates the concentration of amono acids; the values of k ₂ and k _{S 2} depend on the composition of the mixture of amino acids. Even in a high concentration, a single amino acid is less effective than a mixture of amino acids. The dependence of the proteinase repression on the concentration of an external amino acid (leucine) follows the same course as its rate of incorporation into proteins, approaching saturation at concentrations higher than 50 M (half saturation approximately 10 M). However, the total uptake of leucine did not exhibit any saturation even at 500 M external concentration.Symbols X biomass concentration, g/l - E proteinase concentration, unit/l - t time, h - S ₁ concentration of glucose, g/l - S ₂ concentration of amino acids, g/l - specific growth rate, l/h - rE specific rate of enzyme production, unit/g/h - k ₁ growth kinetic constant, l/h - k ₂ product formation kinetic constant (for non-repressible part of enzyme synthesis), unit/g - k ₃ product formation kinetic constant (for repressible portion of enzyme synthesis), unit/g - k _{S 1} saturation constant, g/l - k _{S 2} repression coefficient for a certain amino acid or amino acids mixture, g/l     

On-line monitoring and modelling based process control of high rate nitrification — lab scale experimental results

This paper presents a new concept for the control of nitrification in highly polluted waste waters. The approach is based on mathematical modelling. To determine the substrate degradation rates of the microorganisms involved, a mathematical model using gas measurement is used. A fuzzy-controller maximises the capacity utilisation efficiencies. The experiments carried out in a lab-scale reactor demonstrate that even with highly varying ammonia concentrations in the influent, the nitrogen concentrations in the effluent can be kept within legal limits.List of Symbols c mg/l concentration - c mg/l gas concentration - H ₂ Henry-coefficient - k _L a 1/h mass transfer coefficient - mol/l dissociation constant - K _iS mg/l substrate inhibitor constant - k _iH mg/l inhibitor constant - k _S mg/l saturation constant - K _O2 mg/l oxygen saturation constant - r(B) mg/lh growth rate - r(S) mg/lh degradation reaction rate - t _v h retention time - T °C temperature - V 1 volume - V 1/h flow rate - Y g/g yield coefficient - k _b capacity utilisation efficiency - 1/h specific growth rate     

Stimulation and inhibition of insulin release by an amino-reactive probe of plasma membrane

Summary 4-Acetamido-4-isothiocyanostilbene-2,2-disulphonic acid (SITS), an amino-reacting probe of plasma membranes, stimulated the release of insulin from micro-dissected pancreatic islets ofob/ob-mice. This effect of SITS was inhibited by adrenaline or by calcium deficiency. SITS did not inhibit the insulin-releasing action of glucose or leucine but rather potentiated the effect of glucose. In contrast, SITS markedly depressed the insulin secretory response to chloromercuribenzene-p-sulphonic acid. It is suggested that by reacting with the plasma membranes SITS may induce secretagogic ionic fluxes in the -cells. In addition, SITS apparently inhibits the secretagogic recognition of chloromercuribenzene-p-sulphonic acid, presumably by preventing the organic mercurial from reacting with certain membrane thiol groups.     

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

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