Conversion of paper sludge to ethanol. I: Impact of feeding frequency and mixing energy characterization

In this paper, conversion of paper sludge to ethanol was investigated with the objective of optimization of the overall operation costs. Experimental work was undertaken to optimize cellulase loading, and to determine mixing energy requirements. It was found that decreasing feeding frequency (feed additions per residence time) allows the cellulase loading to be decreased at least two fold with no decrease in cellulose conversion but also entails mixing a slurry of higher solids content and lower conversion at the beginning of the operating cycle. The viscosity of paper sludge slurries was found to increase exponentially with decreasing conversion and increasing solid content. In particular, the viscosity (V) was described well by equation V = e^(kX−X ₀^)(S−S ₀^)+C (V viscosity (cp), X conversion, S solid content (g/L), k, X ₀, S ₀, C are empirical parameters). Added costs associated with operating at low feeding frequencies (including higher mixing energy and higher capital costs for the motor and for sludge hold tasks) were found to be small compared to the economic benefits resulting from reduced cellulase loading.     

Lactose continuous fermentation with cells recycled by ultrafiltration and lactate separation by electrodialysis: model identification

Summary An off-line parameter estimation method has been developed to predict the dynamic behaviour of a continuous lactose fermentation system. The model used is an unstructured model taking into account cell growth, substrate consumption, and metabolite production (lactic acid). This method, based on the Hooke-Jeeves non-linear-programming technique, results in a good estimation of the biological parameters of the model, and so gives a better understanding of the different phenomena involved in lactose fermentation.Nomenclature Cp, Cs, Cz, Dp, Ds, Dz coefficients in system (A) - Fe bioreactor influent flow rate (1/h) - I current in the ED unit (A) - J lactate flux in the ED unit (g/h) - Kd mortality constant (h^-1) - Kp product inhibition constant (g/l) - Ks strbstrate saturation constant (g/l) - P ₀ product concentration in the bioreactor (g/l) - P ₁ product concentration in the D tank (g/l) - P _0r estimation of P ₀ (g/l) - Q ₀ retentate flow rate (UF influent) (1/h) - Q ₁ permeate flow rate (1/h) - Q ₂₂ cell bleed flow rate (1/h) - Q ₃ recycling flow rate in the ED (influent) (1/h) - Se substrate concentration in the influent (g/l) - S ₀ supstrate concentration in the bioreactor (g/l) - S ₁ substrate concentration in tank D (g/l) - S _0r estimation of S ₀ (g/l) - t time (h) - V ₀ fermentation broth volume (1) - V ₁ tank D volume (1) - X ₀ biomass concentration in the bioreactor (g/l) - Y _P/S (=1/Y _S/P) lactic acid yield coefficient (g lactic acid/g lactose consumed) - Y _X/S (=1/Y _S/X) cell yield coefficient (g cells produced/g lactose consumed) - Y _X/Z (=1/Y _Z/X) second cell yield coefficient (g cells produced/g nitrogen consumed) - Y _x, Y _m input mathematical parameters of the linear system (M ₂) - Ze nitrogen concentration in the influent (g/l) - Z ₀ nitrogen concentration in the bioreactor (g/l) - Z ₁ nitrogen concentration in tank D (g/l) - Z _0r estimation of Z ₀ (g/l) - , constants of the Luedeking and Piret's model - specific growth rate (h^-1) - _max maximum specific growth rate (h^-1)     

A heuristic approach to fed-batch optimisation of streptokinase fermentation

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

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

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

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

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

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     

Model-based analysis on growth of activated sludge in a sequencing batch reactor

A mathematical model is developed to describe the growth of multiple microbial species such as heterotrophs and autotrophs in activated sludge system. Performance of a lab-scale sequencing batch reactor involving storage process is used to evaluate the model. Results show that the model is appropriate for predicting the fate of major model components, i.e., chemical oxygen demand, storage polymers (X _STO), volatile suspended solid (VSS), ammonia, and oxygen uptake rate (OUR). The influence of sludge retention time (SRT) on reactor performance is analyzed by model simulation. The biomass components require different time periods from one to four times of SRT to reach steady state. At an SRT of 20 days, the active bacteria (autotrophs and heterotrophs) constitute about 57% of the VSS; the remaining biomass is not active. The model established demonstrates its capacity of simulating the reactor performance and getting insight in autotrophic and heterotrophic growth in complex activated sludge systems.     

Effects of quantitative shock loadings on the constant recycle sludge concentration activated-sludge process

The reliability of the process of Ramanathan and Gaudy (Biotechnol Bioeng., 13 , 125 (1971)) for the completely mixed activated-sludge process holding the recycle cell concentration, X_R, as a system constant with respect to step changes in hydraulic retention time was investigated. The experiments were run at initial dilution rates of ?, ?, ¼, and ½ hr^?1 treating a soft drink bottling wastewater. The influent substrate concentration was maintained at 1000 mg/liter chemical oxygen demand and the hydraulic recycle ratio at 0.3. The recycle sludge concentration was maintained at about 7000 mg/liter. It was found that the system could accommodate hydraulic shock loads up to 200% positive changes and down to 50%negative changes without disruption of the effluent quality. Shorter retention time of the range studied, from 2 to 8 hr, has the advantage of shorter response time with respect to the response of the concentration of biological solids in the reactor.     

Active heterotrophic biomass and sludge retention time (SRT) as determining factors for biodegradation kinetics of pharmaceuticals in activated sludge

The present study investigates the biodegradation of pharmaceutically active compounds (PhACs) by active biomass in activated sludge. Active heterotrophs (X_bh) which are known to govern COD removal are suggested as a determining factor for biological PhAC removal as well. Biodegradation kinetics of five polar PhACs were determined in activated sludge of two wastewater treatment plants which differed in size, layout and sludge retention time (SRT).Results showed that active fractions of the total suspended solids (TSS) differed significantly between the two sludges, indicating that TSS does not reveal information about heterotrophic activity. Furthermore, PhAC removal was significantly faster in the presence of high numbers of heterotrophs and a low SRT. Pseudo first-order kinetics were modified to include X_bh and used to describe decreasing PhAC elimination with increasing SRT.     

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     

Effect of high substrate concentration and cell feedback on kinetic behavior of heterogeneous populations in completely mixed systems

Growth kinetics of heterogeneous populations of sewage origin were studied in completely mixed reactors of the once-through type at a high concentration of incoming substrate, 3000 mg/l glucose, and in systems employing cell feedback or sludge recycle at an incoming substrate concentration of 1000 mg/1 glucose. The recycle flow rate employed was 25% of the incoming feed flow, and the concentration of cells in the recycle was maintained as closely as possible at 150% of the cell concentration in the reactor. Studies were made at various dilution rates. Throughout these studies, batch experiments using cells grown at the various dilution rates were made to determine k_s and μ_m values. As in previous studios using heterogeneous populations, the relationship between specific growth rates μ and substrate concentration S was represented better by the Monod equation than by any other which was tested. The growth “constants” μ_m, k_s, and Y were found to fall in the same general range as those determined in previous studies in once-through systems operated at 1000 mg/l glucose. It was observed that cell recycle, even at the relatively low concentration factor employed in these studies, greatly enhanced the flocculating and settling characteristics of the cells.     

Kinetics of <Emphasis Type="Italic">Bacillus thuringiensis</Emphasis> var. <Emphasis Type="Italic">israelensis</Emphasis> growth on high glucose concentrations

The kinetic and general growth features of Bacillus thuringiensis var. israelensis were evaluated. Initial glucose concentration (S ₀) in fermentation media varied from 10 to 152 g/l. The results afforded to characterize four morphologically and physiologically well-defined culture phases, independent of S ₀ values: Phase I, vegetative growth; Phase II, transition to sporulation; Phase III, sporulation; and Phase IV, spores maturation and cell lysis. Important process parameters were also determined. The maximum specific growth rates (μ _X,m) were not affected with S ₀ up to 75 g/l (1.0–1.1 per hour), but higher glucose concentrations resulted in growth inhibition by substrate, revealed by a reduction in μ _X,m values. These higher S ₀ values led to longer Phases III and IV and delayed sporulation. Similar biomass concentrations (X _m = 15.2–15.9 g/l) were achieved with S ₀ over 30.8 g/l, with increasing residual substrate, suggesting a limitation in some other nutrients and the use of glucose to form other metabolites. In this case, with S ₀ from 30.8 to 152 g/l, cell yield (Y _X/S ) decreased from 0.58 to 0.41 g/g. On the other hand, with S ₀ = 10 g/l growth was limited by substrate, and Y _X/S has shown its maximum value (0.83 g/g).     

Bubble-column design for growth of fragile insect cells

A mathematical model for the design of bubble-columns for growth of shear-sensitive insect cells is presented. The model is based on two assumptions. First, the loss of cell viability as a result of aeration is a first-order process. Second, a hypothetical volume X, in which all viable cells are killed, is associated with each air bubble during its lifetime. The model merely consists of an equation for k _d, the first-order death-rate constant, and A _min, the minimum specific surface area of the air bubbles to supply sufficient oxygen. In addition to X, the equation for k _d contains the air flow F, the air-bubble diameter d _b, the diameter D and the height H of the bubble column. This equation has been experimentally validated. Comparison of the equations for k _d and A _min shows that especially H is the key parameter to manipulate in bubble-column design in order to meet the demands set by A _min and k _dg, the first-order growth-rate constant. It is concluded that net growth of cells is enhanced as size and height of the bubble column increase.     

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.     

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

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

Formulation of Dacarbazine-loaded Cubosomes—Part II: Influence of Process Parameters

The purpose of this study was to investigate the combined influence of three-level, three-factor variables on the formulation of dacarbazine (a water-soluble drug) loaded cubosomes. Box–Behnken design was used to obtain a second-order polynomial equation with interaction terms to predict response values. In this study, the selected and coded variables X ₁, X ₂, and X ₃ representing the amount of monoolein, polymer, and drug as the independent variables, respectively. Fifteen runs of experiments were conducted, and the particle size (Y ₁) and encapsulation efficiency (Y ₂) were evaluated as dependent variables. We performed multiple regression to establish a full-model second-order polynomial equation relating independent and dependent variables. A second-order polynomial regression model was constructed for Y ₁ and confirmed by performing checkpoint analysis. The optimization process and Pareto charts were obtained automatically, and they predicted the levels of independent coded variables X ₁, X ₂, and X ₃ (−1, 0.53485, and −1, respectively) and minimized Y ₁ while maximizing Y ₂. These corresponded to a cubosome formulation made from 100 mg of monoolein, 107 mg of polymer, and 2 mg with average diameter of 104.7 nm and an encapsulation efficiency of 6.9%. The Box–Behnken design proved to be a useful tool to optimize the particle size of these drug-loaded cubosomes. For encapsulation efficiency (Y ₂), further studies are needed to identify appropriate regression model.     

Comparison of different bioreactor performances

Bioreactors are compared based on oxygen transfer rate and efficiency, mixing performance, cell mass productivity as well as with respect to enzyme and metabolite productivity.List of Symbols AC acetate concentration - AL airlift tower loop reactor - CFU colony-forming units - CP coalescence-promoting medium - CS coalescence-suppressing medium - D _D impeller clearance - D _M molecular diffusivity - D _S diameter of the column - DT flat-bladed disc turbine - D _v vessel diameter - E. act enzyme activity - EDR energy dissipation rate - EcoRI restriction endonuclease - EcoR4 protection plasmid - E _{O 2} efficiency of oxygen transfer rate - E _X efficiency of cell mass production with respect to the specific power input - g acceleration of gravity - H height of column - H _v vessel height - HV highly viscous medium - IPTG isopropyl thiogalactoside (inducer of Lacpromoter) - k fluid consistency factor - k _L mass transfer coefficient - k _La volumetric mass transfer coefficient - m exponent - N impeller speed - n exponent - n flow behaviour index - P power input - P/V_L specific power input - PR marine propeller - P _LacUV5 Lac-promoter-induced by IPTG - P _R promoter-induced with temperature shift - _{O 2} oxygen transfer rate - q _g,q _{O 2} aeration rate, specific aeration rate with respect to liquid volume - R density of cultivation medium - R _p product formation rate - R _X growth rate - SpA protein A - ST stirred tank reactor - TCC total cell count - t _Lc liquid circulation time - U enzyme activity unit - u _B bubble rise velocity - u _G superficial gas velocity - V _L volume of the liquid phase - v kinematic viscosity of the cultivation medium - W _SG superficial gas velocity - X cell mass concentration - Y _E/S yield coefficient of ethanol formation with respect to substrate consumption - Y _P/X specific product formation with respect to cell concentration - Y _X/E yield coefficient of cell growth with respect to ethanol consumption - Y _{X/O 2} yield coefficient of cell growth with respect to oxygen consumption rate - Y _X/S yield coefficient of growth with respect to substrate consumption - _L liquid mixing time - _eff effective dynamic viscosity of the cultivation medium - _W dynamic viscosity of water - _max maximum specific growth rate - surface tension of the cultivation medium