Values Of Substrate Concentration Research Articles

Linking the Pre-Steady-State, Steady-State, and Zero-Order Kinetic Parameters Together for Industrial Applications

There appeared not to have been a way of linking pre-zero-order kinetics to zero-order kinetics so as to garner key kinetic parameters at very high industrial concentrations of the substrate. The objectives were the derivation of equations that can be explored in relating pre-zero-order (pre-steady-state (prss)) to zero-order kinetic parameters (ZOK), such as the Michaelis-Menten constant (KM), maximum velocity of catalysis (Vmax), and specificity constant (SC), to be evaluated. The Vmax for the higher industrial-type concentration of the enzyme (alpha-amylase) was 7812.5 micromoles/l/min, while the KM was 115.1 g/l. The SC obtained by calculation, either by the new equation (Eq. (25b)) or the ratio Vmax:KM, was 67.88 micromoles l/g min. Surprisingly, as compared to the literature, the SC obtained by the new graphical method was 275.4 micromoles l/g min using sub-KM values of substrate concentrations. The prss Vmax and KM were 2348.62 ± 479.94 micromoles l/g min and 7.41 ± 1.77 g/l, respectively. There is a justification for an equation linking PRSS and the ZOK, which can enhance reactor design. The equation linking PRSS to the ZOK kinetic parameters was derived. With the equation, the KM for a very high industrial concentration of the substrate and the enzyme that would have been impossible was made possible. Future studies may focus on assays at high concentrations of the enzyme and sub-KM concentrations of the substrate so as to observe a repeat of higher SC. Note that if the concentration of one enzyme is twice (or more) the concentration of another enzyme of the same kind, the Vmax of the first should be twice the Vmax of the latter given saturating concentrations of the substrate for each enzyme; the parameter that is constant is the catalytic first-order rate constant (kcat); it may be theoretically assumed that the KM can follow the same order as with the Vmax. Apart from a very high concentration of the gelatinized starch that retards the mobility of the enzyme, the spectrophotometer has an upper limit of its power to read color development.

Artificial neural networks in the modeling of the catalytic activity of a biosensor composed of conjugated polymers and urease.

Thin films of conjugated polymer and enzyme can be used to unravel the interaction between components in a biosensor. Using artificial neural networks (ANNs) improves data interpretability and helps construct models with great capacity for classifying and processing information. The present work used kinetic data from the catalytic activity of urease immobilized in different conjugated polymers to create ANN models using time, substrate concentration, and absorbance as input variables since the models had absorbance in a posterior instant as output value to explore the predictivity of the ANNs. The performance of the models was evaluated by Pearson's correlation coefficient (ρ) and mean squared error (MSE) values. After the learning process, a series of new experiments were performed to verify the generality of the models. As the main results, the best ANN model presented 0.9980 and 3.0736 × 10-5 for ρ and MSE, respectively. For the simulation step, intermediary values of substrate concentration were used. The mean absolute percentage error (MAPE) values were 3.34, 3.07, and 3.78 for 12mM, 22mM, and 32mM concentrations, respectively. Overall, with the simulations, it was possible to ascertain the interpolatory capacity of the model, which has a learning mechanism based on absorbance and time as variables. Thus, the potential of ANNs would be in their use in pre-evaluations, helping to determine the substrate concentration at which there is higher catalytic activity or in determining the linear range of the sensor.

Performance improvement of co‐culture inoculated microbial fuel cell using fuzzy modelling and Harris hawks optimization

SummaryMicrobial fuel cell (MFC) is a promising technology since two important processes: wastewater treatment and electrical energy, can be obtained simultaneously. The performance of the MFC (maximum power density (MPD) and COD removal) depends mainly on substrate concentration, pH time, and initial COD. Therefore, the main target of this work is to simultaneously increase the MPD and COD removal by determining the optimal controlling parameters. The proposed methodology integrates fuzzy modelling and Harris Hawks optimization (HHO). Firstly, based on the experimental data set, an accurate fuzzy model is created to simulate the performance of MFC in terms of four controlling parameters. To prove the superiority of fuzzy model, the results are compared with response surface methodology (RSM) in terms of RMSE and coefficient of determination (R2). Secondly, using HHO, the optimal values of substrate concentration, co‐culture composition, pH, and time are determined. These four controlling parameters are used as decision variables during the optimization process, whereas the objective function is the simultaneous maximization of the MPD and COD removal. The obtained results proved that the optimal substrate concentration, pH, time, and initial COD values are 58.2%, 7, 14.4 days, and 32 × 10−3 (mg/L) respectively. Under this condition, the integration between fuzzy and HHO, the overall performance of MFC has been improved by 10.37% and 19.13%, respectively, compared with the experimental and RSM.

Optimization Studies in Simultaneous Saccharification and Fermentation of Wheat Bran Flour into Ethanol

The effects of process variables in Simultaneous Saccharification and Fermentation (SSF) of wheat bran flour were studied in bulk fermentation using a coculture of Aspergillus niger - Kluveromyces marxianus. The effect of substrate density, pH, temperature, and enzyme concentration on wheat bran was predicted by designing experiments in which a single parameter is varied keeping other variables at a constant level. The above parameters were optimized for a batch culture in a fermentor. Optimal values for substrate concentration, pH, temperature, and enzyme concentration during processing were 200 g/l, 5.5, 65°C, and 7.5 IU, respectively. In pre-treatment experiments, the concentration of enzymes and the pre-treatment temperature are highly correlated. The influence of pH, temperature, and substrate density on ethanol production was investigated. Temperature pH was determined as optimal, 32°C and 5.5, respectively. After 48 hours of fermentation at optimum pH, a solution of wheat bran containing a maximum of 6% starch produces a maximum of 22.9 g/l ethanol.

Invertase production by yeast mediated silver nanoparticles using statistical tool central composite design

Nanotechnology is a promising field which modifies the properties of materials at the atomic level to attain specific properties with nanometer sized items. The enzyme used in the present study is invertase (beta – fructofuranosidase fructohydrolase, EC 3.2.1.26), which hydrolysis sucrose to fructose and glucose. In industry, the invertase enzyme is used for the significant production of fructose syrup from sucrose syrup. Silver nanoparticles were synthesized from baker’s yeast by bioreduction method. Invertase enzyme was extracted using these yeast mediated AgNPs from a low cost substrate (bagasse). This study focuses on optimization activity interaction of AgNPs induced enzyme invertase, including three factors: pH, temperature (°C), substrate concentration (gl−1). All experiments are designed according to response surface method (RSM) – Central Composite Design (CCD). As the result pH was identified as significant factor (p < 0.05). A maximum activity of 0.266 IUml−1 was achieved at optimum values of substrate concentration (10.29 gl−1), pH (6.3) and temperature (29.06 °C). Technically, these silver nanoparticles are immensely applied to biomedical field. Not only its application, the production process is cost-effective, compatible for medical and pharmaceutical applications as well as large scale commercial production.

Understanding the kinetics of catalysed reactions in microheterogeneous thin film electrodes

In this paper which is dedicated to Professor Richard Compton for his 65th birthday, we examine the problem of describing the transport and kinetics of catalytic reactions in which the catalyst is immobilized within a support matrix such as for example redox enzymes immobilized in a polymer matrix, to form a chemically modified electrode. We examine a mathematical procedure which enables a full analytical solution to the Michaelis-Menten kinetic rate equation when coupled to Ficksian diffusion in thin bounded film. This governing reaction/diffusion equation is non-linear and a full analytical solution has, up until very recently, not been developed. Analytical solutions valid for low and high substrate concentrations have been previously reported. This solution for the amperometric steady state current is accurate whatever the value of substrate concentration. The analysis is applied to diffusion/reaction in a planar slab. General analytical solutions valid for steady state conditions for both the amperometric and potentiometric sensor response are provided. We then extend this useful analysis to consider the effect of concentration polarization in the solution, and to consider the effect of competitive inhibition on the amperometric current response. Finally, the analysis is extended to a polymer modified electrode when a redox mediator is used in the polymer film. General expressions for the current are obtained which were valid for any value of the substrate concentration. Kinetic case diagrams are developed and nine approximate limiting expressions for the amperometric response at steady state when the catalytic matrix is either conducting or insulating are developed.

Transport and kinetics in electrocatalytic thin film biosensors: bounded diffusion with non-Michaelis-Menten reaction kinetics

In this paper, we describe the problem of describing the transport and catalytic kinetics at immobilized enzymes in an electronically conductive polymer thin film where substrate inhibition is important. Here, the enzyme kinetics are not well described by the Michaelis-Menten equation. We describe a mathematical procedure based on the recently developed Akbari-Ganji method (AGM) which facilitates a full analytical solution of the boundary value problem which is valid for all values of substrate concentration. Closed form expressions for both the substrate concentration in the film and the steady-state amperometric current response are presented. Limiting kinetic cases are identified and are expressed pictorially in parameter space using a kinetic case diagram.

A mesoscopic stochastic model for the specific consumption rate in substrate-limited microbial growth.

The specific consumption rate of substrate, as well as the associated specific growth rate, is an essential parameter in the mathematical description of substrate-limited microbial growth. In this paper we develop a completely new kinetic model of substrate transport, based on recent knowledge on the structural biology of transport proteins, which correctly describes very accurate experimental results at near-zero substrate concentration values found in the literature, where the widespread Michaelis-Menten model fails. Additionally, our model converges asymptotically to Michaelis-Menten predictions as substrate concentration increases. Instead of the single active site enzymatic reaction of Michaelis-Menten type, the proposed model assumes a multi-site kinetics, simplified as an apparent all-or-none mechanism for the transport, which is controlled by means of the local substrate concentration in the close vicinity of the transport protein. Besides, the model also assumes that this local concentration is not equal to the mean substrate concentration experimentally determined in the culture medium. Instead, we propose that it fluctuates with a mostly exponential distribution of Weibull type.

PRODUCTION OF BIOETHANOL FROM RICE STRAW USING YEAST EXTRACTS PEPTONE DEXTROSE

The production of bio-ethanol from Rice Straw (Oryza sativa) was carried out using rice straw as a feedstock and a combination of Yeast Extracts Peptone Dextrose (YEPD)at 0.2%(w/v) 0.4%(w/v), 0.6%(w/v), 0.8%(w/v) and 1%(w/v) concentrations and Saccharomyces cerevisiae (yeast) at 0.5% (w/v), 1%(w/v), 1.5%(w/v), 2%(w/v) and 2.5%(w/v) concentrations as cells for fermentation. The study determined the most suitable pre-treatment method from the following pretreatment methods; 1M NaOH and heating. IM NaOH pre-treatment gave the highest cellulose and lowest lignin content. The effects of substrate concentration values of 1g/l, 2g/l, 4g/l, 6g/l and 8g/l; with particle size of 300μm and cell loading combination of YEPD at 0.2%(w/v) 0.4%(w/v), 0.6%(w/v), 0.8%(w/v), 1%(w/v) concentrations and Saccharomyces cerevisiae (yeast) at 0.5% (w/v), 1%(w/v), 1.5%(w/v), 2%(w/v), 2.5%(w/v) on the fermentation process were investigated to obtain optimum conditions of fermentation. The optimum conditions of fermentation were obtained at temperature of 33 0 C, pH value of 4.0, substrate concentration of 4g/l, particle size 300μm and YEPD to yeast ratio of 0.8/1.5 after 72 hours of fermentation time. Also substrate concentration of 4g/l, gave highest bioethanol yield of 49.50%. http://dx.doi.org/10.4314/njt.v36i1.36

PRODUCTION OF BIOETHANOL FROM RICE STRAW USING YEAST EXTRACTS PEPTONE DEXTROSE

The production of bio-ethanol from Rice Straw (Oryza sativa) was carried out using rice straw as a feedstock and a combination of Yeast Extracts Peptone Dextrose (YEPD)at 0.2%(w/v) 0.4%(w/v), 0.6%(w/v), 0.8%(w/v) and 1%(w/v) concentrations and Saccharomyces cerevisiae (yeast) at 0.5% (w/v), 1%(w/v), 1.5%(w/v), 2%(w/v) and 2.5%(w/v) concentrations as cells for fermentation. The study determined the most suitable pre-treatment method from the following pretreatment methods; 1M NaOH and heating. IM NaOH pre-treatment gave the highest cellulose and lowest lignin content. The effects of substrate concentration values of 1g/l, 2g/l, 4g/l, 6g/l and 8g/l; with particle size of 300μm and cell loading combination of YEPD at 0.2%(w/v) 0.4%(w/v), 0.6%(w/v), 0.8%(w/v), 1%(w/v) concentrations and Saccharomyces cerevisiae (yeast) at 0.5% (w/v), 1%(w/v), 1.5%(w/v), 2%(w/v), 2.5%(w/v) on the fermentation process were investigated to obtain optimum conditions of fermentation. The optimum conditions of fermentation were obtained at temperature of 330C, pH value of 4.0, substrate concentration of 4g/l, particle size 300μm and YEPD to yeast ratio of 0.8/1.5 after 72 hours of fermentation time. Also substrate concentration of 4g/l, gave highest bioethanol yield of 49.50%. http://dx.doi.org/10.4314/njt.v36i1.36

SUBSTRATE REDUCTION KINETICS AND PERFORMANCE EVALUATION OF FLUIDIZED-BED REACTOR FOR TREATMENT OF BREWERY WASTEWATER

pilot scale anaerobic fluidized-bed reactor was operated for treating brewery wastewater at nine different hydraulic retention time (HRTs) of 2, 3, 4, 5, 6, 7, 8, 9, and 10hr. The COD reduction efficiencies increased from 9.8% to 74.1% with increasing HRT from 2 to 10hr.Similarly, the corresponding BOD 5 reduction efficiencies increased from 38.0% to 92.9% with the same increase in HRT. The predicted effluent substrate concentration values of Modified Stover–Kincannon model were found to be in strong agreement with the experimental values. This is due to proper solubilization and consumption of entrapped substrates in the fluidized-bed reactor since the microbial cells have maximum opportunity to interact with the entrapped substrates in the reactor. The kinetic parameters were determined using existing linear models with experimental data. Modified Stover-Kincannon model showed high correlation coefficient (R 2 = 0.9996) only in predicted effluent concentration of wastewater. This shows that this model can be best used to predict effluent COD concentration in a fluidized bed reactor treating brewery wastewater. Similarly, FIRST ORDER MODEL has the highest correlation coefficient in the plot using experimental data. This means that first order model can be used to represent the real biological degradation that takes place during the treatment of waste water by microorganisms in a fluidized-bed reactor system treating brewery wastewater. This model and kinetic parameters obtained in this work can be employed in the design of industrial size- fluidized bed reactor for treatment of brewery waste water. Maximum utilization rate constant (U max ) determined is 11.75g COD/l.hr and the saturation value constant (K B ) 131.41g COD/l.hr was also determined. The values of kinetic constants were also used to predict the effluent COD concentration. It was observed that the simulated data (predicted data) were in good harmony with the experimental data counterpart in modified Stover –Kicannon model, The strength of the above-mentioned model can be observed from the correlation coefficient obtained from the model asR 2 =0.9996 in modified Stover-kicannon model. http://dx.doi.org/10.4314/njt.v35i1.15

Using simple models to describe the kinetics of growth, glucose consumption, and monoclonal antibody formation in naive and infliximab producer CHO cells.

Despite their practical and commercial relevance, there are few reports on the kinetics of growth and production of Chinese hamster ovary (CHO) cells—the most frequently used host for the industrial production of therapeutic proteins. We characterize the kinetics of cell growth, substrate consumption, and product formation in naive and monoclonal antibody (mAb) producing recombinant CHO cells. Culture experiments were performed in 125 mL shake flasks on commercial culture medium (CD Opti CHO™ Invitrogen, Carlsbad, CA, USA) diluted to different glucose concentrations (1.2–4.8 g/L). The time evolution of cell, glucose, lactic acid concentration and monoclonal antibody concentrations was monitored on a daily basis for mAb-producing cultures and their naive counterparts. The time series were differentiated to calculate the corresponding kinetic rates (rx = d[X]/dt; rs = d[S]/dt; rp = d[mAb]/dt). Results showed that these cell lines could be modeled by Monod-like kinetics if a threshold substrate concentration value of [S]t = 0.58 g/L (for recombinant cells) and [S]t = 0.96 g/L (for naïve cells), below which growth is not observed, was considered. A set of values for μmax, and Ks was determined for naive and recombinant cell cultures cultured at 33 and 37 °C. The yield coefficient (Yx/s) was observed to be a function of substrate concentration, with values in the range of 0.27–1.08 × 107 cell/mL and 0.72–2.79 × 106 cells/mL for naive and recombinant cultures, respectively. The kinetics of mAb production can be described by a Luedeking–Piret model (d[mAb]/dt = αd[X]/dt + β[X]) with values of α = 7.65 × 10−7 µg/cell and β = 7.68 × 10−8 µg/cell/h for cultures conducted in batch-agitated flasks and batch and instrumented bioreactors operated in batch and fed-batch mode.

Modeling Growth of Filaments and Floc Formers in Activated Sludge Flocs: Integrating the Effects of Kinetics and Diffusion

Filamentous bulking is the most common solid-separation problem in wastewater treatment systems. The phenomenon is attributed to competition between filamentous bacteria and floc-forming bacteria, a tradeoff between kinetic selection (KST) and substrate diffusion limitation (SDL). Previously, we explained our contradictory experimental results by both effects and developed a conceptual framework with three regions (bulking, intermediate, and non-bulking). However, within this purely qualitative framework, the range of substrate concentrations for each region was not determined. To test the framework and quantify the critical concentrations for the three regions, we here integrate KST and SDL into a model that simulates the growth rates of filaments and floc formers inside activated sludge flocs. Bulking and non-bulking sludges were produced in laboratory-scale reactors, and the kinetic model parameters were measured by respirometry. We then extended the model to verify our previous experimental results from sequencing batch reactors (SBRs). The effects of oxygen limitation and decay rates of floc formers and filaments on the modeled growth rates were also evaluated. In basic simulations, the model successfully determined the critical values of substrate concentrations in the framework. The results of the extended model were consistent with our experimental data, indicating that large flocs induce bulking whereas small flocs do not. Simulation results were more sensitive to the kinetic parameters than to the diffusion parameters, and the parameters more sensitively affected the growth of floc formers than filament growth.

Effect of an Error in the Forward Difference based tabular values of the Rate of Enzymatic Reaction based Mathematical Functions

In this research paper, we explained the effect of an error in the tabular values of substrate concentration, enzyme concentration and temperature with the rate of enzymatic reaction. All values are in interval. These values are obtained by mathematical functions of the rate of enzymatic reaction. Mathematical functions show that the concentration of substrate is the limiting factor, as the substrate concentration increases, the Enzyme reaction rate increases. Assuming a sufficient concentration of substrate is available, increasing Enzyme concentration will increase the rate of enzymatic reaction. Temperature, concentration of substrate and concentration of enzyme are increased the rate of enzymatic reaction at a limit which is called optimum limit. We take these values in tabular form with forward difference and added an error in the tabular values of substrate concentration, enzyme concentration and temperature with the rate of enzymatic reaction. Tabular values show the erroneous values. There are changes in tabular values. The effect of an error goes increasing with the succession. The coefficients of ? are the binomial coefficients with alternating signs. In various difference columns of the difference table, the algebraic sum of the error is zero. The maximum error occurs in the same line as the erroneous tabular value. This is show the effect of tabular values of substrate concentration, enzyme concentration and temperature with the rate of enzymatic reaction.

A COMPARISON BETWEEN THE INITIAL RATE EXPRESSIONS OBTAINED UNDER STRICT CONDITIONS AND THE RAPID EQUILIBRIUM ASSUMPTION USING, AS EXAMPLE, A FOUR SUBSTRATE ENZYME REACTION

The software WinStes, developed by our group, is used to derive the strict steady-state initial rate equation of the reaction mechanism of CTP:sn-glycerol-3-phosphate cytidylyltransferase [EC 2.7.7.39] from Bacillus subtilis. This enzyme catalyzes a reaction with two substrates and operates by a random ordered binding mechanism with two molecules of each substrate. The accuracy of the steady-state rate equation derived is checked by comparing the rate values it provides with those obtained from the simulated progress curves. To analyze the kinetics of this enzyme using the strict steady-state initial rate equation, several curves for different substrate concentrations and different rate constants are generated. A comparison of these curves with the curves obtained from the rapid equilibrium initial rate equation, with different substrate concentration values, serves to analyze how the strict steady-state rate equation values are closer to those of rapid equilibrium rate equations when rapid equilibrium conditions are fulfilled.

Characterization of hydrophobic organic contaminant biodegradation by COD analysis

Biodegradation of hydrophobic organic contaminants is often accompanied by the accumulation of hydrophobic intermediates. This paper is devoted to the experimental evaluation of electron balance between the hydrophobic intermediates and the other biodegradation products. The ratio of the fraction of electrons that are not held in the hydrophobic intermediates to the total fraction of electrons released from the substrate during its conversion is called a degradation level (DL). Under certain conditions, the DL value is equal to the angular coefficient of the plot in the coordinates ( 1 − ( S / S 0 ) ; 1 − ( COD / COD 0 ) ) , where S and COD are current values of substrate concentration and chemical oxygen demand of the ethylacetate extract from the culture; S 0 and COD 0 are the respective initial values. This approach has been used for analysis of the conversion of PAHs by Sphingomonas sp. VKM B-2434. The DL values obtained correspond well to most published data on PAH metabolism by this strain. The DL values significantly increased as a result of addition of the strains consuming the products of conversion of PAHs by Sphingomonas sp. VKM B-2434. The proposed method seems useful for characterizing biodegradation of hydrophobic pollutants by both pure and mixed microbial cultures.

MODELING OF SQUID PROTEIN HYDROLYSIS: ARTIFICIAL NEURAL NETWORK APPROACH

ABSTRACTThe processing of squid generates nearly 50% of raw material as a waste by‐product rich in protein. Use of this by‐product as a raw material in the manufacture of a squid protein hydrolysate would add considerable product value to the industry. Enzyme hydrolysis of squid waste protein is a complex process because of a number of inherent simultaneous inhibition and enzyme inactivation reactions which occur during hydrolysis. Artificial neural networks (ANNs) are very effective in developing predictive models for processes involving complex reaction kinetics that would otherwise be difficult to develop by more traditional deterministic approaches. The objective of this work was to develop a kinetic model to describe the kinetics of enzyme hydrolysis of squid waste protein using ANNs. A series of enzyme hydrolysis experiments were carried out on samples of squid waste under specified conditions of temperature, pH, and initial enzyme and substrate concentrations. Experimental data in the form of substrate concentration over time were taken as real time course data. These data were fitted with a cubic spline procedure for use in an ANNs training process to estimate reaction rates for the model. The effect of a number of hidden processing elements on the error in prediction was studied, and the ANN showing necessary prediction performance was constructed. Maximum differences between experimental and predicted values of substrate concentration at any point in time ranged from 0.098 to 0.29 g/L (3–4%). Correlation coefficients between predicted and experimental results were also, respectively, high, ranging from 0.988 to 0.992.PRACTICAL APPLICATIONSResults from this study would suggest that artificial neural networks (ANNs) can be very effective in developing predictive models for processes involving complex reaction kinetics. Enzyme hydrolysis of squid waste protein is an example of such a process because of a number of inherent simultaneous inhibition and enzyme inactivation reactions occurring during hydrolysis. This work resulted in the successful development of a kinetic model to describe the kinetics of enzyme hydrolysis of squid waste protein using ANNs.

Kinetic and Optimization Studies on Ethanol Production from Corn Flour

Studies on Simultaneous Saccharification and Fermentation (SSF) of corn flour, a major agricultural product as the substrate using starch digesting glucoamylase enzyme derived from Aspergillus niger and non starch digesting and sugar fermenting Saccharomyces cerevisiae in a batch fermentation. Experiments based on Central Composite Design (CCD) were conducted to study the effect of substrate concentration, pH, temperature, enzyme concentration on Ethanol Concentration and the above parameters were optimized using Response Surface Methodology (RSM). The optimum values of substrate concentration, pH, temperature and enzyme concentration were found to be 160 g/l, 5.5, 30°C and 50 IU respectively. The effect of inoculums age on ethanol concentration was also investigated. The corn flour solution equivalent to 16% initial starch concentration gave the highest ethanol concentration of 63.04 g/l after 48 h of fermentation at optimum conditions of pH and temperature. Monod model and Logistic model were used for growth kinetics and Leudeking - Piret model was used for product formation kinetics.

Simultaneous saccharification and fermentation of wheat bran flour into ethanol using coculture of amylotic Aspergillus niger and thermotolerant Kluyveromyces marxianus

Studies on simultaneous saccharification and fermentation (SSF) of wheat bran flour, a grain milling residue as the substrate using coculture method were carried out with strains of starch digesting Aspergillus niger and nonstarch digesting and sugar fermenting Kluyveromyces marxianus in batch fermentation. Experiments based on central composite design (CCD) were conducted to maximize the glucose yield and to study the effects of substrate concentration, pH, temperature, and enzyme concentration on percentage conversion of wheat bran flour starch to glucose by treatment with fungal α-amylase and the above parameters were optimized using response surface methodology (RSM). The optimum values of substrate concentration, pH, temperature, and enzyme concentration were found to be 200 g/L, 5.5, 65°C and 7.5 IU, respectively, in the starch saccharification step. The effects of pH, temperature and substrate concentration on ethanol concentration, biomass and reducing sugar concentration were also investigated. The optimum temperature and pH were found to be 30°C and 5.5, respectively. The wheat bran flour solution equivalent to 6% (w/V) initial starch concentration gave the highest ethanol concentration of 23.1 g/L after 48 h of fermentation at optimum conditions of pH and temperature. The growth kinetics was modeled using Monod model and Logistic model and product formation kinetics using Leudeking-Piret model. Simultaneous saccharificiation and fermentation of liquefied wheat bran starch to bioethanol was studied using coculture of amylolytic fungus A. niger and nonamylolytic sugar fermenting K. marxianus.

Estimating Reaction Rates in Squid Protein Hydrolysis Using Artificial Neural Networks

In this study, the use of artificial neural networks (ANN) for estimating reaction rates in enzymatic hydrolysis of squid waste protein was investigated. This is a complex process because a number of inherent simultaneous inhibition and enzyme inactivation reactions occur during hydrolysis that make it difficult to develop a reliable kinetic model by more traditional deterministic approaches. A series of 12 enzyme hydrolysis experiments were carried out on samples of squid waste under specified conditions of temperature, pH, and initial enzyme and substrate concentrations. Experimental data in the form of substrate concentration over time were taken as real time course data (TCD), and divided into three groups for respective use in training, validating, and testing the model. A feed-forward architecture was utilized to construct the necessary predictive model. The network was trained until the mean squared error function between target and actual output values reached a desired minimum. Data sets from the remaining two groups were used for subsequent validation and testing of the model. The model performed well when tested against experimental data in the third group (not used in its development) and taken over a wide range of initial conditions. Maximum differences between experimental and predicted values of substrate concentration at any point in time ranged from 0.3 to 0.5 g L-1 (1% to 3% of initial substrate concentration), with correlation coefficients between predicted and experimental results ranging from 0.95 to 0.97.