As Energy is Being Reconverted Through the Many Forms It is Continuously Lost as

Enzymes

Shijie Liu , in Bioprocess Engineering (Second Edition), 2017

7.2.3 Specific Activity

Enzyme concentrations are often given in terms of "units" rather than in mole or mass concentration. We rarely know the exact mass of the enzyme in a sample, since it is generally prepared via isolation of the enzyme from microorganisms, or animal or plant tissues, and often contains a great deal of noncatalytic protein, the amount of which may vary from sample to sample. Hence a different approach must be adopted, and enzyme concentration is reported in units of specific activity. A "unit" is defined as the amount of enzyme (eg, microgram) that gives a certain amount of catalytic activity under specified conditions (eg, producing 1.0 micromole of product per minute in a solution containing a substrate concentration sufficiently high to be in the "saturation" region, as shown in Fig. 7.9 where [ES] and [E] are relatively invariant).

Thus different suppliers of enzymes may have preparations with different units of activity, and care must be taken in analyzing kinetic data. Thus a purified enzyme preparation will have a higher specific activity than a crude preparation; often a protein is considered pure when a constant specific activity is reached during the purification steps.

(7.15) $\mathrm{Specific}\mathrm{activity}=\frac{\mathrm{Activity}}{\mathrm{mg}-\mathrm{protein}}=\frac{\mathrm{mmol}-\mathrm{product}}{\mathrm{mg}-\mathrm{protein}\times min\times \mathrm{mL}}$

The activity is given by the amount of product formed or substrate consumed in the reaction mixture, under the conditions specified (temperature, pH, buffer type, substrate and enzyme concentrations, etc.). If the molecular weight of the enzyme is known, the specific activity can also be defined as

(7.16) $\mathrm{Specific}\mathrm{activity}=\frac{\mathrm{Activity}}{\mathrm{mmol}-\mathrm{protein}}=\frac{\mathrm{mmol}-\mathrm{product}}{\mathrm{mmol}-\mathrm{protein}\times min\times \mathrm{mL}}$

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Membrane reactors for biodiesel production

S. Curcio , E. Ricca , in Membranes for Clean and Renewable Power Applications, 2014

5.6 Appendix: abbreviations and symbols

[e ₀]

enzyme concentration (mol  m^–3)

[EO]

ethyloleate concentration (mol   m^–3)

[Et]

ethanol concentration (mol   m^–3)

[Et₀]

ethanol initial concentration (mol   m^–3)

[P]

overall concentration of glycerol, monolein and diolein (mol   m^–3)

[S ₀]

substrate concentration (mol   m^–3)

[T]

triolein concentration (mol   m^–3)

[T ₀]

triolein initial concentration (mol   m^–3)

K_i (i  =   1,…,12)

kinetic constants

t

time (s)

Greek symbols

Θ

bioreactor productivity (dimensionless)

α

kinetic constant (Equation [5.3])

β

kinetic constant (Equation [5.3])

δ

kinetic constant (Equation [5.3])

δ₀

kinetic parameter (Equation [5.4])

δ₁

kinetic parameter (Equation [5.4])

ε

kinetic constant (Equation [5.3])

ε₀

kinetic parameter (Equation [5.4])

ε₁

kinetic parameter (Equation [5.4])

ε₂

kinetic parameter (Equation [5.4])

τ_R

residence time in bioreactor (s)

ψ

degree of conversion (dimensionless)

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A Novel Enzymatic Technology for Removal of Hydrogen Sulfide from Biogas

Angela M. Moreno , ... Ashish Mhadeshwar , in New and Future Developments in Catalysis, 2013

2.3.1 Effect of Enzyme Concentration

Figure 2.2 a shows the effect of enzyme concentration (1.5–5%) on the H ₂S removal from simulated biogas for a period of 8   h. Yellow line represents the H₂S concentration in the feed stream (3009   ppm). H₂S in the outlet gas was detected only for enzyme concentrations of ≤4%. H₂S concentration lines show a breakthrough that depends on the enzyme concentration. The time for H₂S breakthrough increases with the enzyme concentration. For example, for 1.5% enzyme solution, the breakthrough occurs at ∼1.5   h, for 2% at ∼5   h, and for 4% at ∼7.5   h. The sudden step change in H₂S concentration suggests that at this time the limited quantity of enzyme is deactivated due to high H₂S concentration.

Figure 2.2. Transient profiles of outlet H₂S concentration for experiments conducted with simulated biogas flowing through various concentrations of enzyme solutions. The biogas flow rate is fixed at 10 sccm. Panels (a) and (b) correspond to 8   h and 24   h tests, respectively. Almost 100% H₂S removal is observed with enzyme concentrations ≥4% in the 8   h tests. H₂S breakthrough is delayed and is more gradual (lower slope) with increased enzyme concentration.

Enzyme concentration of 5% was also analyzed with no H₂S response during the first 8   h (shown in Figure 2.2b). These results demonstrate that a small quantity of enzyme is sufficient for a high removal of H₂S. Similar experiments to the ones described above were performed for 24   h in order to investigate the enzyme's long-term ability to remove H₂S (Figure 2.2b). Results showed up to a 12-h delay in H₂S breakthrough by increasing concentration from 2% to 5%. A comparison with water (see Figure 2.2a and b) shows the active effect of enzyme chemistry vs. simple absorption or solubility of H₂S in water. Enzyme saturation occurs more gradually at higher concentrations. For the cases where H₂S breakthrough occurred, the pH significantly reduced to ∼4.5–4.7 from a starting pH of 7.8.

In addition, the enzyme did not have any adverse effect on the CH₄ and CO₂ concentrations (Figure 2.3). It takes about 2.5   h for the system to equilibrate and evacuate the air initially contained in the reactor; during this time, CH₄ concentration in the outlet is initially greater than 59.7% due to the overlap between air (N₂, O₂) and CH₄ GC peak areas.

Figure 2.3. Transient profiles of outlet CH₄ and CO₂ concentrations for experiments conducted with simulated biogas flowing through various concentrations of enzyme solutions. The operating conditions are same as in Figure 2b. The enzyme has a negligible effect on the CH₄ and CO₂ concentrations in simulated biogas, indicating its selectivity to H₂S removal.

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12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering

Inês P. Rosinha , ... Ulrich Krühne , in Computer Aided Chemical Engineering, 2015

3 Results and Discussion

The optimization loop ends when the maximum enzyme concentration (1.2·10 ⁻¹²  mol·m²) was achieved. On the one hand, 6.75·10⁻²  mM of substrate were converted with the initial configuration, on the other hand, 6.89·10⁻²  mM of substrate were converted with the final shape. In the end, the topology optimization resulted in an improvement of 2%of conversion of the substrate per same amount of enzyme compared with the initial enzyme configuration. It is expected that the application of a topology optimization to a more complex reaction mechanism (with product or/and substrate inhibition) will result in larger improvements. The results of the topology optimization as well as the optimal enzyme configuration can be found in Figure 3. The final configuration of the enzyme is characterized by a higher concentration of enzyme close to the walls and in immobilization elements close to the outlet. This demonstrates that immobilized molecules at locations with higher residence time, due to low fluid velocity, contribute more to the product formation. In addition, within the immobilization elements with maximum velocity, the elements which are close to the outlet are the ones which seem to have more influence on the product formation.

Figure 3. Results from the topology optimization: Concentration of substrate (hydrogen peroxide) and product (water)inside the microreactor for initial and final configurations and immobilized enzyme (catalase) on the bottom surface of the microreactor.

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Topics of Bioluminescence and Chemoluminescence

Akio Makishima , in Biochemistry for Materials Science, 2019

5.3.1 Introduction

The chemoluminescence probe is the most sensitive method for determination of enzyme concentration, because it has a very high signal-to-noise ratio. Therefore, the chemoluminescence probe is used in immunoassays including DNA. Most chemoluminescence probes emit lights by the mechanism as follows: the reaction starts by oxidation of an oxidizing agent, which is oxidized into an unstable peroxide, making a light-emission-material into its excited state; it then rapidly decays to the ground state with emitting light. The oxidation-based mechanism is utilized for the activation of common chemoluminescence substrates such as luminol or oxalate esters. Furthermore, oxidation-activated chemoluminescence has been used to detect and image reactive oxygen species (ROS) in vitro and in vivo.

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28th European Symposium on Computer Aided Process Engineering

Fergus McIlwaine , Dimitrios I. Gerogiorgis , in Computer Aided Chemical Engineering, 2018

3 Results and Discussion

Reaction kinetic and transport phenomena parameters and initial conditions (including the initial enzyme concentration, E ₀) used as a base case in the model are shown in Table 1. The choice of initial glucose (substrate) concentration, C _S, is based on the minimum fasting blood glucose concentration for a healthy individual (Sarwar et al., 2010). Sensitivity analyses investigate the effect of varying reactant and product diffusivities in the droplet and enzyme layer and initial blood glucose concentration on the glucose biosensor electrode current density, as well as output reactant and product concentration profiles. Other parameters are kept constant for all sensitivity analyses.

Table 1. Base-case kinetic and transport parameters and initial conditions for the model.

Parameter	Units	Value
k +   1	m3 mol−   1 s−   1	1.20·10−   2
k −   1	s−   1	0.68·10−   3
k 2	s−   1	5.00·10−   2
k M	mol m−   3	4.22
E 0	mol m−   3	1.25·10−   1
V max	mol m−   3 s−   1	6.30·10−   3
D S,droplet	m2 s−   1	6.00·10−   10
D S,enzyme	m2 s−   1	3.00·10−   10
D P,droplet	m2 s−   1	6.00·10−   10
D P,enzyme	m2 s−   1	3.00·10−   10
C S0	mmol L−   1	4.00

Fig. 2 shows sensitivity analyses for the transient current density of the biosensor electrode. At low substrate diffusivities in the droplet (D _S,droplet), longer timescales are required for substrate to reach the enzyme layer and produce current (Eq. 3); consequently, a plateau in current density is gradually attained. For high D _S,droplet, substrate reaches the enzyme layer faster than it is reacted, leading to an initial peak in current density followed by a gradual decrease. For low substrate diffusivities in the enzyme layer (D _S,enzyme), lesser enzyme layer surface areas are available for substrate reaction, and hence current density peaks are observed followed by gradual decline. As D _S,enzyme increases, a greater surface area is available for reaction, and thus plateaus are gradually attained. As product diffusivities in the droplet (D _P,droplet) increase, less product produced in the enzyme layer occupies enzyme surface area for reaction, hence induced current densities increase. When product diffusivity in the enzyme layer (D _P,enzyme) is lower, less area is available for reaction, due to the lower propensity for product transport from enzyme, and longer times are required for maximum currents.

Figure 2. Effect of model parameters on transient biosensor electrode current densities.

Transient current densities illustrated in Fig. 3 clearly increase with initial blood glucose concentrations; greater initial substrate (glucose) concentrations incur greater amounts of H₂O₂, which thus induces greater current (eqs. 1-3). Current densities plateau beyond 500   s for all initial concentrations considered. Biosensor performance is strongly dependent on patient-to-patient physiological variability, as well as on the temperature dependence and sensitivity of hence varying enzymatic reaction kinetic parameters.

Figure 3. Sensitivity of output concentration profiles vs. model parameters (Table 2).

Fig. 3 shows the variation of substrate and product concentration profiles in the droplet with varying transport properties; parameter values associated with individual analyses are provided in Table 2. Increasing initial blood glucose concentrations lead to higher concentration profiles throughout the droplet. As substrate diffusivity in the droplet (D _S,droplet) increases, concentrations decrease throughout the droplet; expectedly, product concentrations decrease with increasing distance (y) from the enzyme layer. Increasing product diffusivity in the droplet (D _P,droplet) significantly affects profiles due to the effect of product diffusion on enzyme layer surface availability discussed previously. Increasing substrate diffusivities in the enzyme layer (D _S,enzyme) result in decreasing concentrations in the analysed blood droplet. Increasing product diffusivities in the enzyme layer (D _P,enzyme) incur rapidly decreasing product concentrations in the droplet due to the preferential diffusion of product in the enzyme layer to the blood droplet.

Table 2. Parameter variation for the model-based sensitivity analysis shown in Fig. 3.

Fig. 3 notation	C s0 (mmol L−   1)	D enzyme·1010 (m2 s−   1)	D droplet·1010 (m2 s−   1)
①	2.0	0.3	0.6
②	6.0	1.0	2.0
③	10.0	3.0	6.0
④	14.0	8.0	16.0
⑤	–	23.0	46.0

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Microbial Fermentation Strategies for Biomass Conversion

Hongzhang Chen , Lan Wang , in Technologies for Biochemical Conversion of Biomass, 2017

7.1.1 Characterizations of Simultaneous Saccharification and Fermentation

In order to overcome the product's feedback inhibition, Gauss, Suzuki, and Takagi (1976) proposed that saccharification and ethanol fermentation of cellulose be conducted in the same fermenter. The SSF process is shown in Fig. 7.2. Cellulose hydrolysis and fermentation of hexoses were conducted in the same reactor, and glucose generated during cellulose hydrolysis was rapidly utilized by microorganisms. Feedback inhibition of glucose was removed, which improved the efficiency of enzymatic hydrolysis and reduced the amount of cellulase used for hydrolysis. The desired reaction apparatus is reduced, and the likelihood of contamination is also reduced.

Figure 7.2. The systems of simultaneous saccharification and fermentation (SSF) of biomass (Hahn-Hägerdal, Galbe, & Gorwa-Grauslund, 2006).

Glucose and lignin are not separated during SSF, which eliminates the loss of sugar, reduces the number of reactors, and hence decreases investment costs (by about 20%). In addition, SSF can be applied for synergy fermentation of hexose and pentose. SSF has obvious advantages in the detoxification process. Through studies on the influence factors of SSF, results showed that enzymatic hydrolysis remains a major limiting factor for SSF. The reason for this phenomenon is that the optimum temperature of hydrolysis and fermentation is inconsistent (Chen, Li, & Chen, 1999). The optimum temperature for enzymatic hydrolysis is generally about 50°C, while the optimum fermentation temperature of Saccharomyces cerevisiae is generally about 30°C. Selected thermal-tolerant yeast is helpful for SSF technology. The key issue of SSF technology is the choice of the most suitable yeast. Yeast such as S. cerevisiae, Candida brassicae, is commonly used during SSF study.

The best cellulase for SSF technology is currently secreted from the mutagenic Trichoderma reesei strain. Since the 1970s, Natick Army laboratories and Rutgers University have used the wild type Trichoderma viride (T. viride QM6a) as a starting strain, and they also obtained the high-yielding cellulase strains of QM9414, Rut-C30, and Rut-NG-14. The species name was then changed to T. reesei, in honor of E. T. Reese. The cellulase activity of these strains has been 10–15 times higher than that of the wild strain, which is now recognized as the most dynamic cellulase.

In addition to yeast and T. reesei cellulase, used for the SSF process, a number of other species and enzymes, such as the combination of cellulase enzyme produced by Thermophilic Micromonospora (Thermomonospora sp.) and thermophilic cellulose Clostridium (Clostridium thermocellum), were also studied. The most noteworthy fermentation bacteria are swimming Micromonospora (Z. moqilis). The rate of ethanol production by Zymomonas moqilis is approximately three times higher than by the yeast, and ethanol yield was also slightly higher than with yeast, with the theoretical yield up to 96%∼97%. The glucose metabolism pathway was Endurance's pathway, one molecule of glucose would produce two molecules of ethanol, two molecules of carbon dioxide and one molecule of ATP. The optimum pH for ethanol production was 4.5–6.0 for Z. moqilis, which is in accordance with the optimum pH of cellulase production by T. reesei. Its optimum growth temperature and fermentation temperatures are 30°C. The characteristic distinguishing it from yeast is that in response to the appropriate increase in temperature or the appropriate limited nutrient, the cell will stop growing, but fermentation will still proceed as usual. For example, at 37°C, ethanol yield is the same as at 30°C, but cell growth has stopped at this temperature. This feature of Z. moqilis can further improve the conversion rate of substrate (i.e., ethanol yield). For a relatively long period of cellulose fermentation, this is important in economics.

We should pay attention to two issues during the SSF process. The first problem arises if the optimum enzymatic hydrolysis temperature and the optimum yeast fermentation temperature are uniform, (they are generally 50°C and 30°C, respectively). The best temperature of cellulase produced by T. reesei was 45∼50°C, while the general fermentation temperature of yeast is less than 30°C, and yeast will stop growth and fermentation above 37°C. If fermentation is conducted below 30°C, enzyme activity will greatly reduce. Since enzymatic hydrolysis is the rate-limiting step during SSF, so reducing the SSF fermentation temperature is not a good solution. In order to do both, the SSF temperature generally used is 37∼38°C, but this still cannot produce optimal enzymatic hydrolysis and fermentation conditions. Therefore, the selection of thermal- and ethanol-tolerant yeast during SSF is an important research topic.

To solve the above problem, people are studying the process and other aspects of breeding. For instance, thermal-tolerant yeast and bacteria replace traditional yeast, which increases the reaction temperature. In this way the optimum fermentation temperature approaches the temperature of enzymatic hydrolysis, which increases the hydrolyzing rate of cellulose. Szczodrak in 1988 selected 58 kinds from 12 different thermal-tolerant yeast genera, and the growth and fermentation ability of these yeasts at temperatures above 40°C were tested. It was found that Fabospora fragilis CCY51-1-l could convert 140 g/L glucose into 56 g/L ethanol at 43°C, in which process the ethanol conversion rate reached 74% of its theoretical value. However, when the temperature rose to 46°C, the fermentation capacity significantly decreased, and ethanol conversion was only 46% (Lv, 2009; Szczodrak & Targoński, 1988). At present, normal yeast fermented at 40∼46°C has been initially successful in breeding, as evinced in Kluyveromyces marxianus, Kluyveromyces fragilis, Fabospora fragilis, and so on. The fermentation temperature of these three strains was almost exactly the optimum temperature of the enzyme. In addition to selecting thermal-tolerant yeast, the Japanese proposed using the normal yeast fermentation method for SSF, and so this is called the "oriental fermentation method." This method also has been relatively successful, but the fermentation time is too long.

SSF was achieved by using improved devices and process. Xiao Xin from the Institute of Process Engineering proposed dispersion, coupling, and parallel system for bioconversion of cellulosic ethanol production (Xiao & Li, 2000). The installation is shown in Fig. 7.3. In the three phases of this system, saccharification, fermentation, and separation of alcohol were carried out. In the saccharification part, hydrolysis can be carried out at a higher temperature, and hydrolyzate can be separated into enzymes and enzymatic hydrolyzate through the nuclear pore membrane. The cellulase component is returned to saccharification and continues to hydrolysis, sugar continues into the fermentation segment. This will not only solve the inconsistencies of reaction temperature and fermentation temperature, but also remove the glucose inhibition effect on the enzyme. Similarly, in the fermentation part, the yeast cells and fermentation broth are separated by a membrane, the yeast cells return and continue fermentation, and the fermentation broth can be separated for obtaining ethanol by distillation, which removes the ethanol inhibition effect on yeast. Using this system for hydrolysis of cellulose, the cellulose conversion rate reached 81%, while the final conversion rate of cellulose hydrolysis by the general procedure was 66%. The efficiency of the former is 3.9 times that of the latter. Ethanol concentration, fermentation rate and cellulose conversion yield during cellulose ethanol production by this system were 8.14%, 0.66 g/(L·h) and 80.1%, respectively, which is 1.8, 1.3, and 1.7 times the equivalent in SSF.

Figure 7.3. Ethanol production system with dispersion, coupling, and parallel sections.

Another incompatibility problem of microbe and enzymes should be noted during SSF fermentations. Cell lysates or cell secretions (such as proteases) could damage the cellulase, and certain components of the crude enzyme preparation may affect cell growth, as well as the use of sugar and ethanol yield.

The factors of SSF should include as follows (He, 1990):

1.

Enzyme concentration. The higher enzyme concentration results in higher ethanol production, especially in the low enzyme concentration range.

2.

The initial concentration of yeast cells. When cell concentration is around 2.5∼10 × 10⁷ cells/mL in inoculation, ethanol production is not affected.

3.

Temperature and pH. Optimum temperature and pH are different for cellulase and yeast.

4.

Pretreatment. Pretreatment of cellulosic material before hydrolysis is usually necessary. Without pretreatment, fermentation efficiency and conversion rate are often low, but the pretreatment process is often the major cause of rising costs.

5.

Substrate concentration. The high substrate concentration generally results in an increase in ethanol concentration and production. But the degradation rate of the substrate decreases slightly, generally speaking, which is beneficial for the fermentation. However, it is difficult for the water to penetrate the material when the substrate concentration is high, which has made cultivation difficult. One solution is to put the substrate and enzyme into the reactor during the fermentation process, which can effectively improve the ethanol concentration.

In the general SSF process, the pretreated liquid enriched with pentose was fermented alone. With the new development of simultaneous microbial fermentation of glucose and xylose, the industrial development of simultaneous saccharification and cofermentation (SSCF) (combination of bioconversion) was proposed. The sugar solution from pretreatment and the treated cellulose are put in the same reactor, further simplifying the process.

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Strategies to improve enzymatic production of sugars

Mihir Kumar Purkait , Dibyajyoti Haldar , in Lignocellulosic Biomass to Value-Added Products, 2021

7.2 Effect of pH, temperature, solid loading, and enzyme concentration during enzymatic hydrolysis of biomass

The efficiency of any enzymatic hydrolysis process is highly influenced by a number of different factors, such as enzyme concentration, substrate loading, and the reaction condition of the system. Therefore, the optimum dosage of the enzyme in the reaction system is a critical factor in the economic viability of enzymatic hydrolysis. However, the dosage of the enzyme varies significantly depending on the nature and concentration of the treated biomass as the substrate. Hence, the concentration of the enzyme necessitates an optimum dose so that the process cost of biofuel production is more cost attractive than that of fossil fuel-derived biofuels. Apart from the concentration of enzyme, solid loading is another fundamental parameter that directly affects the process of enzymatic hydrolysis and high solid loading is preferred in most of the real cases in industry. However, on some occasions, the performance of enzymatic hydrolysis at high solid loading is drastically affected due to nonproper agitation efficiency and limited contact between enzyme and substrate molecule ( Ramachandriya, Wilkins, Atiyeh, Dunford, & Hiziroglu, 2013).

Fig. 7.1A shows the production of total reducing sugars (TRS) at different biomass loadings during the enzymatic hydrolysis of waste banana stem. From the figure it can be seen that a maximum 32   g/L of TRS was formed for 1:10 biomass loading, whereas a minimum of 13.7   g/L of TRS was observed at 1:20 solid loading of biomass. It is also important to note that due to higher substrate concentration at solid loading of 1:10, the reaction medium for enzymatic hydrolysis was more viscous compared to solid loading of 1:20. Fig. 7.1B shows the probability of forming TRS through enzymatic hydrolysis for 48   h is much more significant with 1:20 compared to the 1:10 solid loading.

Fig. 7.1. (A) Effect of solid loading on enzymatic production of TRS. (B) Probability of TRS production at any instant during the reaction period of 48   h.

(Reproduced with permission from Haldar, D., Sen, D., & Gayen, K. (c. 2018). Enzymatic hydrolysis of banana stems (Musa acuminata): Optimization of process parameters and inhibition characterization. International Journal of Green Energy, 15(6), 406–413. https://doi.org/10.1080/15435075.2018.1467834.)

Fig. 7.2 shows the effect of enzyme dosage at different concentrations on TRS production from biomass. From the figure, it can be seen that TRS of 9.4, 14.6, and 19.6   g/L were obtained respectively after 48   h of enzymatic hydrolysis at 50°C and 4.8 pH in the presence of 10, 20, and 30 FPU enzyme concentrations. Further, an increase in TRS of 54.2% was accounted with 20 FPU of enzyme concentration compared to that of 10 FPU. An increase in TRS production was reduced to 34.8% with 30 FPU of enzyme concentration rather than 20 FPU. Therefore, with regard to a cost analysis, an increase of 27.1% in TRS per unit fold of enzyme price increase is observed with 20 FPU compared to 10 FPU. However, a relatively lower increase in TRS of only 23.2% per unit fold of enzyme price increase was observed for 30 FPU than to 20 FPU. As a result, the application of 30 FPU was restricted to be the enzyme concentration even though the absolute yield was higher with lower economic process condition.

Fig. 7.2. Production of TRS at different enzyme concentrations during enzymatic hydrolysis of biomass.

(Reproduced with permission from Haldar, D., Sen, D., & Gayen, K. (c. 2018). Enzymatic hydrolysis of banana stems (Musa acuminata): Optimization of process parameters and inhibition characterization. International Journal of Green Energy, 15(6), 406–413. https://doi.org/10.1080/15435075.2018.1467834.)

Fig. 7.3 shows the enzymatic production of sugars from biomass at different buffer pH. A maximum 13.7   g/L of TRS was observed at pH 4.8 while an insignificant variation in sugars formation (with average 11.1   g/L of TRS) was accounted at pH 4.45 and 5.48 compared to 6.16.

Fig. 7.3. Effect of different buffer pH of reaction medium on enzymatic production of sugars.

(Reproduced with permission from Haldar, D., Sen, D., & Gayen, K. (c. 2018). Enzymatic hydrolysis of banana stems (Musa acuminata): Optimization of process parameters and inhibition characterization. International Journal of Green Energy, 15(6), 406–413. https://doi.org/10.1080/15435075.2018.1467834.)

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Fundamentals of Biochemical Reaction Engineering

Shaofen Li Professor , in Reaction Engineering, 2017

11.2.1.4 Factors Affecting Reaction Rate of Enzyme-Catalyzed Reactions

There are many factors that can affect the reaction rate of enzyme-catalyzed reactions. Besides previously described enzyme concentration, substrate concentration, product concentration, and inhibitor concentrations, temperature, pH value, and ionic strength will affect reaction rate too.

1.

Temperature effect. Temperature can have both a positive and negative impact on the reaction rate of enzymatic reactions. On one hand, reaction rate increases with temperature and the dependence of reaction rate on temperature follows the Arrhenius equation. On the other hand, extremely high temperature will denature zymoprotein and decrease the activity of the enzyme or even deactivate the enzyme. Due to these two effects, enzymatic reactions have optimal temperatures, and the reaction rate versus temperature plot exhibits a bell shape.

2.

pH effect. For any enzymatic reactions, zymoprotein, substrate, and intermediate complex have specific dissociation forms and a most favorable pH value, at which reaction rate is maximized. Extremely high or low pH will affect the dissociation of the acidic group, such as carboxyl, or basic group, such as amide, in the active site of the enzyme, reduce the activity of enzyme, or even destroy the structure of enzyme and affect the stability of enzyme.

Therefore, for enzyme-catalyzed reactions, substrate concentrations, product concentrations, temperature, and pH values need to be controlled appropriately.

Example 11.2

An enzyme-catalyzed reaction was carried out at room temperature. At given constant initial enzyme concentration, the initial reaction rate at different substrate concentrations were measured and are listed in Table 11B. Inhibitor was added under the above conditions at a concentration of 1.00×10⁻⁵  mol/L and initial reaction rate r _sI was measured at different substrate concentrations. Results are shown in Table 11B. Please determine the type of inhibition and calculate the kinetic parameters K _m, r _max, and K _I based on the experimental data.

Table 11B. Initial Reaction Rates at Different Substrate Concentrations

c s×103/(mol/L)	0.333	0.400	0.500	0.667	1.00	2.00
r s×106/(mol·L/min)	55.6	62.9	73.0	87.0	107	139
r sI×106/(mol·L/min)	35.5	41.2	48.8	60.2	78.7	113

Solution

Assuming inhibition is competitive, linearizing Eqs. (11.1) and (11.2) and rearranging gives

(A) $\frac{1}{r_{\mathrm{s}}}=\frac{1}{r_{\max }}+\frac{K_{\mathrm{m}}}{r_{\max }}\cdot \frac{1}{c_{\mathrm{s}}}=a+b\cdot \frac{1}{c_{\mathrm{s}}}$

(B) $\frac{1}{r_{\mathrm{sI}}}=\frac{1}{r_{\max }}+\frac{K_{\mathrm{mI}}}{r_{\max }}\cdot \frac{1}{c_{\mathrm{s}}}=a\prime +b\prime \cdot \frac{1}{c_{\mathrm{s}}}$

From Eqs. (A) and (B), we can tell that both 1/r _s ~1/c _s and 1/r _sI ~1/c _s are straight lines with the same intercept on ordinate at 1/r _max.

1/c _s, 1/r _s, and 1/r _sI are calculated with the data in Table 9C, and results are listed in Table 11C

Table 11C. Results calculated with the data in Table 9C

1/c s×10−3/(L/mol)	3.00	2.50	2.00	1.50	1.0	0.5
1/r s×10−3/(min·L/mol)	18.0	15.9	13.7	11.5	9.35	7.19
1/r sI×10−3/(min·L/mol)	28.2	24.3	20.5	16.6	12.7	8.85

Substituting the corresponding data in Table 9C into Eqs. (A) and (B), the following values can be obtained by the least square regression method:

$a=5.01\times 10a=5.01\times {10}^{3}3$ ; $b=4.34$ ; correlation coefficient r=1.0

$a^{\prime }=4.99\times {10}^3$ ; $b^{\prime }=7.74$ ; correlation coefficient r=1.0

Results showed that the intercepts on the ordinate of the two lines are very close. Therefore, the assumption of competitive inhibition is valid. From Eqs. (A) and (B) :

$r_{\max }=\frac{1}{(a+a^{\prime })/2}=\frac{1}{(5.01\times {10}^3+4.99\times {10}^3)/2}=2.00\times {10}^4$

$K_{\mathrm{m}}=b·r_{\max }=4.34\times 2.00\times {10}^{-4}=8.68\times {10}^{-4}\mathrm{mol}/\mathrm{L}$

$K_{\mathrm{mI}}=b^{,}·r_{\max }=7.74\times 2.00\times {10}^{-4}=1.55\times {10}^{-3}\mathrm{mol}/\mathrm{L}$

From Eq. (11.2), we get

$K_I=c_{\mathrm{I}}/\left(\frac{K_{\mathrm{mI}}}{K_{\mathrm{m}}}-1\right)=1.00\times \frac{{10}^5}{\frac{1.55\times {10}^{-3}}{8.68\times {10}^{-4}}-1}=1.27\times {10}^{-5}\mathrm{mol}/\mathrm{L}$

Fig. 11A shows that for the above two conditions, the vertical intercepts of both straight lines are 5.0×10³  min·L/mol. Therefore, the vertical intercepts of the L-B line of the competitive inhibition is the same as the intercept when there is no inhibition. The intersection on the x-axis is (−1/K _mI). The L-B plot of noncompetitive inhibition can be obtained with a similar method. The intersection on the abscissa is same as when there is no inhibition, and the intersection on vertical axis is 1/r _SImax. When the reaction is competitive inhibition, its L-B line is parallel to the line when there is no inhibition.

Figure 11A. .

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Biochemical catalytic production of biodiesel

C. Luna , ... C. Verdugo-Escamilla , in Handbook of Biofuels Production (Second Edition), 2016

7.4.5 Statistical approaches for optimization of reaction

Lipase-catalyzed biodiesel production is influenced by number of factors such as temperature, methanol to oil molar ratio, enzyme concentration, water content, flow rate, in case of continuous process, and so on. Thus, optimization of these parameters becomes crucial to obtain maximum yields. Statistical methods such as response surface methodology (RSM) have been widely used for the optimization of lipase-catalyzed biodiesel production ( Verdugo et al., 2011; Luna et al., 2014b). Statistical methods give the advantage of studying a great number of parameters in fewer experimental setups. These methods also give a better understanding of interactions of the parameters as well as extent of on their influence on the reaction.

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Source: https://www.sciencedirect.com/topics/engineering/enzyme-concentration

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