Kinetic analysis of bioethanol production from by sugar fermentable fermentation derivativeed offrom sugarcane byproductsby-products

Bianca Yadira Pérez-Sariñana1§*, Alán Rogelio Gómez-Rincón2, Sergio Saldaña-

Trinidad2, Peggy Elizabeth Alvarez-Gutiérrez2, Sebastian P. J. 1§, Carlos Alberto

Guerrero-Fajardo3

1 Instituto de Energías Renovables-UNAM, Temixco, Morelos, 62580, México

2 Universidad Politécnica de Chiapas, Tuxtla Gutiérrez, Chiapas. 29010, México

3 Departamento de Química, Universidad Nacional de Colombia, Bogotá 11001,

Colombia.

*These authors contributed equally to this work

§Corresponding author

Email addresses:

BYPS: bipes@ier.unam.mx, biyapesa@gmail.com,

ARGR: tool_alan@hotmail.com

SST: ssaldana@upchipas.edu.mx

PEAG: peggy.alvarez@hotmail.com

SPJ: sjp@ier.unam.mx

CAGF: caguerrerofa@unal.edu.co

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Abstract BackgroundBioethanol is considered as an alternative energy source that is renewable and

environmentally friendly, it can be produced from agricultural raw materials with high

sugar content. It can be obtained from common crops such as sugarcane (Saccharum

officinarum). Microorganisms such as yeast can produce ethanol through anaerobic

fermentation by conversion of glucose in bioethanol and carbon dioxide.

ResultsExperimental design 2k factorial with central points was used with the substrate and

cell concentration as a variable with three levels for each parameter. This design was

performed to establish the formulation of media and fermentation conditions.

Our results show the kinetics of fermentation from sugarcane juice and sugarcane

molasses as substrate. BiomassCell concentration, bioethanol production, sugar

substrate consumption were analysed, the physical-chemical variables such as pH,

temperature were monitored. The model predicted that the maximum production of

bioethanol was using sugarcane juice to cell concentration of 1.2e+7 cfu/mL of yeast,

sugar concentration of 120.79 g/L of sugar, producing 40 g/L of bioethanol with a

yield of sugar consumption to bioethanol of 37.97%. Under these conditions,

experimental bioethanol production was 44.41 g/L and 38.35% of yield.

ConclusionsThe evaluation of sugar’s fermentation Fermentation evaluation of sugars from

sugarcane juice and sugarcane molasses, allowed to do statistical analysis and

optimize the bioetanol production.

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Background An alternative to fuel demand is the sustainable use of biomass conversion for energy

(bioenergy). Bioenergy has the potential to become an important part of sustainable

energy systems, contributing to reducing reduce emissions of greenhouse gases,

promoting sustainable development and may could gradually replace fossil fuels [1].

Ethanol derived from biomass is one of modern forms of bioenergy, it has the

potential to be a sustainable transport fuel and a fuel oxygenate that it can replace

gasoline [2].

There are several methods for transforming biomass into energy, the most widely

used is dare the thermochemical and biological processes [4]. The thermochemical

heat is usedused as an energy source of by biomass conversion and can be of three

types: combustion, pyrolysis and gasification.

Biological methods are based on the use of various types of microorganisms such as

bacteria, molds and yeasts, which degrade the molecules to simpler compounds of

high energy density, the best known is the alcoholic fermentation. In the literature

there are several types of microorganisms that produce biofuels, such as biodiesel, by

Rhodococcus opacus [5], E. Coli [6], Cyanobacteria and Microalgae among others.

The biohydrogen can be produced by Cyanobacteria and Microalgae [7] and mixed

acid organisms [8]. The biogas can also be produced by Cyanobacteria and

Microalgae [7]. Isobutanol may be produced by E. Coli [9] and others. And

bioethanol can be produced by Cyanobacteria and Microalgae [7], Clostridium [10],

Pichia stipitis [11], Laminaria digitata [12], Ceriporiopsis subvermispora [13],

Geobacillus [14], Saccharomyces cerevisiae, among others.

Saccharomyces cerevisiae is a microorganism producer of bioethanolAmong the

producers of bioethanol microorganism is the Saccharomyces cerevisiae, it is the most

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commonly used for fermentation, and like many organisms metabolizes glucose via

Embden-Meyerhof [15].

The aim of this study was to evaluate the fermentation of sugars to produce bioethanol

from sugarcane juice and sugarcane molasses, to do statistical analysis and to

optimize the system variables [3].

Results and discussion Growth kinetic of the yeast S. cerevisiae Y2034.Microbial growth explains because increase or decline of bioethanol production, in

other words: carbohydrates consumption is associated with specific growth rate and

bioethanol production rate. In a submerged culture of microorganisms, it can be

differentiated at least four phases: lag phase or adjustment, exponential phase,

stationary phase and death phase [16].

In exponential phase, number differential of cells with respect to time, is equal to

specific growth rate (µ) for the number of cells, in stationary phase the number of

cells with respect to time is equal to zero and in death phase there is a reduction of

viable cell, this reduction is represented for –k [17].

Growth kinetic of yeast S. cerevisiae with 50 and 150 g/L initial sugar from sugarcane

juice (SJ) is displayed in Figure 1. In graph a can be observed lag phase from 0 to 6h,

exponential phase lasted 12 h (6 to 18 h), where µ was 0.30 h-1 (▲), 0.281 h-1 () and

0.280 h-1 (). Death phase was comprised between 18 and 24 h where k was -0.080, -

0.034 and -0.018 for 1e+7, 2e+7 and 3e+7 cfu/mL initials cell concentration. Graph b,

shows lag phase from 0 to 6 h, exponential phase was 12 h (6 to 18 h), where µ was

0.404 h-1 (▲), 0.321 h-1 () and 0.332 h-1 (). Death phase was 18 to 24 h where k

was -0.050, -0.004 and -0.012 for 1e+7, 2e+7 and 3e+7 cfu/mL initials cell

concentration, respectively.

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Growth kinetic of yeast S. cerevisiae with 200 g/L initial sugar from SJ is showed in

Figure 2. It can be observed lag phase from 0 to 6 h, exponential phase lasted 12 h (6

to 18 h), where μ was 0.248 h-1, 0.248 h-1 and 0.224 h-1 for 1e+7, 2e+7, 3e+7 cfu/mL

initials cell concentration, respectively. Neither death phase was observed nor it could

see stationary phase due to long time intervals.

Comparing Figure 1 to Figure 2 was observed that μ in the graph a, the exponential

phase was higher than in the graph b, although in Figure 2 there was no death phase,

but it was obtained maximum number of cells, approximately 1.1e+10, 1.4e+10,

1.6e+10 to 1e+7, 2e+7 and 3e+7 cfu/mL initials cell concentration. This was because

of there was highest sugar concentration therefore fermentation would take longer.

Growth kinetic of yeast S. cerevisiae with 50 and 150 g/L initial sugar from sugarcane

molasses (SM) is displayed in Figure 3. In graph a can be observed that lag phase

from 0 to 6 h, exponential phase lasted 12 h (6 to 18 h), where µ was 0.260 h-1 (▲),

0.246 h-1 () and 0.239 h-1 (). Death phase was 18 and 24 h where k was -0.013, -

0.012 and -0.170 for initial cell concentrations 1e+7, 2e+7 and 3e+7 cfu/mL.

Graph b in Figure 3 was observed lag phase from 0 to 6 h, exponential phase was 12 h

(6 to 18 h) where µ was 0.340 (▲), 0.333 () and 0.337 (). Death phase was 18 to

24 h where k was -0.085, -0.023 and -0.012 for initial cell concentrations of 1e+7,

2e+7 and 3e+7 cfu/mL, respectively.

Growth kinetic of yeast S. cereviasie with 200 g/L initial sugar from SM is showed in

Figure 4. It can be observed that the adaptation phase was in a time interval from 0 to

6 h, exponential phase lasted 12 h (6 to 18 h) where μ was 0.007, 0.008 and 0 to 1e+7,

2e+7 and 3e+7 cfu/mL initial cells concentration, respectively. The death phase was

not observed.

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Comparing Figure 3 to Figure 4 shows that µ in the graph a, exponential phase was

higher than the graph b, although in phase Figure 4 there was no death phase, but a

maximum number of cells, approximately 4.2e+10 (▲), 4.5e+10 () and 5.3e+10

() to 1e+7, 2e+7 and 3e+7 cfu/mL initial cells concentration respectively. This was

because of there was highest sugar concentration therefore fermentation would take

longer.

Consumption kinetics of sugarsSugar consumption from SJ

In Figure 1 sugar consumption with 50 g/L initial sugar was 47.68 g/L, 47.72 g/L and

47.68 g/L; with 125 g/L initial sugar was 110.51 g/L, 110.48 g/L and 110.30 g/L, and

with 200 g/L initial sugar (Figure 2) was 183.21 g/L, 183.99 g/L and 183.53 g/L to

1e+7, 2e+7 and 3e+7 cfu/mL cell initial concentration, respectively.

Sugar consumption from SM

Figures 3 sugar consumption with 50 g/L initial sugar was 46.67 g/L, 46.63 g/L and

46.67 g/L; with 125 g/L was 112.53 g/L, 112.47 g/L and 112.13 g/L, and 200 g/L

initial sugar (Figure 4) was 184.54 g/L, 183.47 g/L and 184.51 g/L to 1e+7, 2e+7 and

3e+7 cfu/mL initial, respectively. As we can observed sugar consumption was a

higher than 89%.

Effect of sugar concentration on the rate of net growth in the yeast S. cerevisiae Y2034In the reactions no catalyzed the product formation by microorganisms depends

linearly on concentration of added substrate. While that catalyzed reactions by

microorganisms, usually a hyperbolic dependence of rate is obtained relative to

substrate concentration, distinguishing itself three different parts of curve. In first part,

at low substrate concentrations, rate is proportional to substrate concentration, such

that if substrate concentration is doubled, the velocity is doubled (first order reaction).

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The second part of curve at intermediate substrate concentrations, the rate increases is

less than first part with increasing concentration, starting around the half of the

maximum speed. Last part of the curve, at high substrate concentrations, the, rate is

independent of substrate concentration (zero order reaction), and so the rate achieved

is close to the maximum [17].

Figure 5, graph a showed the effect of substrate sugar concentration to SJ on net

growth rate of the yeast, which was 0.251 h-1, 0.260 h-1 with 1e+7 cfu/mL, 0.254 h-1,

0.275 h-1 with 2e+7 cfu/mL and 0.251 h-1, 0.295 h-1 with 3e+7 cfu/mL from 50 g/L to

125 g/L of sugar concentration. But from 125 to 200 g/L of sugar ubstrate

concentration, growth rate remains constant.

Figure 5, graph b showed the effect of the concentration of substratesugar

concentration to SM on net growth rate of the yeast. The bBehaviorsbehaviours of

these curves showed that from 50 g/L to 125 g/L the growth rate for were 0.250 h-1,

0.265 h-1 to 1e+7; 0.283 h-1, 0.317 h-1 to 2e+7 cfu/mL; 0.281 h-1, 0.308 h-1 to 3e+7

cfu/mL. But from 125 to 200 g/L growth rate remains constant, which means that at a

concentration greater than 125 g/L, rate was independent of the sugar concentration.

Summary of all results is shown in Table ??????

Statistical analysisThe statistical significance of the corresponding model equation was checked by F

test analysis of variance (ANOVA, Table 2 and 3).

The adequacy of the models was expressed by the coefficient of determination R2,

which proved to be 1 and 0.9998 for the production of bioethanol from SJ and SM,

respectively. These values indicate 100% of the variability of response in the

production of bioethanol from SJ and 99.98% of the variability of response in SM.

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The lower the coefficient of variation (CV) (0.12% for bioethanol production from SJ

and 0.62% to SM), the greater the accuracy and reliability of the experiments carried

out. Chance probability for models less than 0,00501 was also noted that models were

highly significant and no significant chance of lack of fit for models indicate that the

experimental data obtained are in good agreement with the model. TIf the value of

testing the lack of fit for the model was significant (P-value < 0.05) to X1, X2, X1X2,

X22 to SJ and X2 to SM.

Optimization of production of bioethanolFigure 6 and 7 showed that 3D graphics for response surface plotted the regression

equation. Using the response surface plots, the interaction between two variables and

their optimal level are easy to understand and locate. Graphs a and b show interaction

between cell concentration and sugar concentration respect bioethanol production and

yield.

Yields of sugar consumptionversion to bioethanol are shown in Figure 7, results

showed that optimum level (Table 4) was observed near the initial value of biomass

concentration and near the central value of sugar concentration. Myers and

Montgomery describe a multiple response method called desirability [18]. The

method makes use of an objective function D; it reflects the desirable ranges for each

response (di). The desirable range is from zero to one (from least to most desirable,

respectively). This work was obtained 0.952 of desirability for SJ, and 0.943 for SM.

Culture in bioreactorThe optimum condition using sugarcane juice with 1.2e+7 cfu/mL initial cell

concentration and 120.79 g/L initial sugar concentration was evaluated using a

bioreactor, experimental bioethanol production was 44.41 g/L with a conversion yield

of sugar consumption to bioethanol of 38.35% (Figure 8), in this process the total

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consumption of sugars was 115.79 g/L. It may be noted that the yeast metabolized

95%.

Conclusions It is Eestablished a fermentation processes that allowed the construction of the growth

kinetics of Saccharomyces Cerevisiae Y2034 and their effect with sugar concentration

to find the optimal conditions for bioethanol production.

The result of statistical analysis we found that the best theoretical condition for

bioethanol, was using sugarcane juice with 1.2e+7 cfu/mL initial cell concentration,

120.79 g/L initial sugar concentration, 40 g/L of bioethanol production. Under these

conditions bioethanol production was 44.41 g/L and 38.35% of conversion yield.

The oOptimization was an alternative to improve bioethanol production in sugarcane

juice.

Through the growth kinetics of the yeast Saccharomyces cerevisiae Y2034, it was

determined that the best substrate for cells production was sugarcane molasses.

MethodsSubstrateThe sugarcane juice (SJ) was extracted from a local extractor, while the sugarcane

molasses (SM) was obtained from local sugar milltalent in the state. SJ had total

soluble solids concentration of 27 ºBx and SM had 83 ºBx. Both substrates were used

as basis for the preparation of the fermentation medium, and these were supplemented

with salts: 0.02 g/L magnesium sulfatesulphate (MgSO47H2O), 0.2 g/L ammonium

phosphate ((NH4)2SO4) and 2 g/L yeast extract [19].

MicroorganismThe yeast Saccharomyces cerevisiae Y2034 was kindly donated the ceparium

Biotechnology Laboratory of Universidad Politécnica de Chiapas. The strain was

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maintained in YPD agar, (1% w/v yeast extract, 2% w/v peptone, 2% w/v glucose,

and 2% w/v agar) kept slants at 4°C. YPD liquid is used as inoculum shaken at 150

rpm, 30 °C during 24 h.

Fermentation processFermentation process was performed in triplicate with the two substrates in 250 mL

flasks with constant agitation at 180 rpm, 5 pH [20] and 30 ºC temperature [21]. The

final volume used for the fermentations was 150 mL being stirred for 24 h.

For validation experiments with optimum conditions was performed with a Bioreactor

Applikon using 2.5 L of SJ in a 3 L bioreactor (Applikon, Foster City, CA) equipped

with two six-blade Rushton turbines. The pH was monitored using an autocleavable

electrode (Applikon) and controlled at 5 ± 0.48 by a Bioconsole ADI

1035/Biocontroller 1010 (Applikon). The experiments were performed at 30 ºC and

stirred at 180 rpm.

Analytic methodsThe sugars concentration was measured with for Miller’s method [22]. The count of

viable cells was determinated with Neubauer’s Chamber adapted to an optical

microscope; the trypan blue was used as dye of viable cells [23]. To bioethanol

concentration were sampled in the aqueous phase, centrifuged at 5000 rpm for 5 min

at 5 ºC, supernatant was changed to a new tube [24] and the precipitate was discarded.

Samples were analysed by gas chromatography Agilent Technologies 6850 with data

acquisition system with software A.02.01 Agilent Cerity.

For validation experiments with optimum conditions, YSI 2700 SELECT equipment

Biochemistry analyzer was used in manual mode and a membrane (enzyme oxidase

alcohol immobilized) to ethanol 2786 with software included, the equipment was

calibrated with a standard solution of ethanol 2 g/L. Culture samples of 1 mL were

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taken every 3 h and centrifuged at 5000 rpm for 5 min at 5°C. The supernatant was

filtered through a 0.22 m filter (Millipore, Bedford, MA, USA).

Experimental designExperimental design 2k factorial with central points was used to optimizeThe

experimental design with two input variables; initial cell concentration, initial sugar

concentration, and two output variables; bioethanol concentration and yield of sugar

consumption to bioethanol yield conversion of sugar to bioethanol from SJ and SM,

each variable with three levels (Design Expert 7.0.0 software). The experimentals

designed and is shown in Table 5.

Competing interestsThe authors declare that they have no competing interest.

Authors' contributionsBYPS performed the experiments, analyzed the results and prepared of the

manuscript. ARGR carried out validation of optimum conditions. SST participated in

the experimental design and fermentation process. PEAG participated in the

microorganism culture and substrate characterization. SJP coordinated the study and

revised the manuscript. CAGF helped to structure and draft the manuscript. All

authors read and approved the final manuscript.

Acknowledgements This work was supported by Science and Technology Council 100212 to the project

PAPIIT 103410. We would like to thank M.B José Raunel Tinoco of the Instituto de

Biotecnología, and Dr. Alejandro Téllez Jurado of the Universidad Politécnica de

Pachuca, for the crucial technical support during this work.

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Figures

Figure 1 - Growth kinetic of yeast S. cerevisiae Y2034 and consumption sugar kinetic from SJ with 50 g/L (a) and 150 g/L (b) (▲ 1e+7, 2e+7 and 3e+7 cfu/mL).

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Figure 2 - Growth kinetic of yeast S. cerevisiae Y2034 and consumption sugar kinetic from SJ with 200 g/L (▲ 1e+7, 2e+7 and 3e+7 cfu/mL).

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Figure 3 - Growth kinetic of yeast S. cerevisiae Y2034 and consumption sugar kinetic from SM with 50 g/L (a) and 150 g/L (b) (▲ 1e+7, 2e+7 and 3e+7 cfu/mL).

Figure 4 - Growth kinetic of yeast S. cerevisiae Y2034 and consumption sugar kinetic from SM with 200 g/L (▲ 1e+7, 2e+7 and 3e+7 cfu/mL).

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Figure 5 - Effect sugar concentration on rate net growth in yeast S. cerevisiae Y2034, SJ (a) SM (b) (▲ 1e+7, 2e+7 and 3e+7 cfu/mL).

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Figure 6 - Response surface graphs and contour showing effect cell, sugar and bioethanol concentration; from with SJ (a), SM (b).

Figure 7 - Response surface graphs and contour showing effect cell concentration, sugar concentration and yield; from with SJ (a), SM (b).

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Figure 8 - Growth kinetic bioethanol production () and consumption sugar kinetic () from optimum conditions.

Tables

Table 1. Summary results.

Factor 1

(cfu/mL)

Factor 2

(g/L)

SJ SM

µ

(h-1)

dt

(h) Ks

µm

(h-1)

µ

(h-1)

dt

(h) Ks

µm

(h-1)

3.0e+07 50 0.2542.71

90.281

2.19

0

3.0e+07 125 0.2512.55

14.715 0.279 0.308

2.18

88.795 0.330

3.0e+07 200 0.2712.55

80.317

2.01

0

2.0e+07 50 0.2512.75

20.283

2.19

0

2.0e+07 125 0.2952.54

9

11.50

80.309 0.317

2.17

0

10.44

30.342

2.0e+07 200 0.2972.37

00.324

2.07

0

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1.0e+07 50 0.2622.64

40.290

2.56

0

1.0e+07 125 0.3022.29

5

12.06

90.326 0.335

2.65

0

12.84

80.365

1.0e+07 200 0.3032.28

10.340

2.45

0

Factor 1: cell concentration, Factor 2: sugar concentration, µ: specific growth rate, dt:

doubling time, Ks: saturation constant, µm: maximum growth rate.

Table 2. Variance analysis to for bioethanol production fromof sugarcane juice.

SourceEstimated

coefficient

Sum of

squares

Degree

freedom

Mean

squareF-value P-value

Model 1630.0631 5 326.0126 133008.4 0.0001

Intercept 41.4200

X1 0.8300 4.1168 1 4.1168 1679.601 0.0001

X2 16.2600 1585.3502 1 1585.350 646799.9 0.0001

X1X2 0.6700 1.8225 1 1.8225 743.5536 0.0001

X12 0.2100 0.1228 1 0.1228 50.1015 0.0002

X22 -3.5400 34.5942 1 34.5942 14113.94 0.0001

Residual 0.0172 7 0.0025

Lack of Fit 0.0172 3 0.0057

Pure Error 0.0000 4 0.0000

Corr. Total 1630.0 12

X1: cell concentration, X2: sugar concentration, F: Fisher test, P-value: probability

distribution value. The correlation coefficient (R2) was 1, adjusted correlation

coefficient was 0.99 and coefficient of variation was 0.12%.

Table 3. Variance analysis to for bioethanol production fromof sugarcane molasses.

SourceEstimated

coefficient

Sum of

squares

Degree

freedom

Mean

squareF-value P-value

Model 1700.8963 5 340.1793 1126.241 0.0001

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Intercept 37.0162

X1 0.0733 0.0323 1 0.0323 0.1068 0.7533

X2 16.8317 1699.8300 1 1699.830 5627.675 0.0001

X1X2 -0.3875 0.6006 1 0.6006 1.9885 0.2013

X12 -0.3817 0.4024 1 0.4024 1.3324 0.2863

X22 0.2433 0.1635 1 0.1635 0.5412 0.4859

Residual 2.1143 7 0.3020 0.0000

Lack of Fit 2.1143 3 0.7048

Pure Error 0.0000 4 0.0000

Corr. Total 1703.010 12

X1: cell concentration, X2: sugar concentration, F: Fisher test, P-value: probability

distribution value. The correlation coefficient (R2) was 0.99, adjusted correlation

coefficient was 0.98 and coefficient of variation was 0.62%.

Table 4. Optimum parameters.

SubstrateFactor 1

(cfu/mL)

Factor 2

(g/L)

Response 1

(g/L)

Response 2

(%)Desabirility

SJ 1.2e+7 120.79 40 37.97 0.95

SM 1.2e+7 140.13 40 32.12 0.94

Factor 1: cell concentration, Factor 2: sugar concentration, Response 1:

bioethanol production, Response 2: conversion yield

Table 5. Design layout

TestsFactor 1

(cfu/mL)

Factor 2

(g/L)

Response 1 (g/L) Response 2

SJ SM SJ SM

1 3.0e+07 200 55.83 52.75 30.92 30.83

2 2.0e+07 125 41.43 36.94 37.81 33.74

3 1.0e+07 50 21.73 20.04 47.57 44.87

4 3.0e+07 50 22.03 20.29 48.24 45.44

5 2.0e+07 50 21.53 20.19 47.10 45.24

6 2.0e+07 125 41.43 36.94 37.81 33.74

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7 3.0e+07 125 42.43 37.57 38.78 34.31

8 2.0e+07 125 41.43 36.94 37.81 33.74

9 2.0e+07 200 54.16 54.71 29.92 30.82

10 1.0e+07 200 52.83 54.050 29.31 30.44

11 2.0e+07 125 41.43 36.940 37.81 33.74

12 1.0e+07 125 40.76 36.08 37.19 33.06

13 2.0e+07 125 41.43 36.940 37.81 33.74

Factor 1: cell concentration, Factor 2: sugar concentration, Response 1: bioethanol

production, Response 2: yield

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