Fed-batch Saccharomyces cerevisiae fermentation of hydrolysate sugars: A dynamic model-based approach for high yield ethanol production

Biomass and Bioenergy 90 (2016) 32e41

Contents lists available at ScienceDirect

Biomass and Bioenergy journal homepage: http://www.elsevier.com/locate/biombioe

Research paper

Fed-batch Saccharomyces cerevisiae fermentation of hydrolysate sugars: A dynamic model-based approach for high yield ethanol production Anna Karapatsia a, b, Giannis Penloglou b, Christos Chatzidoukas a, *, Costas Kiparissides a, b a

Department of Chemical Engineering, Aristotle University of Thessaloniki (AUTH), P.O. Box: 472, 54124 Thessaloniki, Greece Chemical Process & Energy Resources Institute (CPERI), Centre for Research and Technology Hellas (CERTH), P.O. Box: 60361, Thermi, 57001 Thessaloniki, Greece b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 April 2015 Received in revised form 10 March 2016 Accepted 16 March 2016

The efficient fermentation of hydrolyzed sugars from lignocellulosic biomass feedstock to ethanol remains a complex multi-parametric problem. Thus, in the present study, an advanced structured dynamic model for the simulation of the fermentative ethanol production from hydrolysate sugars is developed. The model is combined with a statistical experimental design to determine an optimal operating strategy that maximizes ethanol production and serves for the systematic evaluation of critical process variables. In particular, the effects of various operating conditions and feeding strategies on the dynamic behavior of batch and fed-batch fermentation processes are explored. The deviation from the desired product or the metabolic inhibition of ethanol production are related with the applied environmental conditions and substrate and product inhibition phenomena. The operating strategy, designed with the assistance of the mathematical tools proposed in this study, includes an exponential addition policy of substrate. This strategy is experimentally proved to enhance the final product concentration, raising the ethanol productivity to 2.27 g L
1 h
1 and the ethanol yield to 53.5% of the maximum theoretical value. Moreover, the simulated strategies were in excellent agreement with the experimental results obtained from the real process using low and high glucose initial concentration, under batch and fed-batch conditions, in both flask- and bioreactor-scale cultivations, proving the model's predictive and optimization capabilities. Further improvement of process performance is expected when combining the proposed dynamic model with advanced optimization algorithms to derive the optimal bioprocess operating strategy. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Phalaris aquatica biomass feedstock Optimal operating strategies High-yield ethanol production Dynamic simulation tool Design of experiments

1. Introduction Ethanol, a high oxygen content fuel, when blended with gasoline, results in cleaner combustion, less urban smog and, thus, lower environmental pollution [1,2]. However, its wider use as a complementary transportation fuel is conditional to the availability of abundant amounts of lignocellulosic biomass [3e5]. Phalaris aquatica L. is one of the most promising lignocellulosic biomass feedstock in South Europe and one of the characteristic species of Mediterranean land. It is a perennial herbaceous energy grass with a high content of structural carbohydrates, exceeding 70% of plant dry mass, and an annual dry biomass productivity of

* Corresponding author. E-mail address: [email protected] (C. Chatzidoukas). http://dx.doi.org/10.1016/j.biombioe.2016.03.021 0961-9534/© 2016 Elsevier Ltd. All rights reserved.

6.3 t ha
1 e 11 t ha
1. P. aquatica L. is grown in marginal farmlands and has low nutrients and water demands [6]. These unique features, together with its high productivity compared to other annual species, render P. aquatica L. an excellent energy crop that can serve as a potential source of natural sugars for the fermentative ethanol production. Prior to fermentation, lignocellulosic biomass is subjected to chemical pretreatment and enzymatic saccharification in order to release its structural carbohydrates from the lignin barrier [7]. In fact, during the dilute acid pretreatment of biomass, hemicellulose is hydrolyzed into a xylose-rich solution of monomeric sugars. In the follow-up enzymatic saccharification process step, the cellulose is hydrolyzed to yield the glucose monomer units. Saccharomyces cerevisiae is a widely used yeast in the fermentative production of ethanol from hexoses (glucose in particular). Its cultivation is affected by the selected bioprocess conditions, including the nutritional composition of the growth medium, the

A. Karapatsia et al. / Biomass and Bioenergy 90 (2016) 32e41

culture aeration profile, the pH of the medium, the inoculum size and the strain type [8]. A systematic investigation of the optimal cultivation conditions via a design of experiments (DoE) approach for the efficient utilization of carbon source can lead to a substantial increase of the ethanol yield. Note that due to the large number of bioprocess variables, a prohibitively large number of experiments are required to determine the optimal culture conditions. To minimize time and effort and, at the same time, maximize the information content of the experiments, the Taguchi statistical method can be employed for DoE using the systematic orthogonal array (OA) testing. The Taguchi method utilizes two, three and mixed level fractional factorial designs and has found wide application in lab and industrial process experiments [9]. The optimal operation of an ethanol bioreactor is unambiguously not a trivial task. Ethanol production via the S. cerevisiae fermentation of hydrolysate sugars is a complex process, involving either ethanol synthesis under relatively constant biomass conditions (i.e., non-growth-associated case), or ethanol synthesis with simultaneous biomass growth (i.e., growth-associated case). The two ethanol synthesis paths do depend on a number of fermentation variables, including substrate(s) concentration(s), product(s) concentration(s), accumulation of inhibitory species, aeration and mixing profiles, temperature, pH, and ionic strength of the culture medium. These variables can highly affect the cell's metabolic mechanism and its redirection to a specific synthesis path that favors either the biomass growth or the ethanol production selectively, both the biomass growth and ethanol production simultaneously, or none of them. It is apparent that selection of the optimal culture conditions, medium composition and operating mode (i.e., batch vs. fed-batch) cannot be based on heuristic rules. Thus, the use of a comprehensive mathematical model, describing the dynamic operation of the ethanol fermentation process, can provide the means for model-based optimization and control of the bioprocess. A great number of publications dealing with the mathematical modeling of ethanol bioprocess have appeared in the open literature. The effects of temperature, cell viability and productivity on the bioprocess cellular kinetics have been extensively studied for cultures with simple or mixed microbial populations [10e14]. In these publications, different rate functions were proposed to describe the temperature dependence of cellular kinetics and fitted to different sets of experimental data. The effects of the reduced sugars source or/and composition (e.g., sweet sorghum [15] or mixtures of glucose/xylose) and co-culturing of two competing yeasts [16] have been experimentally investigated. Moreover, dynamic mathematical models have been developed to describe the fermentative production of ethanol from alternative to glucose sugars and the co-production of ethanol with other bioproducts like glycerol [12]. Mathematical models have been also proposed describing the combined saccharification-fermentation process for ethanol production from starch-based feedstock or cellulose [17,18]. In all the above studies, the specific biomass growth rate was properly modified to account for the effect of ethanol inhibition on its productivity. In a recent publication [19], a “metabolic bottleneck parameter”, accounting for the accumulation of an intracellular metabolite, was introduced to quantitatively describe the inhibitive role of ethanol. Finally, Mustafa et al. [20] developed a structured cell population balance model, based on the individual cells' state (i.e., viable, non-viable and dead cells), to describe the fermentative production of ethanol. Despite all the above model developments, little attention has been paid to model-based optimal selection of operating conditions to maximize the ethanol yield and productivity in terms of the medium composition, inoculum size, and substrate feeding policy. In the present study, the Taguchi DoE statistical method is

33

employed to investigate experimentally the production of ethanol from hydrolysate sugars in S. cerevisiae cultures. Subsequently, a structured dynamic model, accounting for both intracellular and extracellular metabolites at minimum complexity and parameterization, is proposed to describe the cellular kinetics and time evolution of the total biomass, glucose and ethanol concentrations in S. cerevisiae cultures. Experimental data from different batch and fedbatch experiments were used to estimate the unknown model parameters and verify the developed dynamic model. The validated process model was then employed to calculate the optimal fermentation conditions and fed-batch policies that satisfy the nutritional needs of S. cerevisiae cultures while maintaining the concentration of inhibitory compounds at specified levels. 2. Materials and methods 2.1. Saccharomyces cerevisiae strains e inoculum and substrate preparation Two S. cerevisiae strains, the Sigma Type II purchased from Sigma Aldrich and the DSMZ 70449 obtained from the German Collection of Microorganisms and Cell Cultures, were stocked at 4  C in agar plates containing yeast extract-peptone (YP) standard medium [21]. The seed preculture was prepared by transferring a loopful from the stock plate to the preculture medium (containing in g L
1: glucose 30; yeast extract 5; (NH4)2SO4 10; KH2PO4 4.5; MgSO4$7H2O 1; ZnSO4$7H2O 0.65) and incubate in an orbital shaker incubator (GFL 3033) at 2.5 Hz, 30  C, for 8 h [21]. The pH was initially adjusted to 6.0 ± 0.2. Upon sufficient growth at midexponential phase (optical density, OD600nm z 1.5), the preculture was used to inoculate the main culture. Glucose solutions (utilized directly and/or concentrated as culture feedstock) were derived from biomass hydrolysate via a twostage bioconversion of P. aquatica biomass. The selected protocols for biomass pretreatment and enzymatic hydrolysis (followed by detoxification of the derived sugars solutions) are described in detail in a recent work [21]. The experimentally measured yields in terms of pentose and hexose sugars are also reported and compared with the results obtained by other investigators in the same study. 2.2. Flask-scale screening experiments e statistical design To identify the most favorable operating conditions regarding the fermentative production of ethanol from hydrolysate sugars, a series of batch screening experiments were initially conducted in Erlenmeyer flasks. All the experiments were carried out isothermally at 30  C. The culture medium, slightly modified from our previous work [21], consisted of hydrolysate sugars obtained from the pretreatment and subsequent enzymatic hydrolysis of lignocellulosic biomass. Precisely, the medium composition in g L
1 was: glucose 20; KH2PO4 3; Na2HPO4 1; MgSO4$7H2O 1; CaCl2$2H2O 0.1; trace elements solution (containing in g L
1: ZnSO4$7H2O 0.9; FeSO4$7H2O 0.6; H3BO3 2; MnCl2$4H2O 1.5; Na2MoO4$2H2O 0.8; CoCl2$6H2O 0.8; CuSO4$5H2O 0.5) 2 cm3 L
1. A mixture of yeast extract and (NH4)2SO4 was used as nitrogen source according to the selected levels of the experimental design (see Section 4.1). All nutrients were steam-sterilized (at 121  C for 15 min) in separate solutions. Note that the biomass hydrolysate was filter-sterilized using 0.45 mm filter units (Nalgene, Thermo Scientific). The Taguchi statistical method was used to design the series of batch flask-scale experiments for the investigation of the effect of the following five non-dynamic bioprocess variables on the process performance (expressed as ethanol yield and productivity): (i) the initial concentration of elemental nitrogen, (ii) the pH of the

34

A. Karapatsia et al. / Biomass and Bioenergy 90 (2016) 32e41

medium, (iii) the aeration profile (expressed in terms of an aeration coefficient), (iv) the strain type and (v) the inoculum quantity. The inoculum loading was quantified based on the initial culture's OD600nm. The effect of the aeration profile on the ethanol productivity was experimentally analyzed in terms of the geometric flask volume (Vf), the volume of the medium loaded into the flask (Vw), affecting the free surface area of the culture for oxygen mass transfer, and the incubator's shaking rate (A). Thus, in the batch experiments the flask size, the culture volume or/and the shaking frequency were varied accordingly. The ethanol yield was calculated from the mass ratio of the ethanol produced over the consumed glucose and expressed as percentage of the respective theoretical maximum yield value of 510 g kg
1 of glucose converted [22]. Note that based on the statistical analysis of the Taguchi experiments (see Section 4.1), the optimal values for the five process variables were determined via the maximization of the signal to noise ratio (S/N)j, given by the logarithm of the mean squared deviation (MSD)j of the glucose to ethanol yield values, Yj,i [23]:

ðS=NÞj ¼
10 logðMSDÞj ; ðMSDÞj ¼

n X i¼1

. 2 Yj;i

1

!, n

(1)

(S/N)j denotes the value of the signal to noise ratio for the “j” experimental trial (j: 1, 2, …, 16) and n is the number of repetitions for each experimental trial. All trial experiments (16) were carried out in duplicates and the mean values of all measured process variables are reported in the figures and tables of this study. The statistical analysis of these data, the design of experiments and the formulation and solution of the optimisation problem were conducted with the Minitab® 17 statistical analysis software. 2.3. Batch and fed-batch experiments A series of batch and fed-batch experiments were conducted in a 3 L stirred-tank glass bioreactor (BioFlo 110 Bioreactor/Fermenter, New Brunswick Scientific). The bioreactor was initially loaded with 800 cm3 (unless stated differently) of the culture medium. The initial glucose concentration was adjusted to two levels, either 20 or 100 g L
1 (unless stated differently) representing a low and a high glucose concentration, respectively. The pH of the medium was regulated at the specified set point value (determined by the statistical data analysis) with the addition of NaOH and HCl solutions, 2 mol L
1. The dissolved oxygen (DO) concentration in the culture medium was controlled by manipulating the agitation rate (in the range of 2.5 Hze17 Hz). The aeration rate of the fermentation broth was set at 1 min
1. The cultivation temperature was controlled at 30  C. The medium used in the respective fed-batch experiments as feeding stream was 7.5 times condensed compared to the composition of the culture medium initially loaded to the reactor. The implemented addition policy and the respective glucose concentration in the feeding stream for the studied fed-batch experiments are reported in Section 4. 2.4. Analytical measurements The optical density of 5
cm3 culture samples was measured as absorbance at a wavelength of 600 nm with the aid of a UVeVis spectrophotometer (Hitachi U-1800) as an immediate measure of culture's growth. The samples were centrifuged at 10,000  g for 5 min, lyophilized for 16 h and weighted for determination of the dry cell weight (DCW). For the measurement of glucose and ethanol concentrations, the supernatant phase of the collected culture 5
cm3 samples was analyzed with a High Performance Liquid

Chromatograph (Agilent Technologies Inc. 1200 Series HPLC), equipped with a Refractive Index (RI) detector. A Hi-Plex Hþ analysis column (300  7.7 mm) was used for glucose and ethanol separation. The HPLC was operated at 60  C with a mobile-phase consisting of an aqueous solution of H2SO4, 0.005 mol L
1, at a flow-rate of 1.3 mL min
1. Glucose, ethanol, glycerol, acetic and formic acid standard solutions of known concentrations were employed for HPLC calibration. All analytical determinations were performed in duplicate and the results reported are average values. In all cases, the relative standard deviation of all the measured concentrations was less than 5%. 3. Development of the structured dynamic model In the present model derivation it is assumed that the total cellular biomass (X) is structured into three distinct compartments [15,19]. In particular, glucose and ethanol are two intracellular metabolites representing two of the three biomass compartments. The third compartment accounts for the residual biomass (XR). The intracellular glucose, expressed as glucose cellular quota (qG), is the carbon substrate pool used by the cells for their metabolic activities and is continuously refilled through a glucose assimilation mechanism, as long as glucose (G) is present in the medium. Similarly, the intracellular ethanol, expressed as ethanol cellular quota (qE), is the product pool that is accumulated in the medium via the cells' excretion mechanism. For simplicity, the large number of simultaneous metabolic reactions can be lumped into three main cellular activities, namely, the residual biomass growth, the maintenance of the residual biomass and ethanol production. In particular, glucose as the sole carbon source is consumed from the intracellular pool even under zero extracellular glucose concentration conditions (i.e., complete depletion of glucose in the culture or when the glucose feeding rate is equal to the glucose assimilation rate). However, under extensive carbon stressed conditions, the produced ethanol is used for cells' maintenance via an ethanol assimilation mechanism that is conditionally activated. This can result in a progressive decrease of ethanol concentration in the medium as it has been observed by several investigators [16,24,25]. Note that glucose and ethanol, when at critical concentrations in the growth medium, can cause significant inhibition of biomass growth and ethanol production. The proposed dynamic model takes into account both inhibitory mechanisms, probably caused by the substantial osmosis variation over the cellular membrane (i.e., due to the increased glucose and ethanol levels) combined with the limited osmo-tolerance of yeast strains [13]. Thus, the model can simulate either the sluggish culture growth observed at high initial glucose concentrations or/and the progressive termination of biomass growth and ethanol synthesis, exhibited under high ethanol accumulation conditions. The dissolved oxygen (DO) concentration could also affect the cells' behavior via its participation in several biochemical reactions of the metabolic mechanism. However, in the present study, the dissolved oxygen had only a minor effect on ethanol production for its concentration was never below a limited value (i.e., the DO concentration in the culture medium remained constant). To further simplify the derivation of the dynamic model equations describing the ethanol fermentation process, the following additional assumptions were made: (i) The cultivation medium was considered to be chemically defined. All nutrients but the carbon source were in excess, i.e., glucose was the limiting substrate. (ii) The effect of cells' heterogeneity as well as mass-transfer limitations of substrates on ethanol production were assumed to be negligible.

A. Karapatsia et al. / Biomass and Bioenergy 90 (2016) 32e41

Thus, based on the above assumptions one can derive the following system of differential algebraic equations (DAEs) to describe the dynamic evolution of S. cerevisiae cultures in batch and fed-batch bioreactors. Time variation of residual biomass concentration, [XR]:

dðV½XR Þ ¼ V ms ½XR 
Qout ½XR  dt

(2)

Time variation of glucose concentration, [G]:

dðV½GÞ ¼
V rGu ½XR  þ Qin ½Gin
Qout ½G dt

(3)

Time variation of ethanol concentration, [E]:

dðV½EÞ ¼ VrEs ½XR 
Qout ½E dt

(4)

Time variation of glucose intracellular quota, qG:

dðVqG ½XR Þ ¼ VrGu ½XR 
VrGc ½XR 
Qout qG ½XR  dt

(5)

Time variation of ethanol intracellular quota, qE:

dðVqE ½XR Þ ¼ VrEp ½XR 
VrEs ½XR 
VrEc ½XR 
Qout qE ½XR  dt

(7)

(8)

The symbols Qin and Qout denote the volumetric flow rates of the feeding/addition and sampling/product removal streams, respectively. ½Gin is the glucose concentration in the feeding stream. The symbols ms , rGu rGc rEp rEs , and rEc appearing in Eqs. (2)e(6) represent the net specific consumption/production rates of various species and the respective semi-empirical mathematic expressions are analytically reported in Table 1. To account for the inhibitory effect of “Y” species (i.e., glucose or ethanol) on the relevant net specific growth/consumption/secretion rates, a first-order dynamic inhibition model was introduced.   
1 Thus, the term 1 þ exp
Yc a
Y ; appearing in Eqs. (ii), (iii), (v) and (vi) in Table 1, can vary from 0 to 1 (i.e., 0 for maximum inhibition and 1 for no-inhibition). “Yc” denotes the critical (i.e., maximum or minimum value) concentration of the respective species either in the bulk or in the cells and “a” is the characteristic parameter of a first order response. Precisely, when ½
ðYc
YÞ <
4a practically there is no inhibition effect and when ½
ðYc
YÞ > 4a the maximum inhibitive action is developed. In 
1   R =½E in Eq. (v) of Table 1 particular, the term 1 þ exp
XEc
½X a accounts for culture conditions under which the biomass growth dominates and the ethanol synthesis is inhibited (e.g., for values of [XR]/[E] ratio larger than the critical value XEc). Similarly, the term 
1   qE
qE is introduced to describe the first order 1 þ exp Ca decay of the ethanol secretion from the cells when the cellular ethanol quota of “qE ” approaches its critical value “qEc ”. Moreover, the Heaviside function,

1; when Y > Yc 0; when Y  Yc

 (9)

appearing in Eqs. (i) and (iveviii) in Table 1 was introduced to describe the switching between two culture events (i.e., a metabolic activity is terminated or not). Thus, when the glucose or ethanol cellular quota “Y”, is reduced below or increased above a critical (i.e., minimum or maximum value) the relevant metabolic activity is terminated. In particular, when the element “Y” is the [G]/ [E] ratio, then the Heaviside function is used to represent the enhancement of biomass growth, experimentally observed when the glucose to ethanol ratio exceeds a critical value (i.e., GEc). Finally, the Monod kinetic model was employed to express the dependence of the specific growth, glucose uptake and ethanol secretion rates from the intracellular glucose-ethanol quotas or the glucose-ethanol bulk concentrations. In all rate functions in Table 1, the symbol KY (i.e., KG, KGu, KEu) denotes the well-known saturation constant of the Monod kinetic expression. Note that the two kinetic rate constants, KE1 and KE2, in Eq. (v) denote the discrete growthrelated and non-growth-related ethanol production, respectively. Similarly, the three terms in the glucose specific consumption rate, 1=YXG ; KGm and 1=YEG in Eq. (vii), represent the glucose consumption rates for biomass growth, biomass maintenance and ethanol synthesis, respectively. 4. Results and discussion

Time variation of total biomass, [X]:

½X ¼ ½XR  þ qG ½XR  þ qE ½XR 

 HðY
Yc Þ ¼

(6)

Time variation of culture volume, V:

dðVÞ ¼ Qin
Qout dt



35

4.1. Statistical analysis of flask screening experiments To analyse the effects of the five bioprocess variables (namely, (i) the initial concentration of elemental nitrogen, (ii) the aeration coefficient, (iii) the inoculum quantity, (iv) the pH of the medium, (v) the strain type) on the ethanol yield and productivity, a fractional Taguchi series of experiments were designed and run in flask cultures. The selected four levels of the first two process variables (factors) were (0.28, 0.368, 0.56, 1.12 g L
1) and (0.3, 0.375, 0.5, 0.6), respectively. For the remaining three variables (factors) two levels of variation were chosen, that is, (0.5 and 4.0) for inoculum quantity as OD600nm, (5.5 and 6.5) for pH and for the strain type (Sigma Type II and DSM 70449). Thus, a total number of 16 out of 128 potential trials were selected as a set of minimum but well-balanced number of experiments for maximizing the information content for the identification of the “optimal” operating conditions [9]. Table 2 summarizes the values of the five process variables for the 16 individual experiments and the respective measured values of glucose to ethanol yield. In Table 3 typical results from the Analysis of Variance (ANOVA) of the experimental measurements of ethanol yield are presented. According to this data analysis the initial nitrogen concentration is classified first in the ranking of the five factors with respect to their contribution to the overall process performance. The importance of this factor is further proved by its high variance ratio (F-value). Precisely, it is demonstrated that this factor is statistically important and contributes to the variance of ethanol yield by 79.76% within a confidence level equal to {1
(P
value) ¼ 99.9%}. This conclusion is further corroborated by a previous study [26], where the role of nitrogen concentration, as a limiting factor in yeasts' growth rate and activity, was demonstrated. The second most important factor emerged from the ANOVA is the culture pH, however the contribution of this factor to the variance of ethanol yield is an order of magnitude lower compared to the initial nitrogen concentration (i.e., 8.3% vs. 79.76%). Still though this factor is statistically important and contributes to the variance of ethanol yield within a confidence level exceeding 95%.

36

A. Karapatsia et al. / Biomass and Bioenergy 90 (2016) 32e41

Table 1 Net specific production/consumption species rates. Specific residual biomass growth rate:

ms ¼

8 > < ms;max > :

qG =qE InhG InhE f1 þ H ðG=E
GEc Þg ; if qG > qGmin KG þ qG =qE

(i)

0 ; if qG  qGmin

where the inhibition terms are: InhG ¼

  
1 Gmax
½G 1 þ exp
a1 

InhE ¼

(ii)

 
1 Emax
½E 1 þ exp
a1

(iii)

Glucose specific uptake rate: rGu ¼ rGumax

  ½G H qGmax
qG KGu þ ½G

(iv)

Intracellular ethanol specific production rate: 
1      XEc
½XR =½E rEp ¼ KE1 m2s þ KE2 H qG
qGmin 1 þ exp
a2

(v)

Ethanol specific secretion rate:   
1  

qEc
qE ½E 1
H qG
qGmin rEs ¼ rEsmax 1 þ exp
rEumax KEu þ ½E a3

(vi)

where, rEsmax ¼ KE1 m2s;max þ KE2 Glucose specific consumption rate:  rGc ¼

  1 1 m þ KGm f1 þ H ðG=E
GEc Þg þ r H qG
qGmin YXG s YEG Ep

(vii)

Ethanol specific consumption rate:   

 rEc ¼ rEumax H qE
qEmin 1
H qG
qGmin

(viii)

Table 2 L16 Taguchi design of experiments for batch cultivation of S. cerevisiae in flask-scale: levels of variation of the process variables and process response. No

Nitrogen (g L
1 as N)

pH

koi

Strain type

Inoculum quantity (OD600nm)

Ethanol concentration (g L
1)

Yield (%)

S/N ratio

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1.12 1.12 1.12 1.12 0.56 0.56 0.56 0.56 0.368 0.368 0.368 0.368 0.28 0.28 0.28 0.28

6.5 6.5 5.5 5.5 5.5 5.5 6.5 6.5 5.5 5.5 6.5 6.5 6.5 6.5 5.5 5.5

0.3 0.375 0.4 0.5 0.3 0.375 0.4 0.5 0.3 0.375 0.4 0.5 0.3 0.375 0.4 0.5

DSM DSM Type Type Type Type DSM DSM DSM DSM Type Type Type Type DSM DSM

0.5 0.5 4 4 0.5 0.5 4 4 4 4 0.5 0.5 4 4 0.5 0.5

8.1 7.65 8.2 8.75 10.51 9.7 8.2 8.75 8.67 8.9 10.05 9.84 7.28 6.34 6.7 6.9

79.45 75 83.64 85.8 98.1 95.1 80.4 88.94 85.3 89 88.04 83.9 71.4 62.16 68.34 70.4

38.00 37.50 38.45 38.67 39.83 39.56 38.10 38.98 38.62 38.98 38.89 38.47 37.07 35.87 36.69 36.95

i

II II II II

II II II II

The aeration coefficient is defined as ko ¼ [(Vw/Vf)/(Vw/Vf)max]·(A/Amax); Vw ¼ {200, 250, 400} cm3; Vf ¼ {1, 2} L; A ¼ {2.5, 4.17} Hz; thus (Vw/Vf)max ¼ 0.4 and Amax ¼ 4.17 Hz.

Generally, yeast cells can adjust their metabolism efficiently in mild acidic environment as it is further verified by earlier work [27], where it is shown that an increase in pH from 4 to 5.75 is followed by ethanol concentration increase, but further pH increase to 7 reduces the ethanol production rate. The rest of the studied factors exhibited much lower contributions, even lower than the residual error of the statistical analysis, and according to their P-value they

are not considered statistically important within a satisfactory confidence level. In Fig. 1 the effect plots of the five bioprocess variables on the mean ethanol yield are shown with respect to the selected levels of each variable. As can be seen the optimal values of the five variables that maximize the ethanol yield are: 0.56 g L
1 for the initial concentration of elemental nitrogen, 0.3 for the aeration

A. Karapatsia et al. / Biomass and Bioenergy 90 (2016) 32e41

37

Table 3 Analysis of Variance (ANOVA) for the mean values of ethanol production yield (%) for the flask-scale batch fermentations. Parameter

Degrees of Freedom

Sum of squares

Contribution (%)

F-factor

P-factor

Rank

Nitrogen (g L
1 as N) pH ko Strain Inoculum (OD600nm) Error Total

3 1 3 1 1 6 15

14.2105 1.4784 0.4355 0.5573 0.0838 1.0518 17.8173

79.76 8.30 2.44 3.13 0.47 5.90 100.00

27.02 8.43 0.83 3.18 0.48

0.001 0.027 0.525 0.125 0.515

1 2 4 3 5

100 Level Nitrogen (g L ) pH k

95

0.28 0.368 0.56 1.12

Ethanol Yield (%)

90

Strain

Inoculum (O.D)

0.3 5.5 0.375 DSM 0.5 6.5 0.4 Type II 4 0.5

85 80 75 70 65 60 Nitrogen Concentration

pH

Aeration Coefficient

Strain Type

Inoculum Quantity

Fig. 1. Main Effects Plots for the ethanol yield (%) derived from the experimental data statistical analysis from batch flask-scale cultures for the five studied factors: (a) initial elemental nitrogen concentration, (b) pH, (c) aeration coefficient, (d) strain type and (e) inoculum quantity.

coefficient, ko, 0.5 for the inoculum quantity, OD600nm, 5.5 for pH, and the Sigma Type II strain. Note that the identified optimal value for the initial elemental nitrogen concentration corresponds to 3 g L
1 yeast extract and 5 g L
1 (NH4)2SO4. Similarly, the respective “optimal” value of the aeration coefficient represents the effects of two synergistic parameters, namely, the agitation speed value of 2.5 Hz, and the value of culture volume to the total flask volume ratio of 1:5. Τhe identified optimal pH value corresponds to mild acidic conditions. Finally, the selection of the Type II strain from Sigma was primarily due to its robustness against the hydrolysate inhibitory impurities and its smaller lag phase and adjustment time than those of DSMZ strain. The proposed values for the nitrogen loading, the pH and the strain type were explicitly adopted for the batch and fed-batch cultures carried out in the bioreactor scale. The inoculum size was adjusted proportionally for the bioreactor-scale experiments to result in an initial OD600nm equal to 0.5. The conclusion for the culture aeration, which in any case is a factor of very low significance, was taken into consideration for the bioreactor-scale experiments only in the sense that a low culture aeration profile is sufficient. Thus, a constant air feeding rate of 1 min
1 for all cultures was selected and an agitation rate cascaded to the DO control loop with a set point value (DOsp) equal to 20% of the saturation value was applied. 4.2. Estimation of unknown model parameters e model tuning The developed dynamic model (Eqs. (2)e(9) and Eqs. (i)e(viii) of Table 1), describing the fermentative production of ethanol, contains a number of unknown kinetic and bioprocess parameters. The

unknown model parameters were estimated from a series of batch and fed-batch cultivation experiments and dynamic measurements of the total DCW, ethanol and glucose concentrations. Batch and fed-batch operating policies were carefully selected to properly map the culture's dynamic behavior under different operating conditions (e.g., large initial glucose concentrations, sufficient or limited glucose concentration during fermentation and upon certain culture growth, etc.). The numerical values of qGmin and qEmin were set equal to 0.002, representing the cutoff values of glucose and ethanol content in the cells. That is, below these values the substrate cannot be consumed and the product cannot be secreted. The characteristic parameters “ai” appearing in Eqs. (ii), (iii), (v) and (vi) in Table 1 were directly estimated from experimental observations and were assigned the following values (i.e., a1 ¼ 7, a2 ¼ 5.10
2 and a3 ¼ 5.10
4). The gPROMS® (PSE Ltd.) parameter estimator, based on the Maximum Likelihood formulation, was employed to estimate the unknown model parameters (i.e., consumption/production rate constants, KG, KGu, KGm, KE1, KE2, KEu, rEumax , and substrate and product inhibition parameters, GEc, XEc, Gmax, Emax, qGmax ; qEc ). Thus, a set of four experiments, two in flask-scale and two in bioreactorscale under batch and fed-batch operating mode, respectively, were selected to provide the dynamic data for “model training”. It should be pointed out that in those four cultures the initial medium composition was identical. Precisely, the initial nitrogen concentration was adjusted according to the outcome of the statistical analysis, while the initial glucose concentration was set at 20 g L
1 for all the cultures apart from the second flask-scale experiment. In that case, the initial glucose concentration was 100 g L
1, in order to quantify the substrate inhibition effect. Moreover, the time duration was different for the four cultures in order to assess the culture behavior under different conditions of substrate limitation. For the bioreactor-scale fed-batch experiments two strategies were chosen. In the first bioreactor the culture was supplemented at a constant feeding rate (constant addition policy). Glucose was concentrated in the feeding medium at 337.5 g L
1. In the second bioreactor the culture was supplemented again with the concentrated nutrients medium with glucose concentration equal to 350 g L
1. However, the feeding profile was empirically adjusted so that the glucose provision rate would be close to the consumption rate (conservative addition policy). In Figs. 2 and 3 the time evolution of the three bioprocess variables, glucose and ethanol concentrations and dry cells concentration, for the flask-scale cultures is depicted. Note that in the first culture (low-initial glucose concentration) there is no lag-phase distinguished in the growth profile. Glucose is practically consumed within the first 6 h, however ethanol is further produced, at a reduced though production rate, up to the first 10 h, indicating that the intracellular glucose quota has not reached its minimum value yet. Then the culture enters a stationary phase. A comparison of the process performance in the first flask with that in the second one (high-initial glucose concentration) clearly demonstrated the inhibitory effect of the excessive initial glucose

A. Karapatsia et al. / Biomass and Bioenergy 90 (2016) 32e41

7 Exp. Results-Model Predict.: Glucose Ethanol Biomass (DCW)

12.5 10.0

6 5 4

7.5

3 5.0

2

2.5

1 0

0.0 0

1

2

3

4

5

6 7 Time (h)

8

9

10

11

12

90

45

80

40

70

35

60

30 Exp. Results-Model Predict.: Glucose Ethanol Biomass (DCW)

50 40

25 20

30

15

20

10

10

5

0

0 0

2

4

6

8

10 12 Time (h)

14

16

18

20

Fig. 3. Time profile of glucose, ethanol and biomass concentrations in the flask-scale batch experiment under high initial glucose loading.

loading in the medium. The culture growth proceeds significantly slower and the ethanol production is also affected. Furthermore, a comparison of glucose to ethanol yield between the first (90.9%) and the second flask (90.5%) shown that when substrate is provided in excess then the ethanol production mechanism of the cells is retarded and when the cells recover the osmotic stress they keep on metabolizing glucose to ethanol equally efficiently. The respective time profiles of the same bioprocess variables for the bioreactor-scale experiments are shown in Figs. 4(a) and 5(a). The Figs. 4(b) and 5(b) present the time evolution of culture volume and glucose feeding rate for the constant and the conservative addition policy, respectively. In the first bioreactor the constant feeding strategy started 2 h after the beginning of the culture before any significant nutrients and substrate consumption. This strategy induced a state of nutrients and substrate sufficiency (not excess) in the medium for a long period of time. Under such conditions biomass production was favored, while the ethanol production was of lower priority. This culture behavior was also remarked before [28], stating that there is a metabolic path where glucose feeding is kept below a critical value and cell biomass is increasing at maximum level, while ethanol production is restrained. Note that after the first 9 h, glucose concentration was practically zero, which means that from that point onwards glucose was consumed at a rate equal to the feeding rate. Within 24 h over 240 g of glucose were consumed, however ethanol concentration did not exceed

-1

25 20 20

16

15

12

Exp. Results-Model Predict.: Glucose Ethanol Biomass (DCW)

8

10 5

0 1.5

0 12 11

1.4 Culture Volume (L)

50 Ethanol and Biomass Concentrations (g L )

Glucose Concentration (g L )

Fig. 2. Time profile of glucose, ethanol and biomass concentrations in the flask-scale batch experiment under low initial glucose loading.

100

30

24

4 a

Ethanol and Biomass Concentrations (g L )

15.0

35

28

10 9

1.3

8

1.2

7 6

1.1

5 1.0

4 3

0.9

2

Volume-Experimental Volume-Model Prediction Glucose Feeding Rate

0.8

-1

8

40

32

Glucose Feeding Rate (g h )

17.5

36

-1

Glucose Concentration (g L )

9

Glucose Concentration (g L )

10

20.0

Ethanol and Biomass Concentrations (g L )

38

1

b 0.7

0 0

2

4

6

8

10 12 14 Time (h)

16

18

20

22

24

Fig. 4. Time profile of (a) glucose, ethanol and biomass concentrations and (b) culture volume and glucose feeding rate in the bioreactor-scale fed-batch experiment under the constant addition policy.

23.54 g L
1 reflecting a yield of only 19.2% and ethanol productivity 0.98 g L
1 h
1. Upon such a low process performance the conservative addition policy designed for the second bioreactor aiming at comparing the process behavior upon exposure to substrate and nutrients limitation at some degree. In that case the feeding started after approximately ten and a half hours from the beginning of the culture and the addition rate was progressively adjusted according to the culture needs, as shown in Fig. 5(b). Under this strategy the culture performance improved, but definitely didn't reach its maximum level. The final ethanol concentration increased to 29 g L
1 with substantially lower glucose consumption (117 g), raising the glucose to ethanol yield to 48.6% and the ethanol productivity to 1.45 g L
1 h
1. From the results presented in Figs. 2e5 the model parameters were estimated. The dynamic simulation of the above experiments with the developed model with respect to the selected bioprocess variables is displayed in the same figures. The model predictions are in satisfactory agreement with the respective experimental data, which clearly demonstrates adequacy of the estimated model parameters. In Table 4, the numerical values of all the model parameters, including the confidence intervals of the estimated ones, are reported. The numerical values of YXG and YEG parameters were directly taken from the literature. 4.3. Strategy for improved process performance - model validation From the experiments conducted for the model tuning it was concluded that the culture behavior is adjusted depending on the

A. Karapatsia et al. / Biomass and Bioenergy 90 (2016) 32e41 22 20

35

16

30

14

25

12 20

10

15

8 6

10

4 5

2 a

0

0 14

1.4

Volume-Experimental Volume-Model Prediction Glucose Feeding Rate

Culture Volume (L)

1.3

12 10

1.2 8 1.1 6 1.0

4

0.9

Glucose Feeding Rate (g h )

Glucose Concentration (g L )

18

Ethanol and Biomass Concentrations (g L )

40 Exp. Results-Model Predict.: Glucose Ethanol Biomass (DCW)

2 0

b 0.8

0

2

4

6

8 10 Time (h)

12

14

16

18

20

Fig. 5. Time profile of (a) glucose, ethanol and biomass concentrations and (b) culture volume and glucose feeding rate in the bioreactor-scale fed-batch experiment under the conservative addition policy.

substrate availability at the onset of the cultivation (in excess, sufficiency, limitation) and the feeding strategy should be adapted according to the initial substrate availability. In order to further prove this allegation, two additional fed-batch experiments in the bioreactor-scale were designed. All culture conditions employed were identical to the respective bioreactor-scale experiments presented in the previous section. However, in those two bioreactors (Bioreactor I, Bioreactor II) an exponential-like addition policy was applied and the Bioreactor I was initially loaded with 20 g L
1

39

glucose, while the Bioreactor II was initially loaded with 110 g L
1 glucose. In both cases the glucose concentration in the feeding stream was 300 g L
1. Due to the different substrate initial loading of the bioreactors, the addition policy in Bioreactor I started 3 h after the beginning of the culture, while in Bioreactor II 14 h after the beginning of the culture. In Fig. 6 the dynamic evolution of the three bioprocess variables (i.e., glucose, ethanol and biomass concentration) including the intracellular glucose and ethanol quotas is presented together with the exponential-like feeding policy applied in the Bioreactor I. Under this addition strategy the culture was supplemented with nutrients and substrate so that any glucose limitation was avoided throughout the culture growth. On the contrary, 12 h after the beginning of the culture, the glucose feeding rate exceeded the respective consumption rate and glucose was accumulated in the medium. Note that this excess of substrate, upon sufficient culture growth, radically stimulated the ethanol production. Moreover, it should be pointed out that according to the model predictions the intracellular glucose and ethanol quotas (Fig. 6(b)) evolved “independently” of the respective glucose and ethanol bulk concentrations and they were sensitive only to intensive variations occurring in the medium composition. The benefit gained from this strategy was to increase the final ethanol concentration to 36.35 g L
1 and although the glucose to ethanol yield was not seriously improved (53.5%), the ethanol productivity raised to 2.27 g L
1 h
1. In Fig. 7 the time profile of the same model variables together with the dynamic increase of the culture volume under the chosen exponential-like feeding policy applied in the Bioreactor II are presented. Due to the high initial glucose concentration and the provoked substrate inhibition effect a retardation of 6 h is observed in both the culture growth and the ethanol production (Fig. 7(a)). From that point onwards the culture progressively proceeded to the exponential growth phase. Note that despite the large amount of glucose consumed and ethanol produced in the medium throughout that phase, the respective intracellular quotas exhibited a sharp change only when the culture was growing at its maximum specific growth rate, ms, (i.e., at time instant t z 10e12 h). Then the quotas revert back to their previous value (Fig. 7(b)). Finally it should be pointed out that 19 h after the beginning of the culture, the ethanol production, the glucose consumption and the biomass growth rates slowed down even though there was sufficient substrate available in the medium. The reason behind this phenomenon was the product inhibition effect, which emerged upon a considerable ethanol accumulation in the medium with a consequent osmotic stress on the cells [28,29]. That operating strategy

Table 4 Estimated and selected model parameters. Parameter

Value

Confidence interval

Estimation/determination strategy

KG KGu (g L
1) KEu (g L
1) KGm (g g
1 h
1) KE1 (g h g
1) KE2 (g g
1 h
1) rEumax (g g
1 h
1) GEc XEc Gmax (g L
1) Emax (g L
1) qGmax (g g
1) qEc (g g
1) YXG (g g
1) YEG (g g
1) ms;max (h
1) rGumax (g g
1 h
1)

5 9 20.8 0.151 19.73 1.30 0.27 21.1 0.086 98.7 29.1 0.086 0.0055 0.6 0.45 0.50 28

±2.1 ±2.9 ±9.6 ±0.075 ±1.9 ±0.077 ±0.068 ±9.5 ±0.013 ±0.4 ±1.8 ±0.032 ±0.0018

Globally estimated model parameters

[13] The maximum value of ms and rGu observed during all the experiments in this work

A. Karapatsia et al. / Biomass and Bioenergy 90 (2016) 32e41 36

30

24

25

20 20 16 15

12

10

8 4

5

0 0.090

0

60

100 Glucose Concentration (g L )

Glucose Concentration (g L )

28

35

Ethanol and Biomass Concentrations (g L )

Exp. Results-Model Predict.: Glucose Ethanol Biomass (DCW)

32

a

110

40

a

55

90

50

80

45 40

70

Exp. Results-Model Predict.: Glucose Ethanol Biomass (DCW)

60 50

35 30 25

40

20

30

15

20

10

10

5

0

0

0.095

0.0060

Ethanol and Biomass Concentrations (g L )

40

0.0050

0.085

0.070

0.0040

b

Volume-Experimental Volume-Model Prediction Glucose Feeding Rate

Culture Volume (L)

40 35 30

1.1

25 1.0

20 15

0.9

10 0.8 c

0

0.7 2

4

6

0.0030

8 Time (h)

10

12

14

Glucose Quota Ethanol Quota

16

Fig. 6. Time profile of (a) glucose, ethanol and biomass concentrations (b) glucose and ethanol intracellular quotas and (c) culture volume and glucose feeding rate in the Bioreactor I fed-batch experiment under low initial glucose loading.

shown a final ethanol concentration equal to 59.1 g L
1, however with a large amount of glucose consumed for maintenance of the cells and/or other possible products (such as glycerol), suppressing the glucose to ethanol yield to 48.6%, retaining the ethanol productivity to a satisfactory level, 2.19 g L
1 h
1. These two fed-batch strategies were also tested with the mathematical model that was tuned with the previous set of four experiments. The excellent agreement of the model predictions with the experimentally measured process variables clearly demonstrates the model capability to simulate and predict the metabolic phenomena and the culture growth rate under various environmental conditions.

5. Conclusions The present work investigated the growth profile of the S. cerevisiae culture and the potential of this culture for fermentative

0.0025 18

Volume-Experimental Volume-Model Prediction Glucose Feeding Rate

1.3

15

1.2

12

1.1 9 1.0 6 0.9 3 0.8

5

0

0.080

0.075 b 1.4

0.0030

1.3

1.2

0.0035

0.0035

Glucose Quota Ethanol Quota

Culture Volume (L)

0.065

0.0040 0.085

Ethanol Quota (g g )

0.0045

0.0045

0.090

Glucose Feeding Rate (g h )

0.075

Glucose Quota (g g )

0.0050

Ethanol Quota (g g )

0.080

Glucose Feeding Rate (g h )

Glucose Quota (g g )

0.0055

c

0

0.7 0

3

6

9

12 15 Time (h)

18

21

24

27

Fig. 7. Time profile of (a) glucose, ethanol and biomass concentrations (b) glucose and ethanol intracellular quotas and (c) culture volume and glucose feeding rate in the Bioreactor II fed-batch experiment under high initial glucose loading.

ethanol production on sugars derived from the hydrolysis of lignocellulosic biomass. High performance fermentation of the sugars from lignocellulosic sources is very hard to obtain without the use of experimental design principles, and detailed simulation algorithms based on fundamental processes. The Taguchi statistical method was employed as a rapid and efficient way of making optimal decisions on a number of important process operating variables including the medium composition in nitrogen, the pH, the aeration profile, the strain type and the inoculum size, for the maximization of the ethanol yield. A comprehensive structured mathematical model was developed to assist in the objective of a high performance fermentation process. The developed model was tuned and validated against experimental data. Finally, a number of empirically designed batch and fed-batch experiments were conducted. The exponential-like feeding policy was proved as the most promising one to maximize the process performance. However, it was revealed that the best of the process performance cannot be

A. Karapatsia et al. / Biomass and Bioenergy 90 (2016) 32e41

achieved with heuristic administration of the process operating framework and an advanced model-based optimisation approach is needed. Acknowledgments This work was carried out with the financial support of the General Secretariat of Research and Technology (GSRT) of Greece within the Operational Program “Competitiveness and Entrepreneurship” (OPCE ІІ) of the National Strategic Reference Framework (NSRF) 2007-2013, under the National Project titled «LIGNOFOS e Sustainable Production of Biofuels and High Value-added Biochemicals from Lignocellulosic Biomass»; grant number: 09SYN32-434. References [1] H.J. Huang, S. Ramaswamy, U.W. Tschirner, B.V. Ramarao, A review of separation technologies in current and future biorefineries, Sep. Purif. Technol. 62 (1) (2008) 1e21. [2] P. Sassner, M. Galbe, G. Zacchi, Techno-economic evaluation of bioethanol production from three different lignocellulosic materials, Biomass Bioenerg. 32 (5) (2008) 422e430. [3] B. Antizar-Ladislao, J.L. Turrion-Gomez, Second-generation biofuels and local bioenergy systems, Biofuels Bioprod. Bioref. 2 (5) (2008) 455e469. [4] S. Kim, B.E. Dale, Global potential bioethanol production from wasted crops and crop residues, Biomass Bioenerg. 26 (4) (2004) 361e375. [5] D. Van der Horst, S. Vermeylen, Spatial scale and social impacts of biofuel production, Biomass Bioenerg. 35 (6) (2011) 2435e2443. [6] I.A. Pappas, Z. Koukoura, C. Tananaki, C. Goulas, Effect of dilute acid pretreatment severity on the bioconversion efficiency of Phalaris aquatica L. lignocellulosic biomass into fermentable sugars, Bioresour. Technol. 166 (2014) 395e402. [7] M. Balat, Production of bioethanol from lignocellulosic materials via the biochemical pathway: a review, Energy Convers. Manage 52 (2) (2010) 858e875. ~es, J.A. Teixeira, L. Domingues, Optimization of [8] F.B. Pereira, P.M.R. Guimara low-cost medium for very high gravity ethanol fermentations by Saccharomyces cerevisiae using statistical experimental designs, Bioresour. Technol. 101 (20) (2010) 7856e7863. [9] M.A. Khan, S. Yusup, M.M. Ahmad, Acid esterification of a high free fatty acid crude palm oil and crude rubber seed oil blend: optimization and parametric analysis, Biomass Bioenerg. 34 (12) (2010) 1751e1756. [10] A. Gorsek, K. Zajsek, Influence of temperature variations on ethanol production by kefir grains-mathematical model development, Chem. Eng. Transac. 20 (2010) 181e186. [11] E. Ccopa Rivera, A.C. Costa, D.I.P. Atala, F. Maugeri, M.R. Wolf Maciel, R. Maciel Filho, Evaluation of optimization techniques for parameter estimation: application to ethanol fermentation considering the effect of temperature, Process Biochem. 41 (7) (2006) 1682e1687. [12] E. Amillastre, C.A. Aceves-Lara, J.L. Uribelarrea, S. Alfenore, S.E. Guillouet,

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23] [24]

[25]

[26]

[27]

[28] [29]

41

Dynamic model of temperature impact on cell viability and major product formation during fed-batch and continuous fermentation in Saccharomyces cerevisiae, Bioresour. Technol. 117 (2012) 242e250. M. Phisalaphong, N. Srirattana, W. Tanthapanichakoon, Mathematical modelling to investigate temperature effect on kinetic parameters of ethanol fermentation, Biochem. Eng. J. 28 (1) (2006) 36e43. M. Machani, M. Nourelfath, S. D'Amours, A mathematically-based framework for evaluating the technical and economic potential of integrating bioenergy production within pulp and paper mills, Biomass Bioenerg. 63 (2014) 126e139. H. Jin, R. Liu, Y. He, Kinetics of batch fermentation for ethanol production with immobilized Saccharomyces cerevisiae growing on sweet sorghum stalk juice, Procedia Environ. Sci. 12 (A) (2012) 137e145. T.J. Hanly, M.A. Henson, Dynamic metabolic modeling of a microaerobic yeast co-culture: predicting and optimizing ethanol production from glucose/xylose mixtures, Biotech. Biofuels 6 (1) (2013) 44e59. J.M. Van Zyl, T.M. Harms, A model for estimating the medium properties of Avicel to ethanol conversion, Bioprocess Biosyst. Eng. 36 (9) (2013) 1311e1318. M.F. Jang, Y.S. Chou, Modeling and optimization of bioethanol production via a simultaneous saccharification and fermentation process using starch, J. Chem. Technol. Biotechnol. 88 (6) (2013) 1164e1174. K. Sriyudthsak, F. Shiraishi, Investigation of the performance of fermentation processes using a mathematical model including the effects of metabolic bottleneck and toxic product on cells, Math. Biosci. 228 (1) (2010) 1e9. I.H. Mustafa, A. Elkamel, A. Lohi, G. Ibrahim, S.S.E.H. Elnashaie, Structured mathematical modelling, bifurcation and simulation for the bioethanol fermentation process using Zymomonal mobilis, Ind. Eng. Chem. Res. 53 (14) (2014) 5954e5972. A. Karapatsia, G. Penloglou, I. Pappas, C. Kiparissides, Bioethanol production via the fermentation of Phalaris aquatica L. hydrolysate, Chem. Eng. Transac 37 (2014) 289e294. K.S. Yadav, S. Naseeruddin, G.S. Prashanthi, L. Sateesh, L.V. Rao, Bioethanol fermentation of concentrated rice straw hydrolysate using co-culture of Saccharomyces cerevisiae and Pichia stipitis, Bioresour. Technol. 102 (11) (2011) 6473e6478. S.K. Karna, R. Sahai, An overview on Taguchi method, Int. J. Eng. Math. Sci. 1 (2012) 1e7. €mper, T. Horn, Calorimetric control of the specific growth R. Biener, A. Steinka rate during fed-batch cultures of Saccharomyces cerevisiae, J. Biotechnol. 160 (3e4) (2012) 195e201. K. Zajsek, A. Gorsek, Mathematical modelling of ethanol production by mixed kefir grains yeast population as a function of temperature variations, Biochem. Eng. J. 49 (1) (2010) 7e12. S. Malherbe, V. Fromion, N. Hilgert, J.-M. Sablayrolles, Modeling the effects of assimilable nitrogen and temperature on fermentation kinetics in enological conditions, Biotechnol. Bioeng. 86 (3) (2004) 261e272. A. Singh, N. Bishnoi, Optimization of ethanol production from microwave alkali pretreated rice straw using statistical experimental designs by Saccharomyces cerevisiae, Ind. Crop Prod. 37 (1) (2012) 334e341. A. Fiechter, W. Seghezzi, Regulation of glucose metabolism in growing yeast cells, J. Biotechnol. 27 (1) (1992) 27e45. B. Maiorella, H.W. Blanch, W. Charles, By-Product Inhibition effects on ethanolic fermentation by Saccharomyces cerevisiae, Biotechnol. Bioeng. 25 (1) (1983) 103e121.