Application of a modified Anaerobic Digestion Model 1 version for fermentative hydrogen production from sweet sorghum extract by Ruminococcus albus

international journal of hydrogen energy 35 (2010) 3423–3432

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Application of a modified Anaerobic Digestion Model 1 version for fermentative hydrogen production from sweet sorghum extract by Ruminococcus albus I. Ntaikou a,b, H.N. Gavala a,c,*, G. Lyberatos a,b a

Department of Chemical Engineering, University of Patras, Karatheodori 1 St., 26500 Patras, Greece Institute of Chemical Engineering and High Temperature Chemical Processes, 26504 Patras, Greece c Copenhagen Institute of Technology (Aalborg University Copenhagen), Section for Sustainable Biotechnology, Department of Biotechnology, Chemistry and Environmental Engineering, Lautrupvang 15, DK 2750 Ballerup, Denmark b

article info

abstract

Article history:

The aim of the present study was to evaluate the effectiveness of a developed, ADM1-based

Received 2 December 2009

kinetic model for the hydrogen production process in batch and continuous cultures of the

Received in revised form

bacterium Ruminococcus albus grown on sweet sorghum extract as the sole carbon source.

21 January 2010

Although sorghum extract is known to contain at least two different sugars, i.e. sucrose

Accepted 26 January 2010

and glucose, no biphasic growth was observed in batch cultures as such growth is reported

Available online 4 March 2010

to occur in cultures of R. albus with mixed substrates. Thus, taking into account that the main sugar of sweet sorghum extract is sucrose, batch experiments with different initial

Keywords:

concentrations of sucrose were performed in order to estimate the growth kinetics of the

Kinetic model

bacterium on this substrate. The kinetic parameters used, concerning the endogenous

ADM1

metabolism of the bacterium as well as those concerning the effect of pH and hydrogen

Biohydrogen

partial pressure (PH2), were the same as those estimated in a previous study with glucose as

Biomass

carbon source. Subsequently, the experimental data of batch and continuous experiments

Sweet sorghum

with sweet sorghum extract were simulated based on the already developed, modified

Ruminococcus albus

ADM1 model accounting for the use of sugar-based substrate. It was shown that the model which was developed on synthetic substrates was successful in adequately describing the behavior of the microorganism on a real substrate such as sweet sorghum extract and predicting the experimental results quite well with a deviation of the model predictions from the experimental results being between 5-18% for the hydrogen yield. ª 2010 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.

1.

Introduction

It is widely acknowledged that hydrogen is an attractive candidate for the replacement of conventional fossil fuels, from both an environmental and an economic standpoint [1–6]. This is due to the fact that hydrogen is a clean and

renewable energy carrier, possessing higher energy yield than other biofuels [7,8] and having zero emissions when burned [9]. In order though to render hydrogen production truly sustainable, it has to be based on renewable sources, such as water and biomass [1,2,5]. Two are the main basic strategies so far proposed for the exploitation of the latter, i.e. the

* Corresponding author. Copenhagen Institute of Technology (Aalborg University Copenhagen), Section for Sustainable Biotechnology, Department of Biotechnology, Chemistry and Environmental Engineering, Lautrupvang 15, DK 2750 Ballerup, Denmark. Tel.: þ45 99402586. E-mail addresses: [email protected], [email protected] (H.N. Gavala). 0360-3199/$ – see front matter ª 2010 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2010.01.118

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Nomenclature km, kg COD S/kg X .d maximum specific rate of substrate consumption mmax, d1 maximum specific growth rate YX/S, kg COD X/kg COD S yield of microbial biomass pH inhibition factor for low-pH inhibition IpH non-competitive substrate inhibition factor IS Ks, kg COD/m3 Monod half saturation constant S, kg COD/m3 substrate concentration pHmeas, log10[Hþ] measured pH value pHUL, log10[Hþ] optimum pH value for microbial growth pHLL, log10[Hþ] inhibitory lower limit of pH KIS, kg COD/m3 substrate concentration for consumption rate half of max kd, d1 decay constant

thermochemical approach, which comprises technologies such as gasification, pyrolysis, supercritical conversion etc [10–12] and the biotechnological approach, which includes dark fermentation and photofermentation [13–15]. Among the above methods, dark fermentation requires the simplest and most economical technology, which is actually based on the microbial bioconversion of the carbohydrates, contained in the biomass, to hydrogen [16]. Energy crops such as sugarcane and sorghum are rich in simple sugars that can be easily fermented to hydrogen. The crop that was used in the present study was sweet sorghum (Sorghum bicolor (L.) Moench), an annual C4 plant of tropical origin which is well-adapted to sub-tropical and temperate regions and exhibits low water demands and high photosynthetic efficiency [17,18]. The main sugars contained in its stalks are sucrose and glucose [19] that can be removed either as juice, using mechanical pressure [20,21], or as an extract solution using hot water extraction [22]. The residual biomass from either process, namely sorghum bagasse, consists mainly of complex carbohydrates that can be further used for extra production of hydrogen or other biofuels, either via thermochemical or biological methods. The use of sweet sorghum for biofuels production has focused for many years mainly on the bioethanol production field [20–26]. Lately however its potential for fermentative hydrogen production field has also been investigated [27–32]. Fermentative hydrogen production, although requiring a rather simple technology, can be quite complex in terms of optimisation and repeatability, mainly due to the large number of the parameters that influence the process efficiency [33]. It is indeed true that in the past decades, great progress has been achieved on the understanding and development of the process, through many different studies of both mixed microbial cultures [28,29,34–41] and pure ones, composed of one or more microorganisms [42–50]. Moreover, a remarkable variety of substrates has been tested, ranging from commercial sugars and carbohydrates [51–58], used to prepare synthetic media, to complex types of wastes and biomass [28–32,37,39,41,50,59–62]. Among others, studies have focused on the description and evaluation of the fermentative hydrogen production process by kinetic analysis, via which

YP/S, kg COD/m3 yield of product due to glycolysis SP, kg COD/m3 product concentration KF, kg COD/kg X d formate degradation constant IH2, atm inhibition factor of formate degradation PH2, atm hydrogen partial pressure KI-H2, atm hydrogen partial pressure inhibition constant fAC/S, kg COD ac/kg COD S yield coefficient of acetate fFO/S, kg COD for/kg COD S yield coefficient of formate fETH/S, kg COD eth/kg COD S yield coefficient of ethanol fH/S, kg COD H2/kg COD S yield coefficient of hydrogen due to glycolysis YAC/S, kg COD ac/kg COD S yield of acetate YF/S, kg COD for/kg COD S yield of formate YETH/S, kg COD eth/kg COD S yield of ethanol YH/S, kg COD H2/kg COD S yield of hydrogen due to glycolysis

the quantification of the proposed processes and the further prediction of their effectiveness on variable conditions could be predicted. Many kinetics models have been so far proposed, mainly based on the Gompertz model or on Monod kinetics, and incorporating variables such as substrate concentration and pH [63]. Lately, the possible modification and application of the Anaerobic Digestion Model 1 (ADM1), a complex kinetic model primarily developed for the quantification of the anaerobic digestion process [64], for the description of the fermentative hydrogen process by R. Albus has been proposed [66]. The ADM1 framework offers a remarkable flexibility (with appropriate modification) for describing the performance of both pure [65,66] and mixed cultures [67,68] in either batch [65–67] or continuous systems [68]. The aim of the present study was to evaluate the efficiency of a previously modified version of ADM1, for the prediction of hydrogen production and metabolites distribution during growth of the bacterium Ruminococcus albus on sweet sorghum extract. The model was initially developed in order to describe the metabolism of R. albus during hydrogen production from a glucose-based synthetic substrate [66]. Batch experiments with sucrose as the carbon source were performed in this study, in order to estimate the required kinetic constants of the bacterial growth on that substrate, since sucrose is the main sugar contained in sweet sorghum extract. Subsequently, the estimated parameters were used in the modified model and the model prediction was compared with the experimental data from batch and continuous experiments with sorghum extract.

2.

Materials and methods

2.1.

Microorganism, mediums and growth conditions

R. albus, strain DSMZ 20455 [69,70] was obtained from the Deutsche Sammlung von Microorganismen und Zellkulturen (DSMZ) culture collection and was maintained in a modified DSMZ 453 medium that was prepared according to the procedure described previously by Ntaikou et al. [30]. Stock cultures were stored at 22  C in 20% glycerol and inoculation

international journal of hydrogen energy 35 (2010) 3423–3432

cultures were transferred twice before use. The base medium was used in all cultures, while the carbon source varied depending on the substrate tested, i.e. glucose, sucrose and sorghum extract. All cultures were grown at 37  1  C and continuous stirring of 200  10 rpm, whereas anaerobic conditions were ensured by sparging with CO2:N2 gas mixture (30:70v/v).

2.2.

Sweet sorghum extract

The sweet sorghum (Sorghum bicolor L. Moench) used in the present study was produced in field experiments through biological farming techniques according to the European Regulation EC 2092/91. The experiments were conducted at the University of Patras experimental station. Sweet sorghum var. Keller seeds were sown at mid May and the stalks were harvested at mid October. After the harvesting of sorghum stalks, the fresh stems were stripped from the leaves, were chopped to a size of 20 cm and were stored in the freezer at –20  C. Subsequently, the stalks were milled by a laboratory grinder to an average particle size of 1–2 mm, and used for the production of extract according to the procedure described by Ntaikou et al., 2008 [30]. Sorghum extract was preserved at 21  C, at batches of 500 ml. For the needs of the experiments conducted in this study, an adequate quantity was thawed each time and was diluted in order to produce medium of the desired concentration of sugars.

2.3.

Analytical methods

The measurement of hydrogen was carried out by a gas chromatograph equipped with a thermal conductivity detector and a packed column with nitrogen as carrier gas. Soluble fermentation products were measured by two different chromatographic methods. For the quantification of acetic acid and ethanol, acidified samples with 20% H2SO4 were analyzed on a gas chromatograph, equipped with a flame ionization detector and a capillary column. Formic and lactic acid were measured by ion chromatography with an anionic column AS11 and conductivity detector CDM-3. The eluents used were NaOH 5 mM, NaOH 100 mM and purified water Milli-Q (Millipore), pressurized with He and the liquid flow was kept constant at 1.5 ml per minute. A gradient method was used starting with a concentration of 2.25 mM NaOH, until 65 mM NaOH. Before analysis of organic products, the liquid samples were centrifuged at 10.000 rpm for 10 min, and supernatants were passed through a membrane filter (0.2 mm pore size). For total and soluble (following centrifugation and filtration of the supernatant) carbohydrates determination, a colored sugar derivative was produced through the addition of L-tryptophan, sulphuric and boric acid [71,72], which was subsequently measured colorimetrically at 520 nm. Biomass was estimated either by the direct measurement of the optical density at 550 nm or by measuring the protein concentration using a modified Bradford method [30,73].

2.4.

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Batch experiments

Three series of batch experiments were conducted in serum vials of 161 ml total volume, sealed with rubber stoppers and crimped with aluminum seals. The first series was performed in order to estimate the kinetics constants of R. albus during its growth on sucrose. For that reason a set of three batch cultures were carried out in duplicate, with different initial sucrose concentration i.e. 4 g/L, 8 g/L and 12 g/L. Sucrose and biomass concentrations, as well as pH values were followed versus time and the growth kinetics of R. albus on sucrose were determined. The second series of cultures was conducted so as to verify whether the kinetic constants determined for either glucose or sucrose could satisfactorily fit the experimental data during growth on sorghum extract. It consisted of three batch cultures in duplicate, with sorghum extract at different initial concentrations of sugars i.e. 4 g/L, 8 g/L and 12 g/L measured as glucose equivalents. Sugars (expressed either as glucose or as sucrose equivalents) and biomass concentrations and pH values were followed versus time, and the experiments were simulated using the kinetic constants previously determined for both glucose and sucrose. Regarding the first and second set of experiments, liquid samples of 3 ml total volume were removed from each vial according to sampling schedule, using sterilized syringes and under aseptic conditions. Subsequently, the analyses of the samples were performed as described above. The third series was performed in order to verify the ability of the integrated model to predict the course of generation of soluble and gaseous metabolites in batch cultures of R. albus with sorghum extract. For that reason two cultures were carried out in duplicate, with initial concentration of sugars 5 g/L and 10 g/L measured as glucose equivalents. Gaseous samples of 0.5 ml (duplicate) and liquid samples of 3 ml were removed from each vial according to sampling schedule, using sterilized syringes and under aseptic conditions. Subsequently, sugars, microbial biomass and soluble metabolites concentrations, pH and cumulative hydrogen were analyzed. The experimental results were further simulated using the modified ADM1 that was developed by Ntaikou et al. [66]. The growth kinetics used were the ones estimated for growth of R. albus on sucrose, whereas the kinetic parameters concerning the endogenous metabolism of the bacterium, as well as those concerning pH and hydrogen partial pressure (PH2) effect used were the same, as those estimated previously by Ntaikou et al. [30]. In all cases the initial PH2 in the gas phase was zero, and the inoculum used for each set was 20% (v/v) of the same culture of R. albus with sucrose at late stationary phase.

2.5.

Continuous experiments

Continuous experiments were performed in a 2.5 L Virtis Omni-culture bench-top chemostat with working volume of 1320 ml and headspace of 1630 ml. The sterilized feed medium was continuously supplied to the chemostat using a peristaltic pump. The medium supply and the operating mode were as described previously by Ntaikou et al. [30]. The operating hydraulic retention time (HRT) tested was

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42 hours, as this was selected as the most efficient for hydrogen production from glucose according to previous experiments [30]. Liquid and gas sampling were performed directly from the reactor daily (liquid samples of 5 ml and gas samples of 0.5 ml were removed directly from the reactor, using sterile syringes). The total gas produced was also collected daily and its volume was estimated by replacement of acidified water. Sugars, biomass, acids and ethanol concentrations, gas composition and pH were measured throughout the course of the operational period and subsequently, the experiments were simulated using the modified ADM1 model.

3.

Kinetic expressions and modeling

For the simulation of the experimental data from both batch and continuous experiments the modified ADM1 model developed by Ntaikou et al. 2009 [66] was used. The kinetic expressions onto which the model was based are presented in Table 1 (Equations (1)–(8)). Parameter estimation and model simulations were performed using the AQUASIM 2.0 computer software [74]. As previously reported [66,75,76] for the estimation of the COD equivalents of microbial biomass, the empirical formula C5H7O2N was used. All organic substances and molecular hydrogen were described in terms of chemical oxygen demand (kg COD.m3), whereas inorganic carbon, inorganic nitrogen and trace metals were described in terms of their molar concentrations (M). Physicochemical processes

Table 1 – Kinetic expressions used for the simulation of the experimental data according to the model developed by Ntaikou et al., 2009 [66]. Process Substrate consumption rate Microbial growth rate Microbial decay rate pH inhibition of growth rate, IpH

Equation km

km

S XIpH IS KS þ S

S XIpH IS Y . KS þ S X S

(1)

(2)

kd X

2  pHmeas  pHUL exp  3 pHUL  pHLL

(3)

Substrate inhibition of growth, IS

KIS KIS þ S

(4)

Acetate and ethanol (P) production rate Formate production rate

Y . km

S XIpH IS KS þ S

(5)

Y . km

S XIpH IS KS þ S

(6)

Formate degradation rate Hydrogen inhibition of formate degradation, IH2 Hydrogen production rate (from glucose and formic acid degradation)

KF SF XIH2

P

F

S

S

1 H2 1 þ KPIH2 Y . km H2

S

C6 H12 O6 þ ð2  XÞH2 O/2HCOOH þ XCH3 CH2 OH (7)

S XIpH IS þ KF SF XIH2 KS þ S

like acid-base reactions, liquid-gas transfer and pH calculation in batch and continuous experiments as well as liquid and gas phase equations in the continuous experiments were described as in ADM1. Sorghum extract contains the soluble sugars of sorghum biomass, which consist of sucrose mainly as well as small amounts of glucose [19]. Sucrose is a disaccharide, widely used in industrial applications, that is formed by one mole of glucose and one mole of fructose bonded together by an a-1,20 b-glysosidic linkage [77]. Accordingly, it is expected to be metabolized by the same microorganism with a different rate and perhaps even to a different extent than glucose. The modified model that was used for simulating the experimental data of the present study was previously developed based on experiments with glucose as carbon source [66], and consequently the parameter estimation concerning microbial growth kinetics and metabolites generation kinetics refer to the metabolism of glucose by R. albus. Some of those parameters such as kd, pHLL, KF and KI-H2 were considered to remain the same, regardless the substrate used, since they are connected with the endogenous metabolism of the microorganism. On the other hand, parameters such as km, KS, YX/S and KIS had to be estimated by the model again based on experiments with sucrose. That was mainly due to the fact that, sucrose is known to be the main sugar in sweet sorghum. Moreover, the growth kinetic constants of glucose failed to predict satisfactorily the experimental data concerning sugars and microbial biomass concentrations during growth of the bacterium in the cultures with sweet sorghum extract of 4 g/L, 8 g/L and 12 g/L initial sugars concentrations. Since sucrose is a dimer composed of the monomers glucose and fructose i.e two hexoses with the same molecular structure, the generation of metabolites during its fermentation was expected to follow the same pattern with that of glucose fermentation. Consequently, the final distribution of metabolites was assumed to be of the same ratios. In ADM1, the product yield coefficient f, is actually the parameter describing the distribution of metabolites, since it expresses the ratio of COD that is biotransformed to a certain product from the overall net COD that is metabolized to products. In other words, f expresses the product yield without taking into account the amount of carbon that is incorporated in the biomass, referring thus only to the catabolism of the available carbon source. Regarding the product yield coefficients of the present study, i.e. fFO/S, fAC/S, fETH/S and fH/S (for formate, acetate, ethanol and hydrogen respectively), the values used were the ones estimated by the model with glucose as carbon source (namely 0.17, 0.27, 0.52 and 0.06 kg COD/kg COD of sucrose for formate, acetate, ethanol and hydrogen respectively), since the COD ratios of the products per substrate consumed are the same in both cases, as shown by Equations (9) and (10).

þ ð2  XÞCH3 COOH þ ð2  2XÞH2

(9)

C12 H22 O11 þ ð5  2XÞH2 O/4HCOOH þ 2XCH3 CH2 OH (8)

þ 2ð2  XÞCH3 COOH þ 2ð2  2XÞH2

(10)

The final product yields measured as g COD product/g COD sucrose (YFO/S, YAC/S, YETH/S and YH/S) were recalculated taking

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into account the newly estimated value of the biomass yield YX/S on sucrose (0.19 kg COD per kg COD sucrose), since Y is estimated on the basis of the total amount of sugar that was consumed for both anabolism and catabolism. The newly calculated final products yields were 0.13, 0.21, 0.41 and 0.05 kg COD per kg COD of sucrose for formate, acetate, ethanol and hydrogen respectively. This indirect calculation, which was based on the biomass yield in experiments with synthetic medium, had to be validated on the real biomass, i.e. the sweet sorghum extract. This was done during the third series of batch experiments described in section 2.4 and the results are discussed in section 4.2.

the enzyme cellobiose phosphorylase. Due to the presence of this enzyme, one molecule of glucose and one of P-glucose are generated, and thus the energy of the glycosidic bond is partly preserved. Consequently, just one mole of ATP is required and the energy profit leads to higher accumulation of carbon in the microbial cells. So far there are no known studies on the way that sucrose is metabolized by R. albus. However the intracellular breakdown of sucrose by phosphorylation is reported for other bacteria [80]. It could be assumed thus, that the same pathway is also used in the case of R. albus.

3

Concentration (kg COD/m )

a

Estimation of growth kinetic constants on sucrose

For the estimation of the constants km, KS, KI and YX/S, during growth of R. albus on sucrose, three batch cultures (first batch series in section 2.4) with different initial sucrose concentrations i.e. 4 g/L, 8 g/L and 12 g/L were conducted. Sucrose and biomass concentrations as well as pH were followed versus time and the experimental data were simulated by equations (1)–(4) of Table 1. For kd and pHll the same values as previously estimated by Ntaikou et al. [66] were used. Both experimental data and model simulation are shown in Fig. 1. The values of the estimated kinetic parameters during growth on sucrose are presented in Table 3. By comparing the results with the respective from the glucose grown culture (presented also in Table 3 for comparison purposes) it is observed that although the microbial growth rate is lower when sucrose is used as the substrate, the yield of microbial mass seems to be slightly higher. According to previous studies [78,79] during growth of R. albus on the disaccharide cellobiose instead of glucose, higher yields of biomass were obtained. Hungate [78] attributed this observation to the way that cellobiose is catabolized by the microorganism i.e via intracellular phosphorylation by

Table 2 – Composition of sorghum biomass that was used during the present study. Composition of sorghum biomass

Humidity Total carbohydratesa Soluble carbohydratesa Complex carbohydrates Proteins

(% of dry biomass) Whole stalks

Extraction residues

72.6  2.2 75.2  4.3 36.1  5.1 34.9  5.01 4.1  0.2

75.1  2.0 82.5  2.5 0.5  0.1 81.5  2.5 3.9  0.1

a Measured as glucose equivalents.

Sorghum extract (g/L)

3,5 3,0 2,5 2,0 1,5 1,0 0,5 0,0 0,0

b 3

4.1.

Concentration (kg COD/m )

The compositions of the sorghum biomass, as well as of the fractions after extractions, are given in Table 2. As anticipated sorghum extract was rich in soluble sugars, which are an ideal substrate for the fermentative hydrogen production.

0,2

0,4

0,6

0,8

1,0

1,2

0,8

1,0

1,2

0,8

1,0

1,2

time (d) 8 7 6 5 4 3 2 1 0 0,0

c 3

Results and discussion

Concentration (kg COD/m )

4.

4,0

0,2

0,4

0,6

time (d) 12 10 8 6 4 2 0 0,0

0,2

0,4

0,6

time (d) – 19.2  3.1 19.2  3.1 – <0.1

Fig. 1 – Experimental data and model simulation for R. albus batch cultures with different initial sucrose concentrations. (a) Cin, w4 g/L (b) Cin, w8 g/L (c) Cin, w12 g/ L. Symbols: measured sucrose concentration, measured biomass, ⎯ model prediction for sucrose, - - model prediction for biomass.

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Table 3 – Kinetics constants of R. albus growth on sucrose as were estimated in the present study in comparison with the kinetics constants of R. albus growth on glucose as were estimated in the study of Ntaikou et al. [66]. R. albus growth constants mmax km YX/S Ks KIs kda pHLLa

With sucrose as substrate [this study] 0.63  0.03 h1 4.47  0.61 g S/g Xh 0.15  0.00 g X/g S 1.53  0.23 g/l 17.18  2.9 g/l 0.042  0.002 h1

With glucose as substrate [66]

14.47  0.72 d1 142.02  13.99 kg COD S/kg COD X.d 0.19  0.05 kg COD X/kg COD S 1.63  0.25 kg COD/m3 18.32  3.1 kg COD/m3 1.02  0.06 d1 5.13  0.07

18.37  0.70 d1 99.60  6.09 kg COD S/kg COD X.d 0.18  0.01 kg COD X/kg COD S 0.70  0.02 kg COD/m3 25.34  1.58 kg COD/m3 1.02  0.06 d1 5.13  0.07

a As estimated by Ntaikou et al. with glucose as substrate [66].

10

3

Concentration (kg COD/m )

a

8 6 4 2 0 0,0

b

0,2

0,4

0,6

0,8

1,0

1,2

1,4

1,0

1,2

1,4

time (d) 10

3

The growth kinetic constants on glucose failed to predict satisfactorily the experimental data concerning sugars and microbial biomass concentrations during growth of the bacterium in the cultures with sweet sorghum extract of 4 g/L, 8 g/L and 12 g/L initial sugars concentrations (second batch series in section 2.4). Thus, and as it has already been reported (section 3), the growth kinetic constants on sucrose, as calculated from the first batch series, have been used to simulate the third experimental series. In order to examine the ability of the model to predict R. albus growth and metabolites production based on sweet sorghum extract, two batch experiments with two different initial concentration of sugars measured as sucrose equivalents, i.e. 5 g/L and 10 g/L, were performed (third batch series, section 2.4). Both experiments were conducted in duplicates and the experimental data were simulated using equations (1)–(8) of Table 1. The experimental results as well as the prediction of the model for biomass, sugars and soluble products concentration and hydrogen production are illustrated in Figs. 2–4. As far as microbial growth and consumptions of sugars are concerned, the model can predict quite accurately the experimental data, regardless of the initial sugar concentration (Fig. 2). Previous studies have shown that when R. albus grows on mixed sugars substrate consisting of glucose and either cellulose or pentoses, it selectively ferments one type of the available sugars first and thus microbial growth is diauxic [79,81]. However diauxic growth was not observed during growth of the microorganism on sorghum extract (containing both sucrose and glucose). Since as shown by the present study, the growth kinetics constants of R. albus are different for glucose and sucrose, a biphasic growth pattern should indeed be expected if the concentrations of glucose and sucrose in the medium were of comparative values. In the case of sweet sorghum extract though, glucose concentration is very low compared to sucrose in the extract (sorghum stalks contain 55% and 3.2% in dry matter sucrose and glucose, respectively [19]) and thus no diauxic growth is observed. The experimental results and the prediction of the model for the production of soluble metabolites are illustrated in Fig. 3. As it was expected based on previous studies with R. albus grown on sorghum biomass and other carbohydrate

substrates [28,30,66] the only metabolites detected apart from hydrogen and carbon dioxide were acetate, formate and ethanol. The experimentally measured yields for all products, measured as kg COD/product per kg COD of substrate consumed, as well as the final balance for both initial concentrations of sugars are given in Table 4. The model predicted quite satisfactorily the whole course of production of acetate and ethanol, as shown in Fig. 3 and Table 5 where

Concentration (kg COD/m )

4.2. Batch experiments with sweet sorghum extract: experimental data and model prediction

8 6 4 2 0 0,0

0,2

0,4

0,6

0,8

time (d) Fig. 2 – Experimental data and model prediction of sweet sorghum sugars consumption and microbial growth in batch cultures of R. albus with initial sugars concentration of 5 g/L (a), and 10 g/L (b). Symbols: B measured sugars, d model prediction for sugars, > measured biomass, d model prediction for biomass.

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5 4 3 2 1 0 0,6

0,8

1,0

1,2

5 4 3 2 1

0,2

0,4

0,6

0,8

1,0

1,2

1,2 0,8 0,4 0,0

0,0

0,2

0,4

1,0

1,2

1,4

1,0

1,2

1,4

1,6 1,2 0,8 0,4 0,0

0,0

0,2

0,4

0,6

0,8

time (d)

time (d)

the experimental and model predicted yields are presented. As illustrated in Fig. 3, the production of formate was also predicted rather well, except for the final values, which were underestimated (approximately 14% and 28% in batches with 5 and 10 g/L initial sugars concentrations respectively). This is more pronounced for the batches with higher initial concentration of sugars, similarly to what was observed in previous experiments with glucose [66].

0,8

2,0

1,4

Fig. 3 – Experimental data and model prediction of soluble metabolites generation from sweet sorghum extract in batch cultures of R. albus with initial sugars concentration of 5 g/L (a) and 10 g/L (b). Symbols: A measured ethanol, d model prediction for ethanol, measured acetate, d model prediction for acetate, V measured formate, d model prediction for formate.

0,6

time (d)

b

6

0,0

1,6

1,4

time (d)

0

2,0

3

0,4

3

Soluble products (kg COD/m )

b

0,2

Cumulative hydrogen (kg COD/m )

0,0

3

a

6

3

Soluble products (kg COD/m )

a

Cumulative hydrogen (kg COD/m )

international journal of hydrogen energy 35 (2010) 3423–3432

Fig. 4 – Experimental data and model prediction of hydrogen production from sweet sorghum extract in batch cultures of R. albus with initial sugars concentration of 5 g/L (a) and 10 g/L (b). Symbols: C measured produced H2, d model prediction for H2 production (expressed as kg COD H2/m3 of culture).

The experimental data concerning the generation of hydrogen as well as the simulations of the model are illustrated in Fig. 4. As shown, the simulation for the cultures with 5 g/L initial sugars concentration fit quite well, verifying that the model can be accurate in predicting the behavior of the microorganisms at low initial concentrations of sugars. Regarding the batches with 10 g/L of initial sugars concentration, however, it seems that the model although being quite successful in predicting the initial experimental behavior, it

Table 4 – Experimentally measured yields and total equilibrium from batch cultures of R. albus in sorghum extract with initial sugars concentrations of 5 g/L and 10 g/L. Initial concentration of sugarsa

5 g/L 10 g/L

Yield (kg COD/kg COD sugars) Formate

Acetate

Ethanol

H2b

Sum of yields with biomass

0.05  0.00 0.04  0.00

0.24  0.02 0.23  0.01

0.43  0.02 0.45  0.02

0.18  0.01 0.17  0.01

1.01  0.06 1.09  0.05

a Measured as sucrose equivalents. b From both glycolysis and via formate degradation.

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Table 5 – Experimental and model predicted yields of hydrogen, acetate and ethanol obtained from batch cultures of R. albus in sorghum extract with initial sugars concentrations of 5 g/L and 10 g/L. Product Cin of sugarsa

Product yields kg COD/kg COD sucrose Experimental

Model

Model deviation %c

5 g/L

H2 Acetate Ethanol

0.18  0.01 0.24  0.02 0.43  0.02

0.19b 0.21 0.41

5.55 12.50 4.65

10 g/L

H2 Acetate Ethanol

0.17  0.01 0.23  0.01 0.45  0.02

0.20b 0.21 0.41

17.65 8.70 8.89

a Measured as sucrose equivalents. b Estimated based on both glycolysis and formate degradation. c Compared to the mean estimated experimental value.

tends to overestimate the late values. This can be attributed to the respective underestimation of formate degradation and is in accordance with previous observations [66]. The experimental and model predicted yields of hydrogen, acetate and ethanol for the third batch series are presented in Table 5. As expected based on the previous observation regarding the model prediction for the overall hydrogen generation course, the hydrogen yields as estimated by the model are in both cases higher than the experimentally estimated ones, and this tendency of the model seems to be more significant for the higher initial concentration of sugars.

4.3. Continuous experiments with sweet sorghum extract: experimental data and ability of modified ADM1 to predict the experimental results In Table 6 the measured values of sugars, biomass and products concentration from a continuous R. albus culture with HRT 42 h at steady state and the respective model prediction are presented. It can be observed, that in general, the model predictions are in good agreement with the experimental values. Therefore, the developed model could be used for the prediction of the performance of both batch and continuous cultures. It is noticeable that the model tends to underestimate the production of formate in the continuous cultures too, with no significant impact though on the prediction of hydrogen production.

Table 6 – Measured values and model prediction during the steady state of R. albus continuous culture with sorghum extract at an HRT of 42 h.

Sugars consumption, (%) Biomass, (kg COD/m3) pH Acetate, (kg COD/m3) Formate, (kg COD/m3) Ethanol, (kg COD/m3) PH2, (atm)

Experimental

Model

94.77  4.02 0.29  0.03 6.54  0.02 1.46  0.07 0.27  0.02 2.05  0.27 0.53  0.02

96.24 0.35 6.71 1.20 0.19 2.30 0.57

5.

Conclusions

A quantitative kinetic model, initially developed in order to describe the metabolism of R. albus during its growth on glucose, was used for simulating the experimental results from batch and continuous cultures of the bacterium, with sweet sorghum extract as the sole carbon source. The growth kinetics obtained based on sucrose based growth proved to be adequate for predicting the behavior of the microorganism when grown on sweet sorghum extract. It was also shown that the kinetic constants concerning the endogenous metabolism of the bacterium as well as the coefficients expressing the distribution of the metabolites that were previously estimated from glucose based experiments, could describe satisfactorily the process of hydrogen generation from sweet sorghum. Overall, the values of the model predictions were in all cases in good agreement with the experimental values of both batch and continuous experiments. The deviation of the model predictions from the experimental results was between 5-18% for the hydrogen yield.

Acknowledgements The authors wish to thank the Hellenic General Secretariat for Research and Technology for the financial support of this work under ‘‘PENED 2001, 01ED390’’.

Appendix. Supplementary information Supplementary material associated with this article can be found in the online version, at doi:10.1016/j.ijhydene.2010.01. 118.

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