Kinetic modeling of batch photofermentation hydrogen gas production byRhodopseudomonas palustris PBUM001Zadariana Jamil, Mohamad Suffian Mohamad Annuar, Shaliza Ibrahim, and S. Vikineswary Citation: J. Renewable Sustainable Energy 4, 043105 (2012); doi: 10.1063/1.4737131 View online: http://dx.doi.org/10.1063/1.4737131 View Table of Contents: http://jrse.aip.org/resource/1/JRSEBH/v4/i4 Published by the American Institute of Physics. Related ArticlesHydrogen production from methane conversion in a gliding arc J. Renewable Sustainable Energy 4, 021202 (2012) A study of H+ production using metal hydride and other compounds by means of laser ion source Rev. Sci. Instrum. 83, 02B318 (2012) Anaerobic fermentation hydrogen production from apple residue: Effects of sludge pretreatments J. Renewable Sustainable Energy 4, 013104 (2012) Mott insulators: An early selection criterion for materials for photoelectrochemical H2 production J. Renewable Sustainable Energy 3, 053101 (2011) High-purity hydrogen generation by ultraviolet illumination with the membrane composed of titanium dioxidenanotube array and Pd layer Appl. Phys. Lett. 99, 123107 (2011) Additional information on J. Renewable Sustainable EnergyJournal Homepage: http://jrse.aip.org/ Journal Information: http://jrse.aip.org/about/about_the_journal Top downloads: http://jrse.aip.org/features/most_downloaded Information for Authors: http://jrse.aip.org/authors

Kinetic modeling of batch photofermentation hydrogen gasproduction by Rhodopseudomonas palustris PBUM001

Zadariana Jamil,1,a) Mohamad Suffian Mohamad Annuar,2 Shaliza Ibrahim,3

and S. Vikineswary2

1Faculty of Civil Engineering, Universiti Teknologi MARA Pahang, Bandar Pusat Jengka,Pahang 26400, Malaysia2Institute of Biological Sciences, University of Malaya, Kuala Lumpur 50603, Malaysia3Department of Civil Engineering, Faculty of Engineering, University of Malaya,Kuala Lumpur 50603, Malaysia

(Received 27 July 2011; accepted 26 May 2012; published online 16 July 2012)

An indigenous purple non-sulfur bacteria Rhodopseudomonas palustris PBUM001 was

used to produce hydrogen gas via batch photofermentation of palm oil mill effluent

(POME). The photofermentation hydrogen production was carried out in a 5-l reactor

(B. Braun BiostatVR

B) with a working volume of 3.5 l (height: 39 cm and diameter:

16 cm) under anaerobic condition. The stirred tank reactor (STR) was conducted at

temperature, 30 6 2 �C; POME concentration, 100% (v/v); light intensity, 4.0 klux;

pH 6, inoculum size, 10% (v/v); agitation rate, 250 rpm, and operated for 66 h. Two

sets of experiments were run in STR (R1 and R2) and the data obtained were used for

kinetic study of photofermentation hydrogen production. Unstructured models were

used to describe the bacterial growth, substrate consumption, and hydrogen gas

production by R. palustris PBUM001. The discrepancy between the proposed model

and the experimental data in simulating hydrogen production from POME by

R. palustris PBUM001 was measured by using residual sum of squares (RSS).

Logistic model could be adopted to describe the kinetics of bacterial growth

(RSS: 0.3039–0.2313) and the proposed model for substrate consumption agreed well

with the experimental data obtained in this study as shown by its RSS value of 19.1319

and 26.8259 for R1 and R2, respectively. A modified Leudeking-Piret model was

applied for the data fitting to determine the relationship between the cell growth and

photofermentation hydrogen production (RSS: 1.3267–26.3741). VC 2012 AmericanInstitute of Physics. [http://dx.doi.org/10.1063/1.4737131]

I. INTRODUCTION

The interest in hydrogen as a source of energy has been extensively explored over the past

decades. Hydrogen is known as a clean and efficient fuel and considered as a potential substi-

tute for fossil fuel. There are four reasons that can contribute to the change of fossil fuel to

hydrogen gas as major energy source namely, increasing global environmental concerns on

greenhouse gasses production (e.g., CO2) by fossil fuels usage; energy insecurity due to politi-

cal and economical instability; scarcity of oil commodity; and rapid population growth through-

out the world.1 However, global hydrogen gas production that is linked to the consumption of

fossil fuels has to shift towards renewable sources which are environmentally benign processes,

and biohydrogen is an economically viable approach as it generates hydrogen gas from various

renewable substrates such as organic wastes under mild operating conditions.

The major criteria for the selection of waste materials to be used in biohydrogen production are

its availability, cost, carbohydrate content, and biodegradability. Thus, major waste materials which

a)Author to whom correspondence should be addressed. Electronic addresses: riana_jay@yahoo.com.sg and zadariana@

pahang.uitm.edu.my. Tel.: þ6094602702. Fax: þ6094602455.

1941-7012/2012/4(4)/043105/12/$30.00 VC 2012 American Institute of Physics4, 043105-1

JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY 4, 043105 (2012)

can be used for hydrogen production processes are starch and cellulose containing agricultural and

food industry wastes, carbohydrates rich industrial wastes (e.g., dairy industry, olive mill baker’s

yeast, brewery wastewater), and waste sludge from wastewater treatment plants.2 Several types of

wastewater were studied for hydrogen production either by dark fermentation or photofermentation

namely chemical wastewater,3 food processing and domestic wastewater,4 sugar refinery waste-

water,5 tofu wastewater,6 and olive oil wastewater.7 In Malaysia, palm oil mill effluent (POME) is

well known for its rich organic carbon content wastewater. The POME is generated from palm oil

milling processes to produce crude palm oil (CPO) and it is the most polluting agro-industrial efflu-

ent. Physically, raw POME is thick brownish colloidal slurry of water, oil, and fine suspended solids.

Furthermore, it is high in temperature (80–90 �C), acidic (pH 4.5), rich in organic carbon with a

chemical oxygen demand (COD) of 50 000 mg/l, biological oxygen demand (BOD), 30 000 mg/l, oil

and grease, 6000 mg/l, suspended solids, 59 350 mg/l, and 750 mg/l of total nitrogen.8 Several studies

showed the advantage of using POME as the hydrogen substrate can help to reduce its COD

concentration.9–13

The conversion process of the waste materials (e.g., starch, cellulose, carbohydrates, waste

sludge) can be carried out by a large number of hydrogen-producing microorganisms, including

obligate and facultative anaerobes, aerobes, cyanobacteria, photosynthetic bacteria, and algae

through photo- or dark-fermentation processes.14 Photofermentation hydrogen gas production is

favored due to relatively higher substrate-to-hydrogen yields, and its ability to trap energy

under a wide range of spectrum and versatility of metabolic substrates utilization with simulta-

neous waste stabilization.15 Photosynthetic bacteria, purple non-sulfur bacteria such as Rhodo-bacter capsulatus, Rhodopseudomonas sphaeroides, and Rhodopseudomonas palustris are com-

monly utilized for hydrogen gas production from various types of carbon sources.16–21

Nevertheless, few reports on kinetic analysis of biohydrogen gas production by photosynthetic

bacteria have been published. It is important to describe relationship among the principal state

variables in order to explain quantitatively the behavior of fermentation process. In addition, it

also provides useful information for the analysis, design, and operation of a fermentation pro-

cess for a successful biological hydrogen gas production.22

The objective of the present study was to develop a model for bacterial growth, substrate

consumption, and hydrogen gas production by R. palustris PBUM001 in a batch photobioreac-

tor. The development of such a model is important to understand and control the process of

photofermentation hydrogen gas production from industrial waste, specifically POME.

II. MATERIALS AND METHODS

A. Microorganism and medium

R. palustris PBUM001 was isolated from rice noodle processing wastewater23 and was

obtained from the Institute of Biological Sciences, University of Malaya culture collection. The

PBUM001 strain was grown and maintained in sterilized glutamate malate medium (GMM) at

pH 7.23 The cells were grown in 20 ml McCartney bottles containing 18 ml medium at

30 6 2 �C under anaerobic-light conditions with 2.5 klux illumination of tungsten light bulbs for

48 h. In stock culture, the cells were preserved and routinely maintained at 4 �C as stabbed cul-

ture on GMM solidified with 1.5% agar and sealed with sterile paraffin oil until it was ready

for use. The PBUM001 strain was further grown under the same conditions on GMM agar

plates and incubated for 7 days in anaerobic jar (Gas Generating kit, OXOID) and the liquid

culture started from a single colony.

B. Sample collection and preparation

POME was collected from a local palm oil mill in Dengkil (Selangor, Malaysia). The col-

lected POME was initially pre-settled for 24 h inside a 4 �C cold room and centrifuged at

7000 rpm for 15 min. The physical and chemical characteristics of raw and centrifuged POME

are given in Table I. The centrifuged POME was stored in plastic containers inside a freezer at

�20 �C to prevent the wastewater from undergoing biodegradation due to microbial action.24

043105-2 Jamil et al. J. Renewable Sustainable Energy 4, 043105 (2012)

The POME was thawed at 4 �C before use and the remaining solids in the POME were filtered

with Whatman (No. 1) filter paper. The pH of the filtered wastewater was adjusted to pH 6 and

autoclaved at 121 �C for 15 min prior to its use as a culture medium.

C. COD

The COD determination is a measurement of the oxygen equivalent to the portion of or-

ganic matter in a sample that is susceptible to oxidation by a strong chemical oxidant. Standard

methods (APHA, 1992) open reflux method was used. A sample of 20 ml was diluted to 50 ml

with distilled water and added with 0.4 g of mercuric sulphate and anti-bumping granules. The

mixture was then refluxed for 2 h with 10 ml of standard potassium dichromate solution and

30 ml of sulphuric acid reagent. The sample was cooled down and diluted to 140 ml with dis-

tilled water. The sample was then titrated with 0.1 M ferrous ammonium sulphate using ferroin

solution as indicator. The end point is sharp color change from blue-green to reddish-brown.

The amount of oxidizable organic matter, measured as oxygen equivalent, is proportional to the

potassium dichromate used. The COD was calculated based on the formula as follows:

CODðmg=lÞ ¼ 8000� ðA� BÞ � D

sample volumeðmlÞ ; (1)

where A is the volume (ml) of ferrous ammonium sulphate used for the titration of blank; B is

the volume (ml) of ferrous ammonium sulphate used for sample titration, and D is the dilution

factor used as per dilution table provided in APHA (1992).

D. Total Kjedahl nitrogen (TKN)

Total kjedahl nitrogen content was determined by the macro-Kjedahl method (APHA,

1992) using Tecator Kjeltec System 1002 Distilling Unit. Digestion reagent and anti-bumping

granules were added with 25 ml of neutralized liquid sample in a distillation flask. The mixture

was mixed and heated by using a block heater in the fume hood for about 2 h. After digestion,

cooled samples were diluted to 300 ml with distilled water, added with hydroxide-thiosulfate re-

agent, and distilled into indicating boric acid solution. The mixture of distillate and indicating

boric acid was titrated against 0.02 N sulphuric acid until the colour changed to a final colour

of pale lavender at the end of titration. Total Kjedahl nitrogen was determined using the follow-

ing formula:

TKNðmg=lÞ ¼ ðA� BÞ � 280

sample volumeðmlÞ ; (2)

where A is the volume of H2SO4 titrated for sample (ml); B is the volume of H2SO4 titrated for

blank (ml), and 280 is the standardize titrant against an amount of 1 ml Na2CO3 that has been

incorporated in the indicating boric acid solution.

TABLE I. Physical and chemical characteristics of POME.

POME

Characteristicsa Raw After centrifugation

pH 4.2–4.5 4.2–4.5

COD (mg/l) 74 894 6 1183 8880 6 3507

Total solids, TS (g/l) 186 6 14 20 6 6

Total suspended solids, TSS (mg/l) 17 000 6 849 240 6 31

TKN (mg/l) 77 6 1 61 6 6

aData presented is the mean of 3 readings.

043105-3 Jamil et al. J. Renewable Sustainable Energy 4, 043105 (2012)

E. Seed culture

Seed culture was prepared by adjusting the bacterial culture to 0.2 g dry wt. l�1 at OD660

of 0.3. R. palustris PBUM001, at 10% (v/v) was acclimatized in sterilized 25% (v/v) diluted

POME in 500 ml Schott bottle with a working volume of 350 ml. The liquid medium inside the

bottles were flushed with nitrogen (N2) gas for 4 min. Then the cultures were grown under simi-

lar conditions as in GMM. The bottles were agitated continuously at 150 rpm for 48 h. The

acclimatized PBUM001 strain was used as seed culture.

F. Stirred tank reactor (STR)

The kinetic study of photofermentation hydrogen production was carried out in 5 l reactor

(B.Braun Biostat B) with a working volume of 3.5 l (height: 39 cm and diameter: 16 cm). The

reactor vessel containing POME was sterilized at 121 �C for 15 min. Then the reactor’s content

was flushed with N2 gas for 15 min in order to provide an anaerobic condition before inocula-

tion. The STR was operated for 66 h under optimum conditions that were determined in a previ-

ous optimization study.17 The conditions were POME concentration, 100% (v/v); light intensity,

4.0 klux; pH 6; inoculum size, 10% (v/v) and agitation rate, 250 rpm. The pH and temperature

of the reactor were automatically controlled at 30 6 2 �C and pH 6 6 0.1. The reactor was

ensured to be air-tight so that no gas leakage occurs. A gas bag was attached to the reactor for

the purpose of biogas collection. Biogas produced and liquid samples were collected at regular

intervals during 66 h of cultivation. The biogas was analyzed for hydrogen content and the

broth sample was analyzed for COD, TKN (Ref. 25) and bacterial cell dry weight. The sche-

matic diagram of the bioreactor process is shown in Fig. 1.

G. Biogas analysis

The biogas collected was transferred from the gas collection bag to a volumetric flask and

sampling gas bottles for the purpose of biogas volume measurement and hydrogen gas analysis,

respectively. Water displacement method was applied for both transfers using acidic water

(<pH 3) in order to prevent dissolution of the gas components.9 The gas composition was

determined by gas chromatography (Shimadzu Corp., Japan, GC-8A), equipped with thermal

conductivity detector (TCD), Porapak Q column, N2 as carrier gas, detector/injector temperature

at 100 �C, column temperature at 50 �C. 1 ml of the gas sample was injected in replicate.

The hydrogen gas production was calculated from headspace measurements. The cumula-

tive hydrogen production for each time interval was calculated using the mass balance equation

as follows:26

FIG. 1. A schematic diagram of the photobiological hydrogen gas production by R. palustris PBUM001 in a 5-l STR.

043105-4 Jamil et al. J. Renewable Sustainable Energy 4, 043105 (2012)

VH;i ¼ VH;i�1 þ CH;iðVG;i � VG;i�1Þ þ VHðCH;i � CH;i�1Þ; (3)

where VH,i and VH,i�1 denote cumulative H2 gas volumes (ml) at the current (i) and previous

(i�1) time intervals, respectively, VG,i and VG,i�1 denote the total biogas volumes (ml) in the

current and previous time intervals, CH,i and CH,i�1 denote the fraction of hydrogen gas (%) in

the current and previous time intervals, and VH denotes the total volume of headspace in the re-

actor (3200 ml).

H. Bacterial cell dry weight

The bacterial cell concentration was determined by measuring the optical density (OD) at

660 nm with spectrometer (Shimadzu UV-160 A). The OD values were then converted to cell

dry weight from the relationship calculated between cell dry weight (g/l) and OD 660 nm as

follows:

CDWPBUM001 ¼ 0:9674A660; (4)

where CDWPBUM001 denotes the cell dry weight of strain PBUM001 and A660 denotes the ab-

sorbance of culture at 660 nm.

III. RESULTS AND DISCUSSION

A. Stirred tank reactor for hydrogen production

Two set of experiments were run in STR (R1 and R2) under the optimized conditions

obtained from the previous study.17 The accumulated hydrogen gas produced, COD reduction,

and TKN were analyzed at regular interval. Figs. 2 and 3 showed results of accumulated hydro-

gen and COD reduction for the two runs of STR.

As shown in Fig. 2, about 2305.6 ml and 4359.83 ml of accumulated hydrogen produced in

R1 and R2 with COD reduction of 33.86% and 35.78%, respectively. The discrepancy of

hydrogen produced for both reactors may be due to the fact that some other factors that may

affect the photofermentation hydrogen production system. Since the growth rate of bacteria is a

function of both light intensity and substrate concentration, therefore the efficiency of light con-

version need to be considered which is on the light intensity and irradiated area. In addition,

self-shading of the bacterial cells and uniformly substrate diffusion may be also contributed to

the difference. The hydrogen gas production could also be defined as the hydrogen gas produc-

tion rate per culture volume, which was calculated by dividing total accumulated gas produced

FIG. 2. Cumulative hydrogen gas production (ml) for R1 and R2.

043105-5 Jamil et al. J. Renewable Sustainable Energy 4, 043105 (2012)

by the volume of the reactor within specific process time. Thus the hydrogen gas production

obtained were 0.66 ml H2/ml POME and 1.25 ml H2/ml POME in R1 and R2, respectively. The

predicted hydrogen gas production from the optimization study was 1.05 ml H2/ml POME with

31.7% of COD reduction.17

t-test was conducted to analyze the hydrogen gas production and COD reduction mean data

obtained from the STR (R1 and R2) studies against the predicted value. The mean cumulative

hydrogen gas production obtained from R1 and R2 was 0.955 ml H2/ml POME with 34.82%

COD reduction. Table II shows the P-values for both hydrogen gas production and COD reduc-

tion are more than 0.05 (P> 0.05). The smaller the magnitude of t-value and the higher P-value

indicated that difference in experimental values was not significant. Thus, the results obtained

in R1 and R2 agreed well with the predicted value in the optimization studies.

Fig. 4 shows the concentration level of ammonia at regular time interval. As the STR was

sparged using N2 gas at the beginning of the run, the concentration level of ammonia was

observed to increase in the first 12 h caused by nitrogen fixation by R. palustris PBUM001. The

nitrogen fixation reaction is catalysed by nitrogenase enzyme. The increased amount of

ammonical-N for the first 12 h (Fig. 4) could have caused limited inhibition to nitrogenase activ-

ity thus resulting in low hydrogen gas production (Fig. 2). Hydrogen gas production by anoxy-

genic phototrophic bacteria can be inhibited by NH4þ because it represses the synthesis of key

enzyme nitrogenase.27 The inhibition is reversible and nitrogenase recovers its activity once am-

monium is consumed as nitrogen sources in the cultivation of purple non sulfur bacteria.28

The observed results agreed with the previous study by Zhu et al.27 where high production

of hydrogen gas by Rhodobacter sphaeroides in the presence of NH4þ was delayed at the initial

period of fermentation and the average delay for hydrogen gas production was about 8 h. The

extent of delay was directly proportional to the concentration of NH4þ. Since suspended culture

FIG. 3. COD reduction with simultaneous hydrogen gas production for R1 and R2.

TABLE II. t-test results for hydrogen gas production and COD reduction data from STR against optimization model pre-

dicted value.

STR Data Experimentala Model Std. dev SE mean 95% CI t-value P-value

R1þR2 H2 0.955 ml

H2/ml

POME

1.05 ml

H2/ml

POME

0.417 0.295 (�2.793, 4.703) �0.32 0.802

COD

reduction

34.82% 31.71% 1.3577 0.96 (22.6220, 47.0180) 3.25 0.190

aData presented is the mean experimental results of R1 and R2.

043105-6 Jamil et al. J. Renewable Sustainable Energy 4, 043105 (2012)

of anoxygenic phototrophic bacterium is a relatively simple culture system, it can be applied to

the hydrogen gas production from wastewater when the NH4þ concentration is lower than

5 mM.27

B. Kinetic modeling

Mathematical modeling was used to describe the key processes in the 5-l STR. The results

of kinetic modeling were used to describe the relationship among the principle state variables

and to explain the behavior of photofermentation process for hydrogen production by R. palust-ris PBUM001. The modified Leudeking-Piret model suggested by Wang et al.29 was adapted in

this study and POLYMATHVR

6.0 software was used to generate the model. The suitability of the

proposed model in simulating hydrogen production from POME by R. palustris PBUM001 was

evaluated by comparing simulation results with experimental data. The discrepancy between the

proposed model and the experimental data in simulating hydrogen production from POME by

R. palustris PBUM001 was measured by using residual sum of squares (RSS). A small RSS

indicates tight fit of the model to the experimental data. The discrepancy is quantified in terms

of sum square as follows:

RSSn

i¼1¼ Rðyi � yiÞ

2; (5)

where yi is the experimental value and yi is the model value.

All the kinetic parameters evaluated and used in this study were summarized and compared

with literature values (Table III).

1. Growth kinetics

Theoretically, the cell growth rate for microbial growth is expressed as follows:19,30

dX

dt¼ l� X; (6)

where X denotes the bacterial cell dry weight concentration (gl�1), t denotes time (h), and ldenotes the specific growth rate (h�1).

Equation (6) is valid for the exponential growth phase of the bacteria. Prior to this phase,

the cells adapt themselves to their new environment. After the adaptation period, cells should

multiply rapidly, and cell mass and cell number density should increase exponentially with

FIG. 4. Ammonical-N (mg/l) during the photofermentation.

043105-7 Jamil et al. J. Renewable Sustainable Energy 4, 043105 (2012)

time. This is a period of balanced growth in which all components of the cell grow at the same

rate. The average composition of a single cell remains approximately constant during this phase

of growth. During balanced growth, the specific growth rate determined from either cell number

or cell mass would be similar. Since the nutrient concentrations are large in this phase, the

growth rate is assumed not to be limited by the nutrient concentration, hence the exponential

growth rate could be expressed as first order.

l depends on substrate concentration and possibly other factors. Several models provide an

expression for l; however, the Monod expression being the most common used in the studies.

In this study, the growth curves obtained in this study for R. palustris PBUM001 could not be

fitted well to the Monod model. As reported in the previous studies,19,20,30 l did not obey

Monod, Tessier, or Contois models for the growth of hydrogen-producing bacteria. Instead, two

approaches were used to characterize the PBUM001 growth curves in the present study namely

exponential and logistic models.

2. Exponential model for bacterial growth

The first approach was based on the assumption of a constant growth rate in the exponen-

tial phase. Rearranging Eq. (6) by integration gives the equation for the specific growth rate (l)

for a definite time interval and formed the following equation:

lmax ¼lnðX2=X1Þðt2 � t1Þ

: (7)

In Eq. (7), lmax is the specific growth rate in the exponential phase, X2 and X1 are the two dis-

tinct bacterial cell dry weight concentrations in the exponential phase (gl�1), and t2 and t1 are

the time at two distinct cell concentration, respectively (h).

In this study, the values of lmax calculated for both STR runs, R1 and R2 were 0.19 and

0.17 (h�1), respectively. In a study carried out by Eroglu et al.19 using R. sphaeroides O.U.001,

lmax obtained for various dilutions of olive oil mill wastewater (OMW) range between 0.012

and 0.043 (h�1) and the lmax values of the concentrated OMW media are lower than the diluted

OMW. The present study obtained higher lmax values compared to Eroglu et al.19 that explain

the possible reason(s) underlying the difference.

3. Logistic model for bacterial growth

By using logistic model as a second approach, it is possible to model bacterial growth at

exponential phase and lag phase together with stationary phase. For hydrogen production

experiments, bacterial growth data were found to fit logistic model. Specific growth rate for the

logistic model was defined as follows:

l ¼ lmaxð1� X=XmaxÞ; (8)

where Xmax denotes the maximum bacterial cell dry weight concentration (gl�1).

TABLE III. Model parameters established in this study and found from literature.

Model parameter Units Value established in this study Value found from literature Reference for data source

lmax h�1 0.17–0.19 0.1117–0.3963 He et al.21

Xo gl�1 0.155 0.018–0.068 Eroglu et al.19

0.079–0.142 Koku et al.28

Xmax gl�1 1.63 0.63–1.00 Koku et al.28

Yx/s gg�1 0.2 3.7 Eroglu et al.19

Yp/s lg�1 0.1 0.492 Eroglu et al.19

lmax h�1 – 0.009 Eroglu et al.31

043105-8 Jamil et al. J. Renewable Sustainable Energy 4, 043105 (2012)

By inserting Eq. (8) into Eq. (6) and integrating, the expression for X is obtained as

follows:

X ¼ Xmax

1þ fexp½�ðlmax � tÞ� � ðXmax=Xo � 1Þg ; (9)

where Xo denotes the initial bacterial cell dry weight concentration after inoculation in STR

(gl�1).

The predictive power of Eq. (9) may be limited since it does not contain a substrate term.

However, for the purposes of batch hydrogen production experiment in this study, the initial

substrate concentration and the inoculation volume are kept constant. Thus, the logistic model

is a fair approximation of the microbial growth curve.

Fig. 5 shows the relationship between the bacterial cell dry weight and time for STR runs

(R1 and R2). The logistic model predicted curve was also compared to the experimental data

from R1 and R2 (Fig. 5). The model described well the experimental data of the cell dry weight

measured for these STR runs. Correspondingly, the logistic model parameters of lmax and Xmax

were estimated as shown in Table II.

The RSS value shows a combination of all the individual experimental data errors as a

measure of the goodness of the fitting. The value of 0 denotes the maximum agreement of the

experimental data to calculated data. It is shown that the RSS values for this logistic model for

R1 and R2 fits were 0.3039 and 0.2313, respectively. This indicates the selected logistic model

described well the experimental data of the cell dry weight measured for these two reactors.

C. Substrate consumption and product formation kinetics

In this study, the substrate consumption was measured by COD reduction of POME with

hydrogen gas as one of the fermentation product formation. The differential equations describ-

ing substrate consumption by cell growth is expressed as follows:24

dCCOD

dt¼ � l� X

YXCOD; (10)

where CCOD denotes the COD consumption (gl�1) and YXCOD denotes the yield coefficient for

biomass on COD used (gg�1) which can be obtained from the following equation:

YXCOD ¼dX

dt� 1

ðdCCOD=dtÞ ; (11)

FIG. 5. The bacterial growth of R1 and R2 and the fitted curves based on the logistic model.

043105-9 Jamil et al. J. Renewable Sustainable Energy 4, 043105 (2012)

Fig. 6 shows COD reduction in batch culture for 66 h. The fitted equation was compared

with experimental data of STR runs as shown in Fig. 6. The model parameters are shown in

Table II. The RSS values for the fits were 19.1319 and 26.8259 for R1 and R2, respectively.

This indicates the model is accepted and consistent with the experimental data of substrate con-

sumption that was measured by COD reduction. It should be noted that an acceptable agree-

ment was obtained between the model prediction and the experimental data for both bioreactor

runs R1 and R2.

The kinetic expression for product formation (hydrogen) is based on the Leudeking-Piret

equation. According to this model, product formation depends on both growth rate and instanta-

neous biomass concentration in a linear manner.31

dCH2

dt¼ YH2X �

dX

dt

� �þ ðlPX � XÞ; (12)

where, CH2 is the cumulative hydrogen (l), lPX is the specific formation rate of hydrogen (h�1),

and YH2X is the yield coefficient for hydrogen production due to cell growth (lg�1) which can

be obtained from the following equation:

YH2X ¼dP

dt� 1

ðdX=dtÞ : (13)

Fig. 7 illustrated the relationship between cumulative hydrogen production with time in R1

and R2, respectively. The fitted equation is compared with experimental data of R1 and R2.

The logistic bacterial cell growth and substrate consumption models shown in Figures 5

and 6, respectively, were fitted reasonably well with the experimental data obtained. This was

not surprising since bacterial growth and substrate consumption phenomenon had well estab-

lished and well proven type of kinetics. The product formation model fitted reasonably well

with the experimental data for R1 compared to R2 as shown in Fig. 7, however it is acceptable.

The RSS values of R1 and R2 were 1.3267 and 26.3741, respectively. Relatively, metabolic

regulation on photofermentative hydrogen evolution is somewhat complex and it is often diffi-

cult to get a clear picture of the entire fermentation process.

The fact that some other factors that may affect the photofermentation hydrogen production

system. Since the growth rate of bacteria is a function of both light intensity and substrate con-

centration, therefore models relating these three factors need to be considered to improve the

model predictive capabilities.32 Hence the kinetic models used to describe the dynamics of cell

growth and hydrogen evolution have to take into account the dependence of growth and

FIG. 6. The model predicted and experimental substrate consumption by R. palustris PBUM001 in R1 and R2.

043105-10 Jamil et al. J. Renewable Sustainable Energy 4, 043105 (2012)

hydrogen formation on substrate concentration and light intensity as well as the inhibitory

effects of substrate, biomass, and light intensity. Thus, the un-structured models applied in this

study were able to predict bacterial cell growth and substrate consumption. The agreement

between the empirical assessment using modified Leudeking-Piret model and the variability of

the experimental data of hydrogen production can be seen to be reasonable. This degree of

agreement may be acceptable for preliminary evaluations and experimental design for further

understanding of the metabolic regulation of hydrogen production to describe the variations.

IV. CONCLUSIONS

The kinetic models used in this study described reasonably well the experimental data of

bacterial cell growth, substrate consumption, and hydrogen formation. It is proposed that the

models incorporate other inherent factors such as light intensity in the model development for

photofermentation hydrogen production for further improvement of photofermentation of

POME to produce hydrogen gas by R. palustris PBUM001. More engineering approach should

endeavor to develop this study into a real practical production of microbial photofermentative

biohydrogen.

ACKNOWLEDGMENTS

This work was by a grant from University of Malaya and Malaysian Genomic Institute (PPP,

P0216/2006A, and 5302031013).

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