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Optimization of Dilute Acid Hydrolysis and Enzymatic Saccharification for Dark Fermentative Hydrogen Production

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Optimization of dilute acid hydrolysis and enzymatic saccharification for dark fermentative hydrogen production from oil palm empty fruit bunch
Abstract
Pretreatment of oil palm empty fruit brunch (EFB) was investigated for H2 fermentation. EFB pellet was hydrolyzed at various temperatures, H2SO4 concentrations, and reaction times, at a fixed biomass-to-acid solution ratio. Then the acid-hydrolyzed biomass underwent enzymatic saccharification under various temperatures, pH, and enzyme loading. Response surface methodology derived the optimum sugar concentration (SC), hydrogen yield (HY), and hydrogen production rate (HPR) for dilute acid as 28.30 g L-1, 275.75 mL H2 g-1 total sugar (TS), and 2601.24 mL HL-1d-1, respectively, at 120°C, 60 min reaction, and 6 v/v % H2SO4. Following enzymatic saccharification enhanced SC, HY, and HPR to 33.99 g L-1, 282.17 mL H2 g-1 TS, and 3174.59 mL HL-1d-1, respectively, at 47.63°C, pH of 5.45, and 1.0 mg protein mL-1. Dilute acid hydrolysis would be a viable pretreatment for biohydrogen production from EFB. Following enzymatic saccharification would be considered if the enhancement of HPR is required.
Keywords
biohydrogen; lignocellulosic biomass; dilute acid hydrolysis; enzyme saccharification; response surface methodology
1. Introduction
Lignocellulosic biomass derived from crops, wood, and agricultural residues is currently recognized as a cost-effective and environment-friendly alternative energy resource, as compared to fossil fuels, which are non-renewable and contributory to greenhouse gas emissions and global warming [1, 2]. The use of lignocellulosic biomass for bionergy production could not only solve the worldwide problem of depletion of fossil fuels, but also reduce dependency on fossil fuels, greenhouse gas emissions, and problems with waste management and pollution. This type of biomass includes typical byproducts of agricultural and industrial processes, thus abundance and renewability are not detrimental to its use as feedstock for production of biofuels, such as biogas, biomethane, syngas, bioethanol, and biodiesel [3].
Lignocellulosic biomass materials are composed of a complex matrix structure of cellulose, hemicellulose, lignin, and other components [4, 5]. The complex matrix structure of lignocellulosic biomass results to its recalcitrant property, which will require pretreatment processes to break the polysaccharide-lignin complex linkages, to be able to access and utilize the cellulose and hemicellulose components for biofuel production [6].
Dilute acid hydrolysis is one of the most widely performed pretreatment methods for lignocellulosic biomass [4]. It is known to be a relatively cost-effective and environmental-friendly method, compared to concentrated acid hydrolysis, which would still subject the biomass under the same harsh physicochemical conditions. A solution rich with monomeric sugars is hoped to be obtained after the dilute acid hydrolysis, during which lignin-carbohydrate linkages are disrupted. Dilute acid hydrolysis is controlled by a number of factors, including temperature, time, acid concentration, and solid/liquid (S/L) ratio. These factors contribute to the combined severity factor (CSF), which is a representative parameter for the strength or harshness of the hydrolysis process [7]. Higher combined CSF values indicate harsher hydrolysis conditions, which may also lead to not only higher pretreatment cost, but also, further degradation of monomeric sugars into degradation byproducts, such as 5-hydroxymethylfuraldehyde, furaldehyde, formic acid, and levulinic acid [8, 9]. To further break down the complex structure of the lignocellulosic biomass, enzyme saccharification can be performed after the dilute acid hydrolysis. The combined dilute acid hydrolysis and enzyme saccharification pretreatment method would be an efficient way to obtain fermentable monomeric sugars from lignocellulosic biomass, as shown by previous studies [10, 11]. This combined pretreatment method, however, is not usually employed for dark H2 fermentation studies. Furthermore, optimization of the saccharification parameters remains to be an important issue, since no fixed set of pretreatment conditions can be used for all types of lignocellulosic biomass. Based on the biomass used, the following factors may significantly affect enzymatic pretreatment: temperature, pH, and enzyme loading.
Hydrogen (H2) is an environmentally-friendly and energy-efficient biofuel, whose interest on has significantly increased in recent years. Dark H2 fermentation from a variety of biomass has been performed widely with the use of pure microorganism strains or mixed microbial consortiums. Optimization of the fermentation process is highly important to achieve maximum production performance at a minimal cost. This entails enhancement of the pretreatment process, during which fermentable monomeric sugars are obtained for utilization during fermentation.
Response surface methodology (RSM) is a set of mathematical and statistical methods which can be used to design experiments, build models, and solve problems on evaluation of significance of independent variables and optimization of a response influenced by several variables. RSM can be applied to optimize the process by developing a model which relates a process response to various factors. RSM is able to determine the optimal process and identify the limit of a response based on the set experimental conditions [12, 13]. In this method, the interactions of the variables with each other and the consequent effects can be observed to be able to determine optimization point. Furthermore, the variables are combined with one another to deduce the optimization point even outside of recorded experimental data [14, 15].
RSM has been performed previously in a number of H2 fermentation optimization studies [13-20]; however, RSM was not employed for combined dilute acid hydrolysis and enzymatic saccharification, which were performed and investigated in this study. Furthermore, there is a limited number of previous studies on enzymatic hydrolysis as a pretreatment process for Hfermentation, since it is mostly used only for bioethanol and biomethane [21] production.
In this study, central composite design (CCD) based on RSM was performed to determine the optimal operating conditions for both dilute acid hydrolysis and enzymatic saccharification on monomeric sugar yield and subsequent H2 fermentation. Three control variables of dilute acid hydrolysis, hydrolysis temperature, sulfuric acid concentration, and reaction time, were first optimized for sugar concentration and biohydrogen production from oil palm empty fruit bunch (EFB). Another set of control variables for enzymatic saccharification, temperature, pH, and enzyme loading, was then optimized for sugar concentration and biohydrogen production.
 
2. Materials and Methods
 
2.1. Lignocellulosic biomass
EFB pellets were used as the test biomass in this study. The biomass samples were obtained from local agricultural sources in Malaysia. The contents of the lignocellulosic biomass were measured by following the NREL laboratory analytical procedure [22]. The cellulose, hemicellulose, lignin, and other contents of the biomass samples are presented, on dry basis, in Table 1.
2.2. Sludge
The inoculum in this study was an anaerobic granular sludge obtained from a brewery wastewater-treating upflow anaerobic sludge blanket reactor (UASB) in South Korea. The anaerobic granular sludge is known to contain more strains of H2­producing bacteria [23], mostly Clostridium [23]. The pH, volatile suspended solids (VSS), and total suspended solids (TSS) concentration of the sludge were 6.8, 12.6 g L-1, and 22.6 g L-1, respectively. The sludge was heat-treated at 90oC for 30 min[24] before it was used as the inoculum of the following batch H2 fermentation to ensure only the presence of anaerobic spore-forming H2-producing bacteria.
2.3. Response surface methodology
A full factorial, central composite design for three factors with replicates on center and axial center points were used in this study, as tabulated in Table 2. A total of 20 experimental trials, which included 8 trials for cubic points, 6 for axial points, and 6 replicates for center point, were performed. This was done to determine the individual and synergistic effects of the three variables tested on the efficiency of both dilute acid hydrolysis and enzymatic saccharification.
RSM was employed in this study. The full factorial central composite experiment design was shown in Table 2. The variables were coded according to Eq. 1 [14]:
xi= xi-xi*∆xi
(1)
where xi, Xi, X*i, and ΔXi are the coded value of the ith independent variable, the uncoded value of the ith independent variable, the uncoded value of the ith independent variable at the center point of the investigated area, and the step size, respectively.
2.4. Dilute acid hydrolysis of lignocellulosic biomass
The EFB pellets were initially milled to reduce particle size to 1-2 mm. Temperature (X1), H2SO4 concentration (X2), and reaction time (X3) were chosen as the three independent factors in the experimental design. The central values were selected as 100°C, 5% H2SO4, and 45 min reaction time, for X1, X2, and X3, respectively. Dilute acid pretreatment was conducted for samples containing 5% milled biomass sample in acid solution, containing 4, 5, and 6% H2SO4 (Duksan Pure Chemicals, Korea). The process was carried out in an autoclave (SK401, Yamato Scientific Co., Ltd.) at 80, 100, and 120°C for 30, 45, and 60 min reaction times.
2.5. Enzymatic saccharification of lignocellulosic biomass
After determination of the optimal dilute acid hydrolysis conditions, these conditions were applied prior to enzymatic saccharification. The EFB residue from the dilute acid hydrolysis was first washed and dried prior to the use as the substrate for enzymatic saccharification. Cellulase (Celluclast 1.5L®, Sigma-Aldrich, Merck, MO, USA), derived from Trichoderma reesei ATTC 26921, was chosen as the enzyme. The average activity and protein concentration  of the thermophilic enzyme [25] are 65 filter paper unit (FPU)/mL and 79 mg protein mL­1, respectively [26]. Saccharification temperature (X4), saccharification pH (X5), and enzyme loading (X6) were chosen as the three independent factors in the experimental design. The central values were selected as 45°C, 5, and 0.75 mg protein mL­1, for X4, X5, and X6, respectively.
2.5. Batch H2 fermentation
100-mL serum bottles were used as batch reactors for H2 fermentation. 6 mL of the hydrolyzate solution and 24 mL distilled water were added to the serum bottle. Mineral medium was supplied in a solution containing the following: 6.72 g L-1 NaHCO3, 3.00 g L-1 NH4CO3, 0.125 g L-1 KH2PO4, 0.100 g L-1 MgCl2.6H2O, 0.015 g L-1 MnSO4.6H2O, 0.025 g L-1 FeSO4.7H2O, 0.005 g L-1 CuSO4.5H2O, and 0.001 g L-1 CoCl2.5H2O. 5 mL of seed inoculum and 5 mL mineral medium solution were added to obtain a solution with total working volume of 40 mL and initial pH of 7.0-7.5. The serum bottle was purged with N2 gas for 3 min and then agitated at 150 rpm and 35oC. The fermentation process was performed in duplicate.
2.6. Other analytical methods
The glucose and xylose concentrations of the hydrolyzate were quantified using high performance liquid chromatography (Waters 717, USA) with Aminex HPX-87P column (Bio-Rad Laboratories, USA) and a refractive index detector (Waters 410, USA). Total monomeric sugar content was quantified using phenol-sulfuric acid colorimetric method, which was measured with an ultraviolet spectrophotometer at 480 nm (Shimadzu UV-Vis Mini 1240, Japan). The organic acids, furaldehyde, and 5-hydroxymethylfuraldehyde (5-HMF) content were measured by HPLC with Aminex-87H (Bio-Rad Laboratories, USA) and an ultraviolet detector (Waters 2487, USA) at 280 and 210 nm, respectively. Filter paper activity of the enzyme was measured according to the standard procedure by the Commission on Biotechnology of IUPAC [27] and expressed in filter paper units (FPU). One FPU of enzyme was defined as the amount of enzyme capable to produce 1 μmol of reducing sugars in 1 min [27]. Chemical oxygen demand (COD) and volatile suspended solids (VSS) are measured using standard methods [28].
2.7. Assay
The efficiency of dilute acid hydrolysis was calculated from the hydrolyzate monomeric sugar concentration divided by the theoretical sugar concentration [14]. The combined severity factor (CSF) is the quantification of the degree of harshness of the pretreatment conditions. It is obtained using Eq. 2 [7]:
SF= log⁡t x eTh-Tr14.75- pH
(2)
where t, Th, and Tr are the reaction time (in min), hydrolysis temperature (in°C) and reference temperature (100°C), respectively.
H2 production with respect to time was fitted to the values estimated by a modified Gompertz equation (Eq. 1) to estimate maximum H2 volume, maximum H2 production rate, lag time of H2 production, and H2 production yield[29, 30]:
H=P ×exp⁡-exp⁡RHP × λ-t+1
(3)
where H, P, RH, λ, and t are cumulative H2 production (mL), ultimate H2 production (mL), H2 production rate (mL h-1), lag time (h), and time (h), respectively.
The responses of the control variables on dilute acid hydrolysis and enzymatic saccharification were correlated using this general quadratic equation [19, 31]:
Y= βo+ ∑i=1kβiXi+ ∑i=1kβiiXi2 + ∑i<jβijXiXj
(4)
where Y, βo, βi, βii, and βij are the response, y-intercept, linear coefficient, quadratic coefficient, and the interactive coefficient, respectively.
3. Results and Discussion
 
3.1. Optimization of dilute acid hydrolysis
Cumulative H2 production curves obtained during the batch H2 fermentation were described using the modified Gompertz equation (Eq. 3). The volumetric hydrogen production rate (HPR, L HL-1 hr-1) and hydrogen yield (HY, mL H2 g biomass-1) were calculated from the hydrogen production rate (RH), cumulative H2 production (P), reactor volume, and amount of monomeric sugar substrate. Efficiency of the dilute acid hydrolysis process at the various temperatures, reaction times, and acid concentrations are summarized in Table 3. Regression equations for sugar concentration (SC, g L-1), HPR, and HY are shown in Eq. 5 to 7.
SC= -6.90514+0.040879X1-0.216X2-1.02892X3+0.00205833X1X2+0.03X1X3+0.405X2X3-0.000758209X12-0.000687962X22+0.26417X32 (p-value=0.0016;F-value=41.17;R2=0.9432)
(5)
HY= -128.76182+4.76224X1-1.9483X2+11.89163X3+0.020258X1X2+0.3X1X3+0.38017X2X3-0.011591X12-0.010039X22+0.15247X32 (p-value=0.0083;F-value=17.15;R2=0.9549)
(6)
HPR= -130.79757+27.69553X1-55.30703X2-136.05464X3+0.42166X1X2-3.76713X1X3+6.905X2X3+ 0.00796368X12-0.053386X22+23.26209X32 (p-value=0.0002;F-value=187.11;R2=0.9417)
(7)
High regression coefficient values (R2) over 0.94 were obtained, suggesting that the regression model represented the experimental data well. The ANOVA results showed that the computed F-values (41.17, 17.18, and 187.11 for SC, HY, and HPR, respectively) are much greater than the tabular F-value (2.76) at 5% level. More so, low P values (SC: 0.0016; HY: 0.0083; HPR: 0.002) computed in this study indicate that the regression was statistically significant.
Two-dimensional contour plots which model the relative effects of two variables when another variable is held constant are shown in Figs. 1 to 3. The contour plots show that temperature, reaction time, and dilute acid concentration all have significant individual influence on the sugar concentration during dilute acid hydrolysis, which, in turn, influenced H2 production as well. This further validates that all three factors take part in the combined severity of the dilute acid pretreatment, as shown in Eq. 2.
The CSF of each experimental condition was calculated and tabulated in Table 2. Increasing the severity factor beyond the optimal range could decrease sugar recovery due to further degradation of sugar [32]. Park et al. reported that, at CSF values exceeding 2.5, amount of sugars decreased while concentrations of VFA, such as levulinic, formic, and acetic acids, increased [14]. The severity factors of all the dilute acid pretreatment conditions were below the value in this study.
The highest SC of 28.30 g L-1 was obtained at the following conditions: 120°C, 60 min reaction, and 6 v/v % H2SO4, where the CSF was 2.09. At this optimum condition, degradation byproducts, such as formic acid, acetic acid, levulinic acid, furaldehyde, and 5-HMF, were still formed at low amounts. As seen in Table 2, the CSF value at the optimal condition is within the range of 1.7 to 2.2, at which maximum sugar concentration can be obtained for dilute acid pretreatment [33]. Temperature, reaction time, and pH all contribute to the severity of the dilute acid hydrolysis, thus at extremely high values of these parameters, partial degradation occurs and the formation of the degradation byproducts heightened, which proved to be detrimental to sugar concentration and the subsequent H2 production. It can be definitely noticed that SC dramatically dropped while the concentrations of the byproducts increased at higher CSF values. These degradation byproducts are known to be inhibitory for fermentative microorganisms, thus, must be removed from the hydrolyzate using any detoxification method, such as adsorption and overliming, prior to use in fermentation to diminish the inhibitory effects. Table 3 shows the concentrations of soluble metabolic products (SMPs) such as volatile fatty acids (VFA), which include acetic acid, formic acid, propionic acid, and butyric acid, and the furan aldehydes, such as furaldehyde and 5-hydroxymethylfuraldehyde (5-HMF). These SMPs are known to be degradation byproducts of monomeric sugars. High concentrations of these SMPs indicate that the monomeric sugars from the biomass were liberated from the complex lignocellulosic structure; however, the severity of the pretreatment has caused the sugars to be further degraded. Moreover, these SMPs, especially the furan aldehydes, are known inhibitors to the activity of H2-producing microorganisms [34]. It can be seen from Table 3, however, that all the runs have exhibited low values for the presence of these SMPs thus, degradation did not occur greatly during the pretreatment and we expect the H2 fermentation not to be greatly affected by these compounds in this study.
It is noticeable that the hydrogen production using the dilute acid hydrolyzates are directly correlated to sugar concentration, such that the optimum values of HPR (2601.24 mL HL-1d-1) and HY (275.75 mL H2 g-1 biomass) were achieved at the same conditions at which optimum sugar concentration was achieved. This can be explained by the fact that the hydrolyzate solutions contained the highest amount of soluble monomeric sugars, and the concentrations of the SMPs in the hydrolyzate are not high due to the tolerable severity of the pretreatment process, as shown in Tables 2 and 3.
3.2. Optimization of enzyme saccharification
The dilute acid hydrolysate, produced at 120°C and 6 v/v % H2SO4 for 60 min, was further treated by enzyme saccharification at various temperatures, pH values, and enzyme loading. Table 4 summarizes the effects of the additional enzyme saccharificatin on sugar recovery and subsequent hydrogen production. RSM was also implemented to evaluate the influence of each control variable on the efficiency of the enzymatic pretreatment process. Regression equations for SC, HPR, and HY are shown in Eq. 8 to 10.
SC= -43.02048+1.64066X4+10.79865X5+22.91954X6+0.022083X4X5-0.052X4X6+0.855X5X6-0.018102X42-1.15084X52-16.51955X62 (p-value=0.2422;F-value=76.39;R2=0.9871)
(8)
HY= -233.67382+11.18471X4+63.57150X5+194.78598X6-0.00354167X4X5+0.277X4X6+11.9975X5X6-0.118561X42-6.5667X52-183.86874X62 (p-value=0.5376;F-value=62.25;R2=0.9842)
(9)
HPR= -7867.85629+234.09827X4+1420.78592X5+4410.21318X6+1.98771X4X5-1.64433X4X6+152.5525X5X6-2.57045X42-150.85906X52-3426.13293X62 (p-value=0.3574;F-value=88.73;R2=0.9889)
(10)
Similar to the RSM analysis of the dilute acid hydrolysis, ANOVA results show that the computed F-values (SC: 76.39; HY: 62.25; HPR: 88.73) are much greater than the tabular F-value (2.76) at 5% level. More so, low P values (SC: 0.2422; HY: 0.5376; HPR: 0.3574) computed in this study indicate that the regression was statistically significant. Furthermore, the high regression coefficient values all exceeded 0.98, suggesting that the regression analysis was suitable for the experimental data.
Two-dimensional contour plots, which model the relative effects of two variables when another variable is held constant, are shown in Figs. 4 to 6. Among the three independent variables, it can be said that the pH and temperature conditions should strictly be close to the optimal conditions, due to the biological activity of the enzyme. Huge deviation from the optimal conditions would definitely affect the saccharification process. Enzyme loading, on the other hand, has a very direct relationship with sugar concentration, such that, as the enzyme loading increased, the sugar concentration likewise increased. Taking note of the concentrations of the SMPs in the enzymatic hydrolyzates, all the conditions were apparently not harsh enough to be able to degrade monomeric sugars, thus low concentrations of these compounds were observed.
Based from the experimental design runs, the highest SC of 34.52 g L-1 was obtained at the following conditions: 45°C, pH 5.0, and 1.17 mg protein mL-1 enzyme loading. Similar to the results of the dilute acid hydrolysis, the condition at which the maximum SC value was observed also corresponded to the best H2 production, among the experimental runs. However, the analysis of the experimental design within the range set of the independent variables revealed that while only singular values of both pH and temperature are suitable for the enzyme, enzyme loading plays a linear relationship with SC and H2 production. Maximizing the enzyme loading within the set range of 0.50 to 1.0 mg protein mL-1, while maintaining the suitable pH and temperature conditions, was predicted to be able to harness the maximum amount of monomeric sugars, leading to equally better H2 fermentation performance. At 47.63°C, pH of 5.45, and 1.0 mg protein mL-1 enzyme loading, 33.99 g L-1, 282.17 mL H2 g-1 TS, and 3174.59 mL HL-1d-1, respectively, were found to be the highest values of SC, HY, and HPR.
Compared to the single dilute-acid hydrolysis, the subsequent enzyme saccharification enhanced sugar concentration of dilute acid by 20%. The increased easily accessible substrates would result in the enhancement of HPR by 22%. However, HY was only increased by 2%. It would be related to that the cellulose activity of Hproducers including Clostridium species [35-38]. The use of the additional saccharification should be considered if the operational or economic feasibility of a H2 production process for EFB is limited by the productivity, not the yield.
3.3. Overall performance of the optimal conditions
During the H2 fermentation, the monomeric sugars in the hydrolyzate solutions were utilized completely and depleted at approximately 60 h, following a lag phase, during which bacterial growth occurs after introduction in the fermentative environment [18]. During the whole dark fermentation process, no methane production was observed to occur because of the thermal treatment of the sludge, which inhibited the activity of methanogens while, at the same time, enriched the H2-producing bacteria [39].
The production of VFA during the maximum H2 fermentation production is shown in Fig. 7. Alongside H2, VFA such as acetate, butyrate, and propionate, were also produced as SMPs of the dark fermentation process. These VFAs were commonly observed to form during fermentation using xylose and glucose as feed at pH 5.5. Among these VFAs, acetate and butyrate were the main SMPs, suggesting that the main route of H2 production was that of acetate and butyrate [40], which is typical for most Clostridium species [34] which are known to be H2-producing bacteria. Lactate, which is an indicator of the presence of non-H2-producing bacteria [23], was noted to be not formed during the entire fermentation process.
Table 5 shows that the peak HY and HPR from this study was in the range of reported maximum values for H2 production from lignocellosic biomass. Dilute-acid hydrolyzed EFB would be a promising feedstock for biohydrogen production. Following enzymatic saccharification would further enhance the feasibility by increasing HPR.
 
4. Conclusion
A central composite design was implemented to investigate the effects of two sets of independent variables on the sugar concentration and fermentative H2 production during dilute acid and enzymatic pretreatments of EFB, a lignocellulosic biomass sample. Response surface methodology, model building, and regression analysis were also carried out to optimize the conditions for biohydrogen production from EFB, a lignocellulosic biomass.  The following conclusions were obtained:

  1. Hydrolysis temperature, reaction time, and H2SO4 concentration all have individual and interactive significant influence on the sugar concentration and hydrogen production from dilute acid hydrolyzates of EFB.
  2. Sugar concentration and hydrogen production were found to be related for both dilute acid and enzymatic pretreatments, such that optimal conditions for both pretreatment process provided the highest sugar concentration and hydrogen production.
  3. The optimum SC, HY, and HPR using dilute acid hydrolyzates were 28.30 g L-1, 275.75 mL H2 g-1 biomass, and 2601.24 mL HL-1d-1, respectively, at 120°C, 60 min reaction, and 6 v/v % H2SO4.
  4. For enzymatic hydrolyzates, maximum values of 33.99 g L-1, 282.17 mL H2 g-1 TS, and 3174.59 mL HL-1d-1, respectively, were the optimal SC, HY, and HPR at 47.63°C, pH of 5.45, and 1.0 mg protein mL-1 enzyme loading.

These conclusions demonstrate that CCD and RSM are useful in optimization of the pretreatment of lignocellulosic biomass via dilute acid hydrolysis and enzymatic saccharification and the subsequent dark H2 fermentation process.
 
Table 1. Cellulose, hemicellulose, lignin, ash, and extractives content in the oil palm empty fruit bunch

Component Amount
Glucan 38.31 ± 1.01
% Xylan 11.09 ± 0.24
% Arabinan 0.13 ± 0.01
% Lignin 9.37 ± 0.03
% Ash 39.82 ± 2.05
% Extractives 1.43 ± 0.16
Table 2. Experimental design for full factorial central composite design

Run Code Values Real Values CSF
DILUTE ACID HYDROLYSIS
  X1 X2 X3 Temperature (°C) Time (min) Acid (% v/v)
1 0 0 0 100 45 5 1.12
2 -1 -1 -1 80 30 4 0.15
3 0 0 0 100 45 5 1.11
4 -1 1 1 80 60 6 0.93
5 -1 1 -1 80 60 4 0.41
6 1 -1 -1 120 30 4 1.25
7 1 1 1 120 60 6 2.09
8 0 0 0 100 45 5 1.22
9 -1 -1 1 80 30 6 0.65
10 0 0 0 100 45 5 1.16
11 1 1 -1 120 60 4 1.51
12 1 -1 1 120 30 6 1.75
13 1.68 0 0 133.64 45 5 2.15
14 0 0 1.68 100 45 6.68 1.47
15 0 0 0 100 45 5 1.16
16 -1.68 0 0 66.36 45 5 0.25
17 0 -1.68 0 100 19.77 5 0.85
18 0 0 -1.68 100 45 3.32 0.64
19 0 0 0 100 45 5 1.17
20 0 1.68 0 100 70.23 5 1.34
ENZYMATIC SACCHARIFICATION
X4 X5 X6 Temperature (°C) pH Enzyme 
(mg mL-1)
1 0 0 0 45 5 0.75
2 -1 -1 -1 30 3 0.50
3 0 0 0 45 5 0.75
4 -1 1 1 30 7 1.00
5 -1 1 -1 30 7 0.50
6 1 -1 -1 60 3 0.50
7 1 1 1 60 7 1.00
8 0 0 0 45 5 0.75
9 -1 -1 1 30 3 1.00
10 0 0 0 45 5 0.75
11 1 1 -1 60 7 0.50
12 1 -1 1 60 3 1.00
13 1.68 0 0 70.23 5 0.75
14 0 0 1.68 45 5 1.17
15 0 0 0 45 5 0.75
16 -1.68 0 0 19.77 5 0.75
17 0 -1.68 0 45 1.64 0.75
18 0 0 -1.68 45 5 0.33
19 0 0 0 45 5 0.75
20 0 1.68 0 45 8.36 0.75
Table 3. Sugar concentration, soluble metabolic products production, and hydrogen production from dilute acid hydrolysis of oil palm empty fruit bunch

Trial Temperature (°C) Time (min) Acid 
(% v/v)
Total VFAa 
(g L-1)
Furaldehyde 
(g L-1)
5-HMFb 
(g L-1)
Sugar concentration 
(g L-1)
H2 Yield (mL H2 
g-1 biomass)
H2 Production 
Rate (mL H2 L-1d-1)
1 100 45 5 5.62 3.53 1.31 24.67 251.63 2069.24
2 80 30 4 4.98 3.71 0.84 20.35 215.93 1464.73
3 100 45 5 5.07 4.03 0.82 20.63 218.27 1500.97
4 80 60 6 5.23 3.64 0.71 20.86 209.45 1456.38
5 80 60 4 4.76 3.44 0.58 19.83 203.41 1344.54
6 120 30 4 5.61 4.03 0.63 14.38 155.89 747.23
7 120 30 6 5.86 2.25 1.59 17.94 179.70 1074.61
8 100 45 5 4.71 4.17 0.79 26.10 266.31 2316.90
9 80 30 6 4.93 2.36 0.85 16.55 166.52 918.64
10 100 45 5 5.11 4.97 0.92 20.20 217.04 1461.40
11 120 60 4 5.28 5.63 1.01 17.95 181.11 1083.64
12 120 60 6 5.63 8.23 1.11 28.30 275.75 2601.24
13 133.64 45 5 7.18 6.82 1.48 19.83 214.33 1416.72
14 100 45 6.68 5.21 6.02 0.99 25.09 254.29 2126.71
15 100 45 5 4.56 5.78 0.91 15.30 175.47 894.90
16 66.36 45 5 5.29 4.24 0.85 20.20 211.81 1426.19
17 100 19.77 5 4.32 4.88 0.56 18.10 199.55 1203.95
18 100 45 3.32 3.77 2.91 0.60 22.35 231.82 1727.06
19 100 45 5 5.49 5.15 0.71 11.72 139.55 545.18
20 100 70.23 5 6.83 5.64 0.93 21.92 221.38 1617.55

aTotal VFA (after dilute acid hydrolysis) = acetate + butyrate + formate + propionate
b5-hydroxymethylfuraldehyde
Table 4. Sugar concentration, soluble metabolic products production, and hydrogen production from enzymatic saccharification of oil palm empty fruit bunch

Trial Temperature (°C) pH Enzyme 
(mg mL-1)
Total VFAa 
(g L-1)
Furaldehyde 
(g L-1)
5-HMFb 
(g L-1)
Sugar concentration 
(g L-1)
H2 Yield (mL H2 
g-1 biomass)
H2 Production 
Rate (mL HL-1d-1)
1 45 5 0.75 19.12 2.21 0.33 33.46 278.95 3111.22
2 30 3 0.50 8.16 1.84 0.21 22.18 202.16 1494.64
3 45 5 0.75 20.24 2.11 0.26 23.37 213.26 1661.30
4 30 7 1.00 6.52 1.96 0.37 22.81 214.32 1629.55
5 30 7 0.50 8.25 1.41 0.18 27.49 234.92 2152.65
6 60 3 0.50 5.12 1.76 NDc 20.93 176.98 1234.73
7 60 7 1.00 17.56 1.84 0.41 28.02 241.73 2257.76
8 45 5 0.75 19.76 2.16 0.25 33.73 278.30 3129.02
9 30 3 1.00 7.33 2.03 0.29 21.33 198.32 1410.06
10 45 5 0.75 19.81 2.09 0.36 34.16 271.23 3088.41
11 60 7 0.50 18.09 1.87 0.17 33.20 286.98 3175.91
12 60 3 1.00 7.81 1.65 0.19 20.11 213.06 1792.66
13 70.23 5 0.75 10.24 1.83 ND 24.52 231.17 1889.43
14 45 5 1.17 21.36 1.74 0.44 34.52 283.91 3266.86
15 45 5 0.75 20.17 2.09 037 30.56 246.88 2514.88
16 19.77 5 0.75 9.17 0.81 ND 17.29 183.32 1056.53
17 45 1.64 0.75 3.17 0.93 ND 15.48 169.53 1318.19
18 45 5 0.33 18.04 1.97 0.21 21.33 192.64 1369.67
19 45 5 0.75 19.12 2.23 0.38 32.77 274.13 2994.41
20 45 8.36 0.75 5.89 1.57 ND 21.45 211.19 2233.31

aTotal VFA (after enzymatic hydrolysis) = acetate + butyrate + formate + propionate
b5-hydroxymethylfuraldehyde
cNot detected
Table 5. Dark fermentative H2 production from various lignocellulosic biomass pretreated with dilute acid hydrolysis and/or enzyme saccharfication in previous studies

Substrate Pretreatment H2 Fermentation Conditions Inoculum H2 yield* H2 production rate Reference
Oat straw Sequential dilute acid and enzymatic hydrolysis Batch, 35oC, pH 7.0 Anaerobic sludge 113.75 mL H2 g-1 TS 710 mL H2 L-1 d-1 [47]
Oil palm empty fruit bunch Dilute acid hydrolysis Batch, 37oC, pH 7.0, 5 g TS/L Anaerobic sludge 165.88 mL H2 g-1 TS 1824 mL H2 L-1 d-1 [5]
Oil palm empty fruit bunch Dilute acid hydrolysis Batch, 35oC, pH 7.0, 5 g TS/L Anaerobic sludge 278.0 mL H2 g-1 TS 1551 mL H2 L-1 d-1 [46]
Pine tree wood Dilute acid hydrolysis Batch, 37oC, pH 7.0, 10 g TS/L Anaerobic sludge 191.18 mL H2 g-1 TS 2565 mL H2 L-1 d-1 [44]
Pine tree wood Dilute acid hydrolysis Batch, 37oC, pH 7.0, 10 g TS/L Anaerobic sludge 179.0 mL H2 g-1 TS 1629 mL H2 L-1 d-1 [5]
Poplar leaves Dilute acid hydrolysis Batch, 35oC, pH 7.0 Mixed culture 44.9 mL H2 g-1 TS 1611 mL H2 L-1 d-1 [43]
Rice husk Dilute acid hydrolysis Batch, 37oC, pH 7.0, 5 g TS/L Anaerobic sludge 251.63 mL H2 g-1 TS 2608 mL H2 L-1 d-1 [42]
Rice husk Dilute acid hydrolysis Batch, 37oC, pH 7.0, 10 g TS/L Anaerobic sludge 275.53 mL H2 g-1 TS 1510 mL H2 L-1 d-1 [44]
Rice husk Sequential dilute acid and enzymatic hydrolysis Batch, 37oC, pH 7.0, 10 g TS/L Anaerobic sludge 473.1 mL H2 g-1 TS 3340 mL H2 L-1 d-1 [10]
Wheat Dilute acid hydrolysis Batch, 55oC, pH 7.0, 20 g TS/L Anaerobic sludge 178 mL H2 g-1 TS 1840 mL H2 L-1 d-1 [45]
Oil palm empty fruit bunch Dilute acid hydrolysis Batch, 37oC, pH 7.0 Anaerobic sludge 275.75 mL H2 g-1 TS 2061 mL H2 L-1 d-1 This study
Oil palm empty fruit bunch Sequential dilute acid and enzymatic hydrolysis Batch, 37oC, pH 7.0 Anaerobic sludge 282.17 mL H2 g-1 TS 3175 mL H2 L-1 d-1 This study

aTotal sugar
bReducing sugars
C:Users12732929DownloadsImages1.png
Fig. 1. Two dimensional contour plots of the quadratic model for sugar concentration (g L-1) from dilute acid hydrolysis of oil palm empty fruit bunch.
C:Users12732929DownloadsImages2.png Fig. 2. Two dimensional contour plots of the quadratic model for H2 production rate  (mL H2 L-1 d-1) from dilute acid hydrolysis of oil palm empty fruit bunch.
C:Users12732929DownloadsImages3.png Fig. 3. Two dimensional contour plots of the quadratic model for H2 yield (mL H2 g biomass-1) from dilute acid hydrolysis of oil palm empty fruit bunch.
C:Users12732929DownloadsImages4.png Fig. 4. Two dimensional contour plots of the quadratic model for sugar concentration (g L-1) from enzymatic saccharification of oil palm empty fruit bunch.
C:Users12732929DownloadsImages5.png Fig. 5. Two dimensional contour plots of the quadratic model for H2 production rate (mL H2 L-1 d-1) from enzymatic saccharification of oil palm empty fruit bunch.

C:Users12732929DownloadsImages6.png Fig. 6. Two dimensional contour plots of the quadratic model for H2 yield (mL H2 g biomass-1) from enzymatic saccharification of oil palm empty fruit bunch.
C:Users12732929DownloadsImages7.png
Fig. 7. The volatile acid (Fo: formate; Ac: acetate; Pr: propionate; Bu: butyrate; Le: levulinate) formation after H2 fermentation at the conditions of maximum H2 production performance after dilute acid hydrolysis and enzymatic saccharification
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