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Korean Society for Biotechnology and Bioengineering Journal 2022; 37(3): 100-106

Published online September 30, 2022 https://doi.org/10.7841/ksbbj.2022.37.3.100

Copyright © Korean Society for Biotechnology and Bioengineering.

Optimization of Enzymatic Saccharification of Lipid-Extracted Sewage Sludge for Bioethanol Production

Pansuwan Supaporn1 and Sung Ho Yeom2*

1College of Multidisciplinary Sciences, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
2Department of Biochemical Engineering, Gangneung-Wonju National University, Gangneung 25457, Korea

Correspondence to:Tel: +82-33-640-2406, Fax: 82-33-641-2410
E-mail: shyeom@gwnu.ac.kr

Received: September 15, 2022; Revised: September 26, 2022; Accepted: September 26, 2022

Our previous study reported the extraction of lipids from raw sewage sludge for biodiesel production. This study demonstrates application of the residue, lipid-extracted sewage sludge (LESS) with 8.9% carbohydrate, to obtain sugars for bioethanol production. Enzymatic saccharification was adopted to process the LESS, while a mixture of two widely used commercial enzymes (Celluclast and Viscozyme at a mixing volume ratio of 2:1) was shown to be the most effective among the individual enzymes and enzyme mixtures at various mixing ratios. By statistical optimization of enzymatic saccharification process, a sugar yield of 72.0% was obtained from the LESS under the following optimal conditions: 1188.6 μL of enzyme mixture/g-LESS at 50.0°C and a reaction time of 37.1 h. Using 5.0 and 10.0 g/L solutions of sugars obtained from LESS via enzymatic saccharification under the optimal conditions, 1.91 and 3.31 g/L bioethanol were obtained after 18 h of fermentation, respectively.

Keywords: lipid-extracted sewage sludge, enzymatic saccharification, optimization, bioethanol

The annual production of sewage sludge in Korea, which was 4 million tons in 2013, is expected to increase to 5.4 million tons by 2025 [1,2]. Currently, the most common sewage sludge disposal processes include recycling for cement and other construction materials (24.5%), drying for fuel (24.4%), incineration (22.3%), and landfilling (19.0%) [1,3]. However, the utilization of these processes has become increasingly strained due to limitations on the availability of land and increasingly restrictive environmental regulations. Environmentally friendly alternatives, which are both economically feasible and regulation-compliant, must therefore be developed [3-5].

Our previous studies utilized raw sewage sludge (RSS) discharged from a local wastewater treatment facility comprising of 14.5% lipids, 8.9% carbohydrates, 42.8% proteins, and 32.1% others (including ash) [4,5]. Lipids from RSS were converted to biodiesel by transesterification with methanol via a novel one-step direct process, as well as the conventional two-step process [4,5]. In the two-step process, lipids are extracted from RSS using organic solvents and subsequently transesterified with methanol to yield biodiesel. In contrast, our proposed method allows for simultaneous lipid extraction and transesterification in a reactor via a one-step direct process. The lipid-extracted sewage sludge (LESS) generated from both biodiesel production processes accounted for over 80 wt% of RSS. Therefore, further utilization of RSS is required to minimize solid waste disposal while maximizing the valorization of RSS; in this regard, sugar extraction from LESS for bioethanol production presents a promising alternative [3].

Dilute acid treatment has been widely utilized for sugar extraction from sustainable biomass, including lignocellulosic materials, macroalgae, microalgae, agricultural waste, and paper sludge [6-10]. Enzymatic saccharification is a promising alternative technology for sugar extraction from biomass [11-18]. In particular, commercial enzymes such as Viscozyme® L (Viscozyme below), Celluclast® 1.5 L (Celluclast below), and their mixtures have been widely used for enzymatic saccharification of various biomass materials [12-18]. For example, carbohydrates in soy slurry were hydrolyzed to generate sugars using Viscozyme; 1.36 and 0.19 g of glucose and galactose (per 100 g of tofu) were obtained at 55oC, respectively [12]. Celluclast has individually been used for the hydrolysis of paper sludge for bioethanol production [13], Brewer’s spent grain for bioconversion [14], and watermelon for bioethanol production [15]. A mixture of Viscozyme and Celluclast has also been used to produce sugars from willow for bioethanol production [16], from apple pomace for biofuel and biorefinery chemicals [17], and from wheat straw for bioethanol production [18].

Despite the extensive research on sugar extraction from sustainable biomass and waste resources, only a few studies have investigated sugar extraction from sewage sludge as a resource for bioethanol production [19,20]. Moreover, these studies involve the preparation of sewage sludge hydrolysate by direct saccharification of RSS, followed by fermentation to produce ethanol. Therefore, the scope of these studies is limited to the utilization of only carbohydrates from RSS to produce bioethanol. In contrast, our previous work first utilized RSS lipids to produce biodiesel as a 3.0% additive to conventional gas station diesel fuel, consistent with Korean regulations [4,5]. To enhance the utilization of LESS via dilute-acid treatment, we statistically optimized the sugar extraction process to achieve a 75.5% sugar yield under the following conditions: 5.9% (v/v) sulfuric acid at 120°C, over a duration of 85 min [3]. Bioethanol at a concentration of 1.8 g/L was subsequently produced from 5.0 g/L of sugars extracted from LESS [3].

In this study, an alternative enzymatic saccharification technique was implemented for sugar extraction from LESS. This process was also statistically optimized to achieve a high yield of sugar, which was subsequently used as a resource for bioethanol.

2.1. LESS preparation and materials

RSS was donated by a wastewater treatment facility located in the city of Gangneung, Korea [4,5]. The facility discharges sewage sludge after mixing primary and secondary sewage sludge at a mixing ratio of 5:1 [4,5]. The RSS was dried in a fume hood over a period of four days and then in an oven at 105°C for 24 h [4,5]. LESS preparation and analysis techniques have been described in previous studies [3-5]. The two commercial saccharification enzymes, Viscozyme® L (Novo, Denmark) and Celluclast® 1.5 L (Novo, Denmark), were purchased from Sigma Aldrich, USA. For brevity, we designate them as Viscozyme and Celluclast throughout the remainder of this work. All bioethanol production chemicals were of analytical grade.

2.2. Statistical experimental design for enzymatic saccharification

Statistical experimental design is an effective tool for investigating the impact of various factors on a given response (here, sugar yield). This method yields model equations quantifying the relationship between the factors and response; the reaction conditions can subsequently be optimized to maximize the desired response [27-29]. This study employed a central composite design (CCD) with 20 runs [3]. Enzyme loading (X1), reaction time (X2), and reaction temperature (X3) were selected as the independent variables to optimize sugar yield (Y), which is defined as the mass of sugar extracted from carbohydrates present in LESS. The five levels of each variable were coded as -2, -1, 0, 1, and 2; their actual values are listed in Table 1. Experimental design and analysis of experimental data were conducted using Design Expert 8.0.6 (Stat-Ease Inc., USA). The relationship between Xi and Y was expressed as the second-order polynomial represented by Equation (1).

Table 1 Formulation of central composite design for enzymatic sugar extraction from lipid-extracted sewage sludge

SymbolFactorUnitLevel
−2−1012
X1Enzyme loadingμL500750100012501500
X2Reaction temperature°C3037.54552.560
X3Reaction timeh1025405570


Y=β0+i=13βiXi+i=13β iiXi2+i=13j=13 βijXij

Here, Y represents the response (sugar yield), while β0, βi, βii, and βij are the model intercept and linear, quadratic, and interactive regression coefficients, respectively. Xi and Xj are the independent variables. The interactions between the independent variables were determined by analysis of the three-dimensional response surface plot. The set of optimum reaction conditions and corresponding maximum response were obtained by solving the equation (1) with Design Expert 8.0.6.

2.3. Enzymatic saccharification

Viscozyme and Celluclast have been extensively used for biomass saccharification [10-13], while also being employed for enzymatic saccharification of LESS to obtain sugars for bioethanol production. One gram of LESS was added to a 100 mL flask containing 50 mL of 0.1 M sodium citrate buffer (pH 4.8). The enzyme/enzyme mixture was introduced to the flask [30]. The enzyme loading (μL), reaction temperature (°C), and reaction time (h) were varied for each experimental run. The reaction mixture was incubated in a shaking water bath (BS-21; Jeio Tech, Korea). Following saccharification, the flask containing the reaction mixture was immediately transferred to a water bath at 90°C where it remained for 10 min to allow the enzyme to become denatured. The reaction mixture was then centrifuged at 3,000 rpm for 10 min [30]. The supernatant solution was sampled and filtered using a syringe filter (pore size: 0.22 μm; Whatman, USA). The sugar concentrations were measured using a high-performance liquid chromatography unit (HPLC, Perkin-Elmer, USA) equipped with a refractive index detector (RID) and an organic acid analysis column (300mm× 78 mm, Aminex HPX-87H, Bio-Rad, USA) [3]. The mobile phase was a 0.005 M sulfuric acid solution flowing into the HPLC unit at 0.5 mL/min. The column temperature was set at 50°C.

2.4. Bioethanol production

A previous study determined commercial brewer's yeast – registered as Active Dry Yeast (Ottogi, Korea) – to be the most effective among the candidates evaluated for bioethanol production from sugars derived from the dilute-acid treatment of LESS [3]. The commercial brewer’s yeast was therefore also used in this study. The basal medium, which was adjusted to a pH of 5.0, consisted of 0.02% (NH4)2SO4 and 0.006% NaH2PO4 [3]. Bioethanol was produced in a 100 mL bottle containing 50 mL basal medium supplemented with sugars extracted from the LESS via enzymatic saccharification; sugar concentrations of 5 and 10.0 g/L were each used in two separate bottles. Cells were inoculated into the bottles to concentrations of 1.0 g/L. The bottles were then placed in a shaking incubator (SI-600R, Jeio Tech, Korea) at 30°C and 120 rpm [3]. Samples were periodically collected from the bottle and centrifuged at 12,000 rpm (Combi-514R, Hanil, Korea) for 10 min each. The supernatant was filtered through a syringe filter (pore size; 0.22 μm; Whatman, USA) and injected into the HPLC unit to measure its bioethanol concentration. The HPLC unit (Perkin-Elmer, USA) was equipped with a refractive index detector (RID) and an organic acid analysis column (300 mm × 78 mm, Aminex HPX-87H, Bio-Rad, USA). The mobile phase comprised of 0.005M H2SO4 solution at a flow rate of 0.5 mL/min. The column temperature was set at 50°C [3]. A commercial ethanol of analytical grade (Duksan, Korea) was used to identify bioethanol in the HPLC analysis. The experiments on bioethanol production were conducted in triplicate, and mean data with standard deviation are presented in this study.

3.1 Comparison of the performance of a single enzyme vs. enzyme mixture for saccharification

The performance of individual enzyme (Celluclast or Viscozyme) and their mixtures was evaluated for sugar extraction from LESS. As shown in Fig. 1, sugar yield increased with both an increase in reaction time and enzyme loading. When the loading of Celluclast was increased from 200 to 400 μL, the sugar yield at 12 h of reaction also increased from 5.9 to 11.2%, respectively, while increasing from 9.8 to 15.2% at 24 h of reaction. When analogously increasing the loading of Viscozyme from 200 to 400 μL, the sugar yield at 12 h of reaction increased from 6.2 to 7.9%, and from 8.7 to 13.7% at 24 h of reaction. To investigate the effect of mixing the two enzymes, a total enzyme loading of 400 μL of a 1 : 1 mixture of Celluclast and Viscozyme was used for sugar extraction. As indicated in Fig. 1, sugar yields of 15.2 and 22.7% were obtained using this enzyme mixture at 12 and 24 h of reaction time, respectively. This 1 : 1 enzyme mixture exhibited 49.3 and 65.7% higher sugar yields relative to identical enzyme loadings of Celluclast and Viscozyme, respectively. These results indicate that mixture of the two enzymes is more effective than a single enzyme for sugar extraction from LESS.

Figure 1. Effect of enzyme type and reaction time on the sugar yield.
■: 12 h, ▨: 24 h
A: Celluclast 200 μL, B: Celluclast 400 μL, C: Viscozyme 200 μL, D: Viscozyme 400 μL, E: Enzyme mixture (Celluclast 200 μL + Viscozyme 200 μL).

To determine the optimal mixing ratio of the two enzyme components, enzymatic saccharification experiments were conducted at various total enzyme loadings and mixing ratios. Total loadings of 300, 400, and 500 μL/g-LESS were employed – each at Celluclast to Viscozyme mixing ratios of 1 : 3, 1 : 2, 1 : 1, 2 : 1, and 3 : 1. As presented in Table 2, the sugar yield increased with an increase in total enzyme loading. In addition, a 2 : 1 mixing ratio of Celluclast to Viscozyme was determined to be the highest performing at all levels of total enzyme loading. The highest sugar yield, 32.06%, was obtained at a total loading of 500 μL, 24 h of reaction time, and with a 2 : 1 ratio of Celluclast to Viscozyme. Other studies which also utilized mixtures of Celluclast and Viscozyme indicate that the optimal mixing ratio of the two enzyme components differs depending on the biomass substrate: 1 : 5 for the enzymatic saccharification of steam-pretreated willow [16]; 1 : 1 for apple pomace [17]; 1 : 2 for wheat straw [18]. These variations are a result of the two enzymes possessing different activities for various sugar-resource substrates [17].

Table 2 Effect of total enzyme loading and mixing ratio on the sugar yield (%)

Loading (μL)Mixing ratio (Celluclast to Viscozyme)
1:31:21:12:13:1
Time (h)Time (h)Time (h)Time (h)Time (h)
24482448244824482448
3004.236.286.958.985.4212.079.5417.128.1210.53
40012.3115.8713.5721.8915.1822.7322.6824.7215.4518.21
50015.5118.3422.6226.3421.8725.5228.8532.0621.3423.12


3.2. Statistical enzymatic saccharification

A central composite design (CCD) was used to optimize the conditions for sugar extraction from LESS – based an enzyme mixture composed of a 2 : 1 ratio of Celluclast to Viscozyme. The experimental design is presented in Table 1. Experimental data and data analysis are presented in Tables 3 and 4, respectively. Table 4 shows that the model equation is significant; the value of the regression coefficient (R2) for the model is 0.93, indicating suitable fit. Based on the magnitude of their coefficients, it can be seen that the linear and quadratic terms of X1 (enzyme loading), linear and quadratic terms of X2 (reaction temperature), and quadratic term of X3 (reaction time) significantly impact sugar yield. The second–order polynomial model for sugar yield is represented by Equation (2), in which the variable coefficients are expressed as real values. Optimal enzymatic saccharification conditions were obtained by solving this equation, which corresponded to: 1188.6 μL/g-LESS enzyme loading; a reaction temperature of 50.0°C; a reaction duration of 37.1 h.

Table 3 Experimental design and actual experimental data for the central composite design

RunFactorbY
aX1X2X3
175052.52548.3 ± 0.63
2125052.55571.5 ± 1.64
3100045.04073.5 ± 0.97
4100060.04051.1 ± 0.72
5150045.04055.7 ± 1.72
6100045.04073.9 ± 0.97
750045.04016.4 ± 1.16
8100045.04075.4 ± 0.86
9125037.52542.5 ± 0.03
10125037.55527.4 ± 0.52
11100045.04076.6 ± 0.91
12125052.52572.1 ± 0.59
1375037.55516.6 ± 0.47
1475052.55548.3 ± 0.09
15100045.01033.1 ± 0.55
16100045.07044.7 ± 0.57
17100045.04076.5 ± 0.32
1875037.52519.2 ± 0.80
19100030.04026.9 ± 0.50
20100045.04077.0 ± 0.44

a: Xi is defined in Table 1.

b: Y (sugar yield, %) is expressed as mean ± standard deviation.


Table 4 Regression coefficients and their p-values from the central composite design of three factors for sugar yield (%)

Coefficientp-value
Model< 0.0001
Intercept (β0).−552.17
aX10.3460.0007
X215.100.0002
X32.980.8785
X120.000160.0001
X22−0.170.0002
X32−0.0420.0002
X1X20.00860.5887
X1X3−0.00430.5887
X2X30.0190.4791
R293.0%

a: Xi is defined in Table 1



Sugar yield (%) = -552.17+0.346X1+15.10X2+2.98X3+0.00086X1X2-0.00043X1 X3+0.019X1X3-0.00016X12-0.17X22-0.042X32

3.2.1. Effect of enzyme loading and reaction temperature on sugar yield

The loading of enzyme biocatalysts on a substrate (in this case, LESS) represents one of the most important factors influencing saccharification yield; a high enzyme loading generally results in a high yield [21-23]. Since the enzyme is composed of proteins, there is an optimum temperature that corresponds to the highest saccharification yield; however, the enzyme can undergo thermal denaturation above this optimum temperature [11,13,16,23]. The interrelated impact on sugar yield of enzyme loading and reaction temperature were investigated in the ranges of: enzyme loading from 500 to 1500 μL; reaction temperatures from 30-60°C. Fig. 2(A) illustrates that sugar yield increased with increased enzyme loading from 500 to approximately 1200 μL. However, sugar yield decreased as enzyme loading was increased beyond 1200 μL. Experimental data presented in Table 3 indicate that sugar yield increased from 16.4 to 75.5% as enzyme loading increased from 500 to 1000 μL at intermediate temperatures and reaction times (45°C and 40 h), but decreased to 55.7% when the enzyme loading was further increased to 1500 μL. Beyond a certain point, increased enzyme loading was found to have a minimal or negative impact on sugar yield due to the following factors: a decrease in the extent of the adsorbed enzyme; transformation of the cellulose structure into a less digestible form; the inhibition of enzyme activity due to the accumulation of hydrolysis products [11,14,23]. With respect to temperature, sugar yield increased from 26.9 to 75.5% by increasing the reaction temperature from 30 to 50°C, while decreasing to 51.1% with an additional increase from 50 to 60°C, which could be explained by the loss of enzyme activity due to thermal deactivation [16,17,23,24]. According to a previous study, the optimum temperature for both Celluclast and Viscozyme is approximately 50°C – indicating the potential for the inhibition of their activity at higher temperatures [17]. Previous studies have reported reducing saccharification yields of 50.0, 36.2, and 22.5% with increasing temperatures of 45, 55, and 65°C, respectively [24]. Another study has demonstrated a maximum saccharification yield (56.4%) at 50°C, with decreased saccharification observed at temperatures higher than this optimum temperature [25].

Figure 2. Contour plots illustrating the effect of two factors on sugar yield: (a) effect of enzyme loading and reaction temperature; (b) effect of enzyme loading and reaction time; (c) effect of reaction temperature and reaction time.

3.2.2. Effect of reaction time and enzyme loading on sugar yield

The product yield of biocatalytic reactions is generally improved by long reaction times. For example, the saccharification yield from de-oiled biomass increased from 15.9 to 33.5% with an increase in hydrolysis time from 6 to 72 h [26]. However, a reaction time that is too long can introduce side reactions that lower the overall product yield [11,21]. Fig. 2(B) presents sugar yield in relation to reaction time in the range of 10-70 h and enzyme loading in the range of 500-1500 μL. The data indicates that low sugar yields were obtained at over short reaction durations and with low enzyme loading. The sugar yield increased monotonically with both reaction time, up to approximately 40 h, and enzyme loading, up to 1200 μL, beyond which a reduction in sugar yield was observed. With respect to reaction time, sugar yield increased from 33.1 to 75.5% as the reaction time increased from 10 to 40 h – at an intermediate level of enzyme loading and reaction temperature (1,000 μL and 45°C, respectively); the sugar yield subsequently decreased to 44.7% when the duration of the reaction was allowed to further progress to 70 h.

3.2.3. Effect of reaction temperature and reaction time on sugar yield

Fig. 2(C) presents sugar yields obtained at reaction temperatures in the range of 30 – 60°C and reaction times from 10–70 h. At a relatively low reaction temperature of 30°C and short reaction time of 10 h, the sugar yield was expectedly lowest. The optimal sugar yield was obtained by increasing the reaction temperature and extending the reaction time to approximately 50°C and 40 h, respectively. However, consistent with our previous findings, sugar yield decreased as reaction temperature exceeded 50°C and as reaction time progressed beyond 40 h.

3.2.4. Verification experiments

As stated above, the optimal saccharification conditions were determined to be: 1188.6 μL/g-LESS enzyme loading; 50°C reaction temperature; a reaction duration of 37.1 h. A sugar yield of 80.2% was predicted under the optimal conditions. The validation experiments conducted under these conditions produced a sugar yield of 76.3 ± 2.7%, which is slightly lower than, though still in good agreement with, the predicted value.

3.3. Bioethanol production from sugars obtained by enzymatic saccharification

A previous study produced bioethanol from sugars extracted from LESS via dilute acid treatment; we adopted a promising commercial microorganism (brewer’s yeast), which was chosen from among various candidates, as a biocatalyst for bioethanol production [3]. In this study, we have also utilized the commercial brewer’s yeast for bioethanol production. The initial sugar concentrations, prepared via enzymatic saccharification of LESS, were approximately 5.0 and 10.0 g/L. As illustrated in Fig. 3, bioethanol concentrations after 6 h of fermentation were 1.70 and 2.47 g/L for initial sugar concentrations of 5.0 and 10.0 g/L, respectively. After 12 h of fermentation, the bioethanol concentrations further increased to 1.76 g/L and 3.29 g/L, respectively. Finally, after 18 h of fermentation, the concentrations of bioethanol evolved to 1.91 g/L and 3.31 g/L, respectively. No further increase in the bioethanol concentration was observed beyond 18 h of fermentation. This result indicates that bioethanol can readily be produced from sugars prepared via enzymatic saccharification from LESS. Relative to the previous bioethanol production from sugars, prepared via dilute acid treatment of LESS, bioethanol production was significantly faster (18 h vs. 24 h). However, the required duration for enzymatic saccharification was much longer than that for dilute-acid treatment (37 h vs. 85 min). Dilute-acid treatment is conducted in an autoclave at high temperature (120°C) with the aid of an acid catalyst (5.9 v/v sulfuric acid) – rapidly speeding up the saccharification of LESS. Because each process possesses unique advantages and disadvantages, detailed comparisons should be developed in future studies based on a sound technoeconomic analyses in order to synthesize and design the most promising process for bioethanol production from LESS.

Figure 3. Bioethanol production from sugars obtained from lipid-extracted sewage sludge via enzymatic saccharification.

Following lipid extraction, sewage sludge was used as a source of sugars for bioethanol production. The mixture of two commercial enzymes, Celluclast and Novozyme at a volume ratio of 2 : 1, outperformed all other pure and mixed enzyme component loadings. The statistical optimization of the enzymatic saccharification process was performed to arrive at a maximum sugar yield of 76.3% under a set of optimal conditions (1188.6 μL/g-LESS enzyme mixture, 50.0°C reaction temperature, and 37.1 h reaction duration). Bioethanol concentration of 1.91 and 3.31 g/L was successfully produced using 5.0 and 10 g/L of sugar, respectively, prepared via enzymatic saccharification from LESS. These findings indicate that LESS is a promising feedstock for bioethanol production.

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07051113 and NRF-2022R1F1A1072189). The authors greatly appreciate this support. The authors declare no conflict of interest. Neither ethical approval nor informed consent was required for this study

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