Energy production from different organic wastes by anaerobic co-digestion: Maximizing methane yield versus maximizing synergistic effect

Renewable Energy 136 (2019) 683e690

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Energy production from different organic wastes by anaerobic codigestion: Maximizing methane yield versus maximizing synergistic effect Jinsu Kim, Gahyun Baek, Jaai Kim, Changsoo Lee* School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, Republic of Korea

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 August 2018 Received in revised form 3 January 2019 Accepted 13 January 2019 Available online 14 January 2019

The anaerobic co-digestion of spent coffee grounds (SCG) and Ulva biomass, which are problematic wastes and unsuitable for mono-digestion, with food waste (FW) was investigated to widen the scope of feedstocks for biogas production. The effect of the feedstock mixing ratio on the methane yield and synergistic effect of co-digestion was analyzed by response surface analysis. The models for the methane yield and synergistic effect indicated different response patterns and predicted the maximum responses at different mixing ratios. As maximizing the conversion of individual feedstocks to methane is the primary focus in this study, the mixing ratio required for maximizing the synergy index is perceived to be more desirable than that for maximizing methane yield of the mixture. The experimental and modeling results demonstrated that FW, SCG, and Ulva biomass can be effectively co-digested with little antagonistic effect, regardless of their mixing ratio, and a synergistic effect in most cases. It is expected that codigestion could be flexibly applied when managing the waste feedstocks to enhance their energy recovery potential. The findings of this study can help promote the valorization of underused waste feedstocks through co-digestion and increase the deployment of renewable energy. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Food waste Methane yield Response surface analysis Spent coffee ground Synergy index Ulva

1. Introduction Converting organic wastes into energy-rich methane through anaerobic digestion (AD) is a sustainable solution to the current energy and environmental problems. Owing to its ability to recover renewable energy from waste biomass, AD has been widely used to manage various organic wastes, such as food waste, animal manure, and sewage sludge. However, there are many potential feedstocks to be explored to allow wider and more versatile applications of AD as a source of clean energy. Those with high carbon content (i.e., high energy potential) and low economic value (i.e., less expensive feedstock) are of great interest from both academic and practical viewpoints. Coffee is one of the most traded primary commodities. Approximately nine million tons of coffee is consumed per year, which is expected to continue to increase (http://www.ico.org). As a result, over eight million tons of spent coffee grounds (SCG) are

* Corresponding author. E-mail address: [email protected] (C. Lee). https://doi.org/10.1016/j.renene.2019.01.046 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

produced annually as residue after brewing [1]. In many countries, SCG is usually disposed in landfills, and its management is becoming increasingly challenging [2]. If it is not properly treated, the readily degradable organic matter in SCG can decompose and cause serious pollution problems. The high organic content of SCG means that it is an attractive feedstock for AD, and many studies have investigated its biomethanation potential. However, previous studies have digested SCG with supplementary nutrients and/or trace elements, and it has been reported that the mono-digestion of SCG as a sole feedstock is prone to failure due to the lack of nutrients and trace elements, and high content of slowly degradable lignocellulosic fibers [2]. Ulva, commonly known as sea lettuce, is a green macroalgal genus whose species are the main culprits of the ‘green tides’ (i.e., massive blooms of green macroalgae) that occur perennially worldwide [3]. Blooms of readily perishable macroalgae cause serious pollution in coastal areas, which in turn results in serious hygiene and environmental problems [4]. Ulva blooms have become more frequent and severe with global warming; therefore, an effective method of managing the waste macroalgal biomass is urgently required. Large green tides have occurred every year in the

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western Yellow Sea off China since 2007, and the most severe bloom, which occurred in the summer of 2008, left over 1.5 million tons of Ulva biomass on the coast of Qingdao [5,6]. Ulva biomass is readily biodegradable and rich in nutrients and minerals to support microbial growth [2], suggesting that AD may be an economically and environmentally attractive method of managing its problematic waste. However, the low C/N ratio and high S content of Ulva biomass can cause the buildup of toxic free ammonia and hydrogen sulfide and inhibit the microbes involved in AD [4]. A promising approach to overcome the limitations of these potential feedstocks is co-digestion with other feedstocks, which can improve the properties of the feedstock mixture [7]. Codigestion can enhance the performance and stability of an AD process by adjusting the carbon/nutrient ratio, remedying the trace-element deficiency, increasing the buffering capacity, and diluting the content of inhibitors [2]. Therefore, selecting suitable co-feedstocks and determining their optimal mixing ratio are vital for successful co-digestion. Co-digesting SCG and Ulva biomass could produce positive outcomes due to their physicochemical properties, which has been demonstrated in previous studies by the authors [2,8]. There are seasonal (between the summer and winter) and regional (between inland and coastal regions) variations in the availability of Ulva biomass, while SCG production does not vary significantly across these scales. This makes it difficult to establish a stable supply of feedstock and hence reduces the feasibility of co-digestion. One way to solve this problem is codigestion with another common and abundant feedstock that can serve as the sole substrate for AD. This allows the more flexible and robust management of feedstock variability. This study investigated the co-digestion of SCG and Ulva biomass with food waste (FW) to explore their potential in diversifying the feedstocks used for biogas production and increasing the deployment of renewable energy. Ternary mixtures of the feedstocks were tested at different mixing ratios to determine their biomethanation potential in batch mode. The effect of the feedstock mixing ratio on methane production was evaluated through response surface analysis. Both the methane yield and the synergistic effect of co-digestion on the yield were determined for each mixture, and the optimum conditions for maximizing their responses were estimated by response surface modeling. To the authors' knowledge, this is the first study to quantitatively determine and model the synergistic effect of mixing different waste feedstocks for anaerobic co-digestion. The outcomes of this study provide a useful reference for selecting suitable feedstocks and their mixing ratios for successful co-digestion. 2. Materials and methods 2.1. Inoculum and waste feedstocks The anaerobic sludge used as an inoculum for the biochemical methane potential (BMP) tests was obtained from a full-scale digester that co-digests food waste and sewage sludge. The sludge was sieved through an 850-mm mesh and starved for 7 days at 35
C under anaerobic conditions before use. The volatile solids (VS) concentration of the inoculum was 24.6 g/L, accounting for 64.2% of the total solids (TS), while the volatile suspended solids (VSS) concentration was 22.5 g/L. The total and soluble chemical oxygen demand (COD) concentrations of the inoculum were 43.4 and 4.3 g/L, respectively. SCG was obtained from a coffee shop in Ulsan National Institute of Science and Technology (UNIST), and was dried and stored in a desiccator at room temperature until use. Fresh Ulva biomass was collected from a local beach and rinsed with a small amount of tap water to remove any sand, debris, and salt. FW was collected from a

cafeteria in UNIST and mainly contained cooked rice with smaller amounts of meat, vegetables, and fruit. The FW and Ulva biomass were separately ground into a slurry using a household blender. The physiochemical characteristics of the feedstocks are summarized in Table 1. 2.2. Biochemical methane potential test BMP tests were performed in 120-mL serum bottles with a 100mL working volume, which were filled with equal volumes of the inoculum and a feedstock mixture. Thirteen mixtures were prepared based on the VS concentration, according to a ternary mixture design: three vertices, six thirds of edges, three axial check blends, and one overall centroid. The VS concentration of the mixtures was adjusted to 20 g VS/L with distilled water so that each bottle contained 1 g of the VS feedstock for digestion. Fourteen BMP runs (i.e., 13 runs with feedstock mixtures and the inoculum-only control to determine the biogas production from the inoculum alone; Table 2), were conducted in triplicate (i.e., a total of 42 trials). Each bottle was deoxygenated by flushing the headspace with nitrogen for 30 s and then tightly sealed with a rubber stopper and an aluminum crimp. The BMP reactors were incubated with intermittent manual shaking at 35
C for 30 days, along with biogas production and composition monitoring. The biogas volume was measured by using a syringe. The measured biogas volume was corrected to standard temperature and pressure. 2.3. Analytical methods Solids were measured following the procedures presented in the Standard Methods [9]. COD was measured spectrophotometrically using HS-COD-MR vials (HUMAS, Korea) with a detection range of 50e1500 mg/L. Volatile fatty acids (VFAs, C2eC7) were analyzed using a gas chromatograph (7820A, Agilent, USA) equipped with a flame ionization detector and an Innowax column (Agilent). The dynamic range for VFA measurement was 0e10 mM. The samples

Table 1 Physiochemical characteristics of the feedstocks. FWa d

C (%) H (%)d O (%)d N (%)d S (%)d Al (mg/L) Co (mg/L) Cr (mg/L) Cu (mg/L) Fe (mg/L) Mn (mg/L) Ni (mg/L) Zn (mg/L) Mo (mg/L) W (mg/L) Total COD (g/L) Soluble COD (g/L) Total VFAs (mg COD/L) Acetate (mg COD/L) Butyrate (mg COD/L) TS (g/L) VS (g/L) a b c d e f

e

49.2 (1.3) 7.1 (0.2) 44.4 (0.3) 3.7 (0.6) ndf 1.95 (0.00) nd 0.03 (0.00) 0.42 (0.00) 3.86 (0.00) 0.73 (0.00) 0.09 (0.00) 2.10 (0.00) 0.04 (0.00) nd 127.4 (11.3) 77.4 (0.9) 941.6 (127.7) 941.6 (127.7) nd 105.5 (0.7) 97.4 (0.6)

SCGb

Ulvac

54.3 (0.1) 7.4 (0.1) 36.9 (0.4) 2.1 (0.1) nd 1.65 (0.00) nd 0.05 (0.00) 0.14 (0.00) 1.22 (0.00) 0.28 (0.00) nd 3.64 (0.00) 0.05 (0.00) 0.01 (0.00) 15.6 (0.2) 0.4 (0.4) 1.3 (0.2) 1.3 (0.2) nd 10.0 (0.1) 9.9 (0.1)

39.3 (0.3) 5.9 (0.0) 42.2 (0.1) 4.4 (0.0) 2.2 (0.2) 36.80 (0.03) 0.01 (0.00) 0.05 (0.00) 0.54 (0.00) 55.20 (0.00) 2.96 (0.00) 0.12 (0.00) 1.77 (0.00) 0.01 (0.00) nd 49.0 (2.7) 11.9 (0.1) 400.7 (22.6) 13.5 (4.6) 387.2 (27.2) 39.2 (2.0) 32.2 (1.4)

Measured from a FW slurry prepared by grinding. Measured from a SCG suspension in water (10 g dry/L). Measured from an Ulva slurry prepared by grinding. Given as % weight/weight on a dry basis. Standard deviations are given in parentheses. Not detected.

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selection.

Table 2 Experimental conditions for the BMP experiments. Run

FW

SCG

Ulva

C/N ratioa

C/S ratio

1 2 3 4 5 6 7 8 9 10 11 12 13 Control

1 0 0 0.67 0.33 0.67 0.33 0 0 0.67 0.17 0.17 0.33 0

0 1 0 0.33 0.67 0 0 0.67 0.33 0.17 0.67 0.17 0.33 0

0 0 1 0 0 0.33 0.67 0.33 0.67 0.17 0.17 0.67 0.33 0

13.3 25.9 8.9 16.0 20.0 11.7 10.2 17.3 12.2 13.6 18.5 11.2 14.0 ec

ndb nd 17.9 nd nd 63.3 28.9 68.0 30.0 130.7 137.5 29.8 64.9 e

All conditions were tested in triplicate. a Calculated based on the characteristics and mixing ratio of feedstocks. b Not detected. c Not determined.

for analyzing soluble COD and VFAs were prepared by filtration through a membrane syringe filter (pore size 0.45 mm). Biogas composition (CH4, CO2, and H2) was analyzed using the same gas chromatograph equipped with a thermal conductivity detector and a ShinCarbon ST Micropacked column (Restek, USA), and the detection limit was 1.0% (v/v) for all the gases. The C, H, O, N, and S contents were determined on a dry weight basis using a Flash 2000 elemental analyzer (Thermo Scientific, The Netherlands) with a detection range of 0.01e100%. Metals were quantified using an inductively-coupled plasma-optical emission spectrometer (700ES, Varian, USA) with the following lower detection limits (in mg/ L): Al (0.014), Co (0.010), Cr (0.012), Cu (0.015), Fe (0.015), Mn (0.013), Mo (0.005), Ni (0.027), W (0.010), Zn (0.020). All analyses were performed in duplicate at least. 2.4. Synergy index calculation The synergistic effect of co-digestion was analyzed for each feedstock mixture based on the methane yield and compared to mono-digestion. The synergy index was determined as the ratio of the observed to the estimated yields (i.e., the sum of the yields from each feedstock in the mixture estimated based on the proportion of each feedstock in the mixture and the mono-digestion results). Therefore, synergy indices below and above 1 indicate antagonistic and synergistic effects, respectively.

3. Results and discussion 3.1. Methane production from different feedstock mixtures Fig. 1 compares the cumulative methane yield (per unit mass of feedstock based on the VS content) among the BMP runs under different experimental conditions. The different feedstock mixtures exhibited different methane production profiles during the 30-day incubation period (Fig. 1). The feedstock mixtures with 67% or greater proportions of FW (Runs 1, 4, 6, and 10; Table 2) exhibited greater cumulative methane yields than the others. This suggests that higher proportions of FW in feedstock mixtures are beneficial for AD, which may be due to its highly biodegradable and nutritionally balanced nature. The high-FW mixtures exhibited faster initial methane production, particularly during the first three days, without a lag phase. This could be because the concentrations of VFAs in FW were higher than those in the other feedstocks (Table 1), given that VFAs, particularly acetate, are readily converted to methane. The highest methane yield of 0.535 L/g VS fed was observed during Run 4 (67% FW:33% SCG), while Run 1 (100% FW) yielded less methane, with a suspension of methane production between days 6 and 13. This could be related to the complex organic composition of FW, as it contains various carbohydrates, proteins, and lipids with different biodegradation characteristics. Carbohydrates are generally more readily biodegradable in anaerobic environments than proteins and lipids, which require a longer time for hydrolysis [10]. This may explain the biphasic methane production pattern observed during the mono-digestion of FW, which could reflect the sequential utilization of readily and slowly biodegradable substances. The rapid hydrolysis and fermentation of readily biodegradable organics can result in a buildup of VFAs and a decrease in the pH. Such an effect may have also contributed to the methane production profile of Run 1, given that methanogenesis is inhibited under acidic conditions [11]. This corresponds to the higher soluble to total COD ratio (i.e., the readily bioavailable fraction of organic matter) of FW (60.8%) than those of SCG (2.5%) and Ulva (24.4%) (Table 1). The high inoculation ratio (50% v/v) and alkalinity (ammonia) produced by the degradation of proteins may have contributed to the restoration and maintenance of a favorable environment [12,13]. In contrast, the feedstock mixtures with 67% or higher

2.5. Response surface analysis Response surface analysis (RSA) is a mathematical tool for modeling expected responses based on experimentally obtained data. It is often used to describe the combined effects of multiple independent variables on the responses of a dependent variable in a complex system. RSA approximates the response surface within the experimental region with a minimal number of experimental trials. The independent variables were the proportions of FW, SCG, and Ulva biomass in the feedstock mixtures from 0 to 1 (Table 2). RSA was conducted by a sequential procedure of collecting experimental data, constructing polynomial equations, and checking the model adequacy. Increasingly complex polynomials were fitted to the experimental data to approximate the responses of the dependent variables (i.e., the methane yield and synergy index). Backward regression was applied to select the most suitable model. Design Expert 7 software (Stat-Ease, USA) was used to design the experimental matrix and conduct the RSA computation for model

Fig. 1. Cumulative methane yield per unit mass of feedstock (L/g VS fed) in the experimental BMP runs. Curves are labelled with the corresponding run numbers. Error bars indicate standard deviations (n ¼ 3).

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proportions of Ulva biomass (Runs 3, 7, 9, and 12; Table 2) exhibited lower methane production rates and yields than the others. Run 3 (100% Ulva) exhibited the lowest methane yield (0.148 L/g VS fed), which is comparable to the values for the mono-digestion of Ulva biomass in the literature [14]. These could be related to the high S content of Ulva biomass (2.2%), resulting in a low C/S ratio of 17.9 (Table 1). A high S content is characteristic of Ulva species as they contain ulvan, a highly sulfated polysaccharide (approximately 20% of the weight constitutes of sulfate groups), as a major cell-wall polysaccharide accounting for approximately 30% of the cell wall weight [15]. A feedstock C/S ratio below 40 can stimulate the overgrowth of sulfate-reducing bacteria that compete with methanogens for substrates (i.e., hydrogen and acetate) and produce toxic sulfide [16]. Uncharged H2S is directly toxic to methanogens and other microbes as it can freely diffuse across cell membranes and denature proteins by forming sulfide or disulfide cross-links [17]. The high-Ulva runs likely produced favorable conditions for sulfate-reducing bacteria with C/S ratios far below 40, significantly inhibiting methanogenesis. The low C/N ratio (8.9; Table 1) and the slow or limited degradation of the fibrous fraction may have also influenced the lower methane yields of the highUlva mixtures than those of the other mixtures [18,19]. Although SCG contained the most C among the feedstocks with a suitable C/N ratio for AD (Table 1), the feedstock mixtures with 67% or more SCG (Runs 2, 5, 8, and 11; Table 2) exhibited lower methane yields than those of the high-FW mixtures and higher yields than those of the high-Ulva mixtures. The methane yield of the mono-digested SCG (Run 2) was 0.337 L/g VS fed, which is comparable to previously reported values [2]. The other high-SCG mixtures exhibited comparable or higher yields, indicating that the bioconversion of SCG to methane is limited or inhibited under mono-digestion conditions. This could be due to the limited utilization of lignocellulosic SCG residues, which have low biodegradability [20]. The possible production of heterocyclic volatiles, such as pyrroles, furans, and pyrazines, by the Maillard reaction during coffee roasting at high temperatures should also be considered [21]. Unlike furans and aldehydes, which reportedly degrade during AD, pyrazines and other aromatic amines are not fully degraded anaerobically [22]. Among the high-SCG mixtures, the methane yield tended to increase with an increase in the proportion of FW and decrease in the proportion of Ulva biomass. 3.2. Synergistic effect of co-digestion Fig. 2 compares the methane yields and synergy indices determined for the early (10 days), mid (20 days), and total (30 days) incubation periods in the BMP runs performed following the experimental design (Table 2). As expected, the methane yield increased with incubation time, regardless of the tested feedstock

mixtures, although the magnitude and pattern of increase differed between them. However, the synergy index decreased during in all runs, except for those with high-Ulva mixtures (Runs 7, 9, and 12). This suggests that the synergistic effect of feedstock mixing was more pronounced during the early period than the later period. This could be reasonable as more biogas can be produced in a shorter time (i.e., increase in the reaction rate) if the biodegradability of feedstock is improved by co-digestion. A more easily biodegradable co-feedstock could promote the growth of microbes and the start-up of AD, accelerating the microbial production of enzymes, particularly those that catalyze the hydrolysis of complex macromolecules. The enhanced hydrolytic activity could facilitate the utilization of slowly or poorly biodegradable organics and eventually increase the overall methane yield [23]. Such an effect may have occurred in the co-digestion runs with a higher proportion of FW (Runs 4, 6, and 10), which exhibited much higher earlyperiod synergy indices and more significant changes in the methane yield with incubation time than the others. This appears to reflect the accelerated initiation of AD with the utilization of easily biodegradable organics in FW, and the synergistic indices were maintained at high levels until incubation ended (1.146e1.301 on day 30). The high-SCG feedstock mixtures (Runs 2, 5, 8, and 11) produced over 64% of their 30-day cumulative methane production within 10 days. This suggests that the easily biodegradable fraction of SCG was preferentially consumed during the early period, while the residues with limited biodegradability (mainly lignocellulosic compounds) remained largely unutilized. The synergy index increased with a decrease in the proportion of FW and an increase in the proportion of Ulva biomass among the high-SCG mixtures. This may represent a significant increase in the biodegradability of Ulva biomass, which produced least methane when digested alone, by mixing with SCG. This could be at least partially related to the improved C/S and C/N ratios (Table 2). Additionally, Ulva biomass has been reported to be a suitable co-feedstock to SCG as it supplements the mixture with alkalinity and trace elements for stable AD [2,8]. The overall synergy index determined based on the 30-day experimental data was greater than 1 in all the tested codigestion runs (Fig. 2B). Therefore, co-digesting FW, SCG, and Ulva biomass could enhance their anaerobic biodegradability and methane productivity, regardless of their mixing ratio. Co-digestion can provide an effective method of exploiting underused feedstocks, i.e., SCG and Ulva biomass, for biogas production and thus increase their biomass potential for energy purposes. Although they are unclear, the major reasons for synergy could differ between the co-digestion mixtures depending on the characteristics and proportions of the mixed feedstocks. Only the high-Ulva feedstock mixtures (Runs 7, 9, and 12) exhibited synergy indices

Fig. 2. Cumulative methane yields and synergy indices determined on days 10, 20, and 30 of BMP incubation. The mean values are shown on top of the bars. Error bars indicate standard deviations (n ¼ 3).

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model residuals) [24,25]. Consequently, the obtained model (Eq. (1)) could adequately describe the response surface of the methane yield. The linear effects of the feedstocks (XF, XS, and XU) in the model were highly significant (p < 0.0001) (Table 4). The remaining quadratic and cubic terms that represent the interactions between the feedstocks were all significant (p < 0.05). The ternary interaction term (XFXSXU), which exhibited a too-high p-value (>0.17) in the cubic model, was not included in the selected model. These, together with the positive coefficients of all interaction terms, indicate that the effect of the feedstock mixing ratio on the methane yield cannot be described by a linear function, and the interactions between XF, XS, and XU impose significant synergistic effects. XFXS and XFXS(XF e XS) exhibit much higher coefficients than the other interaction terms, suggesting that greater synergistic effects can be incurred by mixing FW and SCG than those in the other mixtures. This corresponds to the BMP experimental results (Fig. 2), and such a pattern is also reflected in the response surface plot for the methane yield generated using the obtained model (Fig. 4). The estimated methane yield is higher in the higher-XF region and lower in the higher-XU region in the model. Correspondingly, correlation analysis of the experimental results indicated that the methane yield had a strong positive significant correlation (R2 ¼ 0.7487, p ¼ 0.0032) with XF and a strong negative correlation (R2 ¼ 0.8592, p ¼ 0.0002) with XU. The maximum estimated methane yield was 0.547 L/g VS at a mixing ratio of 76.3% FW:23.7% SCG. The synergy index data were also best fitted by a reduced cubic model (Table 3):

below 1 (i.e., antagonistic effect) during the early or mid-incubation periods, with no discernible increasing or decreasing patterns. This could be partially attributed to the outgrowth of sulfate-reducing bacteria, which may have inhibited the growth of slow-growing methanogens in S-rich environments, particularly during the early-to mid-incubation period. The antagonistic effect disappeared with further incubation, and the overall synergy index on day 30 exceeded 1 in the runs. Therefore, it may be more suitable to avoid including a high proportion of Ulva biomass (67%) in the feedstock mixture, particularly if a sufficient reaction (or retention) time is unattainable (20 days). 3.3. Response surface models For a more comprehensive description of the interactions between the feedstocks and the effect of co-digestion, the response surface models for the methane yield and synergy index were constructed using the values determined for the total 30-day incubation period. The fit of linear to cubic polynomials to the experimental data were tested (Fig. 2), and the best-fit model was selected based on its simplicity and significance. A reduced cubic model was selected as the most suitable for illustrating the response surface of methane yield (Table 3):

YM ¼ 0:44XF þ 0:33XS þ 0:15XU þ 0:33XF XS þ 0:16XF XU þ 0:18XS XU þ 0:73XF XS ðXF  XS Þ þ 0:23XF XU ðXF  XU Þ þ 0:34XS XU ðXS  XU Þ (1) where YM is the predicted response of the methane yield and XF, XS, and XU are the proportions of FW, SCG, and Ulva biomass in the feedstock mixtures (between 0 and 1), respectively. The statistical significance of the model and individual terms were tested by analysis of variance (ANOVA). The obtained response surface model exhibited an excellent fit to the experimental data (R2 > 0.99, p < 0.0001), and the coefficient of variation (CV) for the model was very low (3.02%). The adequacy of the model was also confirmed by its high adequacy-of-precision (AP) value of 44.331. AP is a measure of the range of model responses relative to the average estimation error (i.e., signal-to-noise ratio), and it is usually required to be greater than 4 for a model to be considered adequate [24]. Additionally, the normality assumption of the model was evaluated by generating a normal probability plot of residuals for the regression equation (Fig. 3A). The residuals were randomly scattered around a straight line without structure or pattern, indicating that the model errors follow a normal distribution (i.e., constant variance of the

YS ¼ 1:01XF þ 0:99XS þ 0:97XU þ 0:80XF XS þ 0:51XF XU þ 0:71XS XU þ 1:91XF XS ðXF  XS Þ þ 1:15XS XU ðXS  XU Þ (2) where YS is the predicted response of the synergy index and XF, XS, and XU are the proportions of FW, SCG, and Ulva biomass in the feedstock mixtures (between 0 and 1), respectively. The constructed response surface model achieved a good fit to the observed data (R2 > 0.92, p < 0.0168), with the CV being as low as 4.58%. The AP value was 8.769, which is high enough to ensure the model adequacy. The regression residuals were randomly and normally distributed, meaning that the model produces responses with constant variance (Fig. 3B). These suggest that the obtained response surface model (Eq. (2)) can reliably predict the synergy index. In contrast to the model for methane yield, the linear effects of

Table 3 Statistical significance of the response surface models for the methane yield and synergy index. Response

Model

SDa

%CVb

R2

Adjusted R2

PRESSc

p-value

APd

Methane yield

Linear Quadratic Special cubic Cubic Reduced cubice Linear Quadratic Special cubic Cubic Reduced cubice

0.043 0.039 0.041 0.009 0.011 0.120 0.100 0.110 0.039 0.051

12.32 11.02 11.77 2.44 3.02 10.85 9.37 9.86 3.46 4.58

0.8725 0.9287 0.9302 0.9985 0.9969 0.1041 0.5322 0.5558 0.9727 0.9203

0.8470 0.8777 0.8604 0.9940 0.9908 0.0751 0.1981 0.1117 0.8909 0.8087

0.037 0.089 0.110 0.054 0.044 0.290 0.620 0.760 1.100 0.370

<0.0001 0.0007 0.0031 <0.0001 <0.0001 0.5771 0.2774 0.3962 0.0330 0.0168

17.468 13.999 11.933 51.217 44.331 2.290 3.951 3.843 9.157 8.769

Synergy index

a b c d e

Standard deviation. Coefficient of variation (%). Predicted residual sum of squares. Adequacy of precision. Selected best-fit model.

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Fig. 3. Normal probability plots of the regression residuals for the methane yield (A) and synergy index (B) models.

Table 4 Statistical significance of the response surface models' coefficients for the methane yield and synergy index. Methane yield model Terms Linear mixture XFXS XFXU X SX U XFXS(XF e XS) XFXU(XF e XU) XSXU(XS e XU) a

Synergy index model Coefficient 0.33 0.16 0.18 0.73 0.23 0.34

a

SE

F-value

p-value

Terms

0.043 0.043 0.043 0.090 0.090 0.090

568.37 57.76 12.93 18.37 65.55 6.74 14.16

<0.0001 0.0016 0.0229 0.0128 0.0013 0.0603 0.0197

Linear mixture XFXS XFXU X SX U XFXS(XF e XS) XSXU(XS e XU)

Coefficient 0.80 0.51 0.71 1.91 1.15

SE

F-value

p-value

0.21 0.21 0.21 0.43 0.43

3.27 14.94 5.89 11.68 19.55 7.11

0.1238 0.0118 0.0596 0.0189 0.0069 0.0446

Standard error.

Fig. 4. Two- and three-dimensional response surface plots for the methane yield constructed based on the BMP experimental data.

the independent variables (XF, XS, and XU) were not significant (p > 0.05) in the model for synergy index (Table 4). However, all interaction terms, except for XFXU, which represents the quadratic interaction between FW and SCG, were significant (p < 0.05). Although not significant, XFXU exhibited a p-value of 0.0596. XFXU(XF e XU) and XFXSXU, which exhibited too-high p-values (>0.17) in the cubic model, were dropped from the final model. As with the model for methane yield, all interaction terms in the synergy index model have positive coefficients. This demonstrates that the interactions between the feedstocks exert significant synergistic effects on the model response (i.e., enhanced synergy). The interaction terms between XF and XU and those between XS and XU, particularly XFXS(XF e XS) and XSXU(XS e XU), have greater

coefficients. This suggests that mixing FW and SCG or SCG and Ulva incurs a stronger and more direct effect on the synergistic effect of co-digestion than mixing FW with Ulva. Such an effect is well reflected in the response surface plot for the synergy index produced by the constructed model (Fig. 5). Higher model responses occur near the FW-SCG and the SCG-Ulva axes, i.e., in the low-Ulva and low-FW substrate mixtures. The maximum predicted synergy index was 1.335 in the mixture of 73.1% FW:26.9% SCG, which differs from the conditions for the maximum methane yield. It is interesting to note that the co-digestion of SCG and Ulva biomass can achieve a noticeable synergistic effect even without FW (synergy index ¼ 1.231 at 70.8% SCG:29.2% Ulva). This suggests that it is possible to efficiently manage and valorize these problematic

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Fig. 5. Two- and three-dimensional response surface plots for the synergy index constructed based on the BMP experimental data.

wastes that are unsuitable for mono-digestion together through codigestion.

study is required to secure the process feasibility and stability during long-term field application.

3.4. Optimum feedstock mixture: potential implications

4. Conclusions

As seen in Figs. 4 and 5, the response surface models for the methane yield and synergy index exhibit significantly different shapes, suggesting that the model responses are affected in different ways by interactions between the feedstocks. Accordingly, the feedstock mixing ratios for their maximum responses were different, although both were highest during BMP Run 4 (67% FW:33% SCG) (Fig. 2). As the primary benefit of using underused feedstocks for biogas production is the enhancement of the biomass potential for energy applications, maximizing the conversion of each feedstock to methane, particularly those that are not readily biodegradable, is deemed more desirable than simply increasing the methane production from the feedstock mixture by co-digestion. Therefore, this study suggests a mixing ratio that maximizes the synergy index (1.335 at 73.1% FW:26.9% SCG) to be the optimum feedstock mixing ratio for co-digestion. No significant antagonistic effect was observed for co-digestion, with the estimated synergy index being greater than 0.95. This is in accordance with the experimental BMP results, as all test runs exhibited a synergy index greater than 1 (Fig. 2B). These suggest that FW, SCG, and Ulva biomass can be co-digested without compromising the methane potential of the feedstocks at any mixing ratio, and the energy recovery from the feedstocks can be increased in most cases. This offers attractive possibilities for the treatment and conversion of SCG and Ulva biomass to biogas using the spare capacity of an existing FW digester. By using SCG or Ulva biomass as an additional feedstock to the normal FW load according to their availability, the underused waste feedstocks can be valorized. Such an approach avoids the problems related to the high seasonality of the availability of Ulva biomass and suggests a useful strategy to manage it. As marine algal blooms are becoming increasingly severe and perennial in East Asian seas around China and Korea [26], there will be an increasing demand for a method to manage harmful seaweed waste. SCG, which is free from seasonal variations, may serve as a co-feedstock to compensate for fluctuations in the organic load and increase the energy recovery from feedstocks during co-digestion with FW and/or Ulva biomass. To practically apply the proposed co-digestion strategy, the process resilience to repeated fluctuations in feedstock characteristics and organic load in the continuous mode should be examined. Further

The anaerobic co-digestion of FW, SCG, and Ulva biomass was examined as an approach to the efficient valorization of underused waste feedstocks and increase the biomass potential for energy use. The models describing the effects of the feedstock mixing ratio on the methane yield and synergy index were successfully constructed using a ternary mixture design by response surface analysis. The obtained models generated significantly different response surfaces, and the predicted maximum responses of the methane yield (0.547 L/g VS at 76.3% FW:23.7% SCG) and synergy index (1.335 at 73.1% FW:26.9% SCG) were observed at different mixing ratios. All experimental BMP runs exhibited a synergy index greater than 1, and no modeled synergy index was below 0.95. This suggests that the co-digestion of FW, SCG, and Ulva biomass has no significant antagonistic effect on methane production and can synergistically enhance the methane yield in most cases. The overall results show that co-digestion can be a promising strategy of effectively managing waste feedstocks and increasing their potential as energy resources. Acknowledgements This research was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) through the “Human Resource Program in Energy Technology” (No. 20184030202250), funded by the Ministry of Trade, Industry and Energy, Republic of Korea and the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education of the Republic of Korea (No. 2016R1A6A3A11934571). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.renene.2019.01.046. References [1] D.R. Vardon, B.R. Moser, W. Zheng, K. Witkin, R.L. Evangelista, T.J. Strathmann, K. Rajagopalan, B.K. Sharma, Complete utilization of spent coffee grounds to produce biodiesel, bio-oil, and biochar, ACS Sustain. Chem. Eng. 1 (10) (2013) 1286e1294.

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