High-solid anaerobic co-digestion of pig manure with lignite promotes methane production

Journal of Cleaner Production 258 (2020) 120695

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High-solid anaerobic co-digestion of pig manure with lignite promotes methane production Hai-Gang Guo a, b, c, Qing-Lin Chen b, Hang-Wei Hu b, Ji-Zheng He b, * a

College of Life Sciences and Food Engineering, Hebei University of Engineering, Handan, 056038, China Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Victoria, 3010, Australia c Huayu Agricultural Science and Technology Co., LTD, Handan, 057153, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 November 2019 Received in revised form 15 January 2020 Accepted 19 February 2020 Available online 21 February 2020

High-solid anaerobic digestion is a common practice to treat organic wastes, but its efficiency of methane production is poor. In this study, the effect of high-solid anaerobic co-digestion (HS-AcoD) of pig manure with lignite (0%e64% based on total solid) on methane production at 37
C was investigated. The results showed that pH, alkalinity, total ammonia nitrogen, and free ammonia decreased with the increasing ratios of lignite addition during HS-AcoD. After HS-AcoD for 30 days, the highest cumulative methane yield of 175.90 mL g1 volatile solid was obtained for the treatment with 2% lignite addition, which was 9.26% higher compared with the control treatment without lignite addition (CK). However, the highest cumulative methane yield (the 8% lignite addition treatment) was 36.51% higher than CK after HS-AcoD for 12 days. The modified Gompertz model was fitted well to the methane production (R2  0.9863). The synergistic effects of HS-AcoD with lignite ranged from 8.84 to 14.58% when lignite addition accounted for 2e50%. Structural equation models indicated that lignite mainly represented the indirect effect on cumulative methane yield by impacting C/N, pH and total ammonia nitrogen during HS-AcoD. Highthroughput sequencing demonstrated that HS-AcoD of pig manure with lignite highly enhanced the relative abundance of Methanosaeta for methanogenesis. Overall, the results suggested that lignite addition could enhance the methanogenes’ activities and improve the methane production during HSAcoD of pig manure with lignite. © 2020 Published by Elsevier Ltd.

Handling editor: Prof. S Alwi Keywords: High-solid anaerobic co-digestion Pig manure Lignite Kinetic analysis Synergistic effects

1. Introduction The massive production of pig manure in modern intensive pig feeding has become a growing concern, due to high amounts of organic compounds, manure-borne pathogens, bad odor emission and veterinary antibiotics in livestock industry (Xie et al., 2017; Hu et al., 2019; Zhang et al., 2019a). Land application of organic waste in agricultural soils is a common practice, however, pig manure should be treated to reduce pollution to the environment before land application. Anaerobic digestion has been suggested as a sustainable and environment-friendly method to convert organic waste into a renewable energy and high quality fertilizers (Vrieze et al., 2019; Sun et al., 2019). Substantial efforts have been devoted to yield more methane from the anaerobic digestion of pig manure (Shen et al., 2018; Vrieze et al., 2019). Traditionally, the

* Corresponding author. E-mail address: [email protected] (J.-Z. He). https://doi.org/10.1016/j.jclepro.2020.120695 0959-6526/© 2020 Published by Elsevier Ltd.

process of anaerobic digestion was called low solid anaerobic digestion, as the total solid content (TS) in substrates was less than 15% (Zhang et al., 2015). Compared with low solid anaerobic digestion, high-solid anaerobic co-digestion (HS-AD) (the total solid content  15%) has many advantages, such as smaller reactor volume, less energy input for heating, minimal material handling (Zhang et al., 2015), and easy for transportation and land application (Salman et al., 2017). The mono-digestion of manure is known to have low efficiency and low stability of methane production, due to the ammonia inhibition and low C/N ratio of substrates (Rajagopal et al., 2013), which might be resolved by adding carbon rich co-substrates. Anaerobic co-digestion, the simultaneous anaerobic digestion of two or more substrates, is able to increase methane yields, shorten the lag phase, and improve system stability and economic viability of anaerobic digestion, compared with mono-digestion (MataAlvarez et al., 2014; Xie et al., 2016; Md. Nurul and Zularisam, 2018). Numerous studies have reported about anaerobic codigestion of pig manure with other substrates. For example, Xie

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et al. (2017) found that anaerobic co-digestion of pig manure with grass silage improved system stability and biogas yields. The significant synergistic effects were observed in the co-digestion of durian shell and pig manure at 1 : 1 and 1 : 3 ratios, respectively (Shen et al., 2018). In addition, a food waste/pig manure ratio of 50 : 50 led to stable dry co-digestion (TS of substrate > 20%) with a specific methane yield of 252 mL$g1VSadded (Jiang et al., 2018). Moreover, HS-AcoD of pig manure with rice straw pretreated by alkaline and alkaline-microwave increased biogas yields by around 25% (Qian et al., 2019). Therefore, anaerobic co-digestion of pig manure with other substrates could be an effective method to promote methane production. Lignite is a natural adsorption material (Li et al., 2015), and had better adsorption capacity than available active carbon for the treatment of organic compounds (Aivalioti et al., 2012; Hassani et al., 2014). Abundant pore materials could provide sufficient habitats for the microbes which play an important role in enhancing the digestibility, alleviating ammonium inhibition and shortening the digestion startup time (Fagbohungbe et al., 2017;
bio et al., 2018a and 2018b). For example, anaerobic digestion Fa with biochar addition improved the cumulative methane yield
bio et al., 2018b). Lignite (CMY) and had a fast build-up phase (Fa has a similar structure to biochar but is cheaper. Cellulose, lignin, humins and humic acids are commonly present in lignite, which could be degraded to methane by microbial communities under anaerobic conditions (Park and Liang, 2016; Detman et al., 2018). Lignite has low pH (Sun et al., 2016a) and decreases the free ammonia, which can help reduce ammonium inhibition during anaerobic digestion. However, lignite as the mono-substrate for methane generation is economically unprofitable, because it is recalcitrant to biodegradation (Detman et al., 2018) and requires mixing with external substrates (Yoon et al., 2016; Bucha et al., 2018). The C/N ratio of lignite is approximately 50e80 (Yoon et al., 2016; Wang et al., 2017), while the C/N ratio of pig manure is approximately 10. Carbon-rich substrates could improve the C/N ratio of manure, increase stability and create a suitable culture for digestion (Yangin-Gomec and Ozturk, 2013). Therefore, lignite might be a suitable compromise in terms of sustainability and performances for AcoD of pig manure. The application of lignite for methane production has been reported previously (Wang et al., 2017; Bucha et al., 2018), but the synergistic effects of anaerobic co-digestion of pig manure with lignite have never been investigated. Here, modified Gompertz model and structural equation models (SEMs) were used to analyze biodegradation kinetics and synergistic mechanism during highsolid anaerobic co-digestion (HS-AcoD) of pig manure with lignite. The objectives of this study were to: (1) evaluate the stability and performance of HS-AcoD of pig manure under varying lignite/pig manure ratios; (2) analyze the kinetic mechanism of HSAcoD of pig manure and lignite; and (3) explore the synergistic mechanism for HS-AcoD of pig manure and lignite. 2. Materials and methods 2.1. Inocula and substrates Pig manure was obtained from piggery at Berry Bank farm located at Windermere, Victoria, Australia, which had a standing swine population of 20,000 pigs. The inocula were collected from a large-scale anaerobic digester in the same piggery. Both materials were preserved in sealed buckets at a 4
C before use. Lignite was collected from Bacchus Marsh coal mine in Australia. Prior to the beginning of high solid anaerobic co-digestion, the inocula were centrifuged and the supernatant was removed. The detailed characteristics of inocula, pig manure and lignite are listed in Table 1.

2.2. High solid anaerobic co-digestion Batch experiments were conducted in 150 mL pressure bottles as digestion reactors. Each reactor contained a total of 18.75 g substrates mixed with 6.25 g inocula, equivalent to approximately 15% total solid. Six replicate reactors were operated for each treatment, and The details are shown in Table 2. Before HS-AcoD, oxygen in reactors was removed by exchanging it with nitrogen gas for 5 min, then reactors were sealed with butyl rubber stoppers, and incubated at 37 ± 1
C for HS-AcoD for 30 days. Volumes of daily produced biogas were measured using a 50 mL injector and three reactors for each treatment were selected randomly for destructive sampling at days 12 and 30 to measure the parameters of solid samples. The measured biogas volumes were adjusted to the volumes at standard temperature (0
C) and pressure (101.325 kPa). The gas samples were injected into 12 mL sampling bottles and the solid samples were stored at 20
C before measurement. A control treatment containing inocula only was carried out in triplicate to correct samples for the methane yield generated by inocula only. 2.3. Analytical methods and calculation TS, volatile solid (VS), and alkalinity were determined using the standard methods (APHA, 2005). Dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) were measured with the total organic carbon analyzer (TOC-L, SHIMADZU). Total ammonium nitrogen (TAN) was analyzed with a segmented autoanalyser (Skalar SANþþ, Skalar, Breda, The Netherlands), and pH was determined using a digital pH meter (smartCHEM-Lab, TPS). Total nitrogen (TN) and total carbon (TC) were measured with analizator elementarny TruMac CN (LECO Corp. USA). Biogas concentrations were analyzed by gas chromatography (7890A, Agilent Technologies, Wilmington, USA) equipped with a thermal conductivity detector. The free ammonia (FAN) concentration was calculated using Eq. (1) (Zhang et al., 2017):

CFAN ¼

CTAN 2729:92 0:09018þ273:15þT pH

10

þ1

(1)

Where T is temperature (
C). An equation was used to quantify the synergistic or antagonistic effects of co-digestion on bioenergy recovery as follows (Xie et al., 2017; Du et al., 2019):

a¼ b¼

CMYPM  A þ CMYL  B CMYco  ðA þ BÞ 1a

a

 100%

(2)

(3)

where CMYPM (cumulative methane yield), CMYL, and CMYco are the CMYs of pig manure, lignite and the mixed substrates, respectively (mL CH4$g1 VS); A and B are the corresponding mass of VS fraction of PM and lignite in mixed substrates (%), respectively; a is the synergism coefficient; b is the synergistic effect in a percentage. When a less than 1 and b greater than 0 indicate a synergistic effect, while a greater than 1 and b less than 0 suggest an antagonistic effect during co-digestion. 2.4. Kinetic model The biogas yield curves reflected the qualitative differences between treatments. Pig manure was readily biodegradable organic material, and the modified Gompertz model could well fit the

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Table 1 Characteristics of lignite, pig manure and inocula. Parameters

Lignite

Pig manure

Inocula

TS (%) VS (%) pH Alkalinity (mg$kg1) TAN (mg$kg1) FAN (mg$kg1) TC (%) DOC (mg$kg1) DIC (mg$kg1) TN (%) C/N

52.51 ± 0.32 88.90 ± 0.42 5.76 ± 0.02 1067.65 ± 11.56 e e 52.66 ± 0.05 597.00 ± 23.46 10.92 ± 0.59 0.62 ± 0.01 84.45 ± 1.13

15.88 ± 0.32 75.79 ± 0.48 6.85 ± 0.03 11726.37 ± 173.30 1036.41 ± 30.78 9.32 ± 0.28 37.03 ± 0.09 1308.75 ± 31.54 683.00 ± 19.37 4.56 ± 0.02 8.13 ± 0.01

15.27 ± 0.08 73.45 ± 0.46 7.59 ± 0.02 11616.08 ± 177.84 1162.07 ± 49.29 55.21 ± 2.34 33.48 ± 0.02 977.00 ± 31.84 666.75 ± 29.28 3.64 ± 0.01 9.19 ± 0.01

Note: total solid (TS), volatile solid (TS), total ammonia nitrogen (TAN), Free ammonia (FAN), total carbon (TC), dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), total nitrogen (TN), carbon to nitrogen (T/N), “-” represents below detection limits. Table 2 Experimental condition with different lignite to pig manure ratios. Treatments

Lignite (g)

Pig manure (g)

Inocula (g)

Deionized water (g)

Lignite/(Lignite þ PM) (%)

CK 2% L 4% L 8% L 16% L 32% L 40% L 50% L 64% L

0.00 0.11 0.23 0.45 0.91 1.82 2.27 2.84 3.63

18.75 18.38 18.00 17.25 15.75 12.75 11.25 9.38 6.75

6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25 6.25

0.00 0.26 0.52 1.05 2.09 4.18 5.23 6.53 8.37

0 2 4 8 16 32 40 50 64

Note: the weight of lignite, pig manure (PM) and inocula is based on wet weight, and Lignite/(Lignite þ PM) is based on TS. 2% L, 4% L, 8% L, 16% L, 32% L and 64% L represent the treatments with different ratios of lignite, respectively.

biogas yield curve for anaerobic digestion (Liu et al., 2016). The duration of the lag phase (l) was an important factor to determine the efficiency of anaerobic digestion. The modified Gompertz model, l was introduced, was a typical ‘S’ style curve equation as the following Eq. (4):

   e$Rmax YðtÞ ¼ Ymax exp  exp ðl  tÞ þ 1 Ymax

(4)

where Y(t) is the cumulative methane yield at time t (mL$g1VS); Ymax is the potential maximum methane yield (mL$g1VS); Rmax is the maximum methane yield rate (mL$g1VS$d1); l is the lag phase (d); t is the duration of the assay (d) and e is the exp(1) z 2.71828.

2.6. Data analysis One-way analysis of variance (ANOVA) was applied to compare the difference using SPSS25.0 software (SPSS, USA). Statistical difference was assumed significant at P < 0.05 with Tukey test. The graphing and modelling processes were implemented in Origin 8.6 (OriginLab, Northampton, Massachusrtts, USA). Principal component analysis (PCA) and redundancy analysis (RDA) was performed using Canoco version 5.0. Cumulative methane yield and the influencing factors (lignite, pH, etc.) were used for the structural equation models (SEMs) analysis by the Amos 22.0 (SPSS, USA). Based on the taxonomic assignments, the relative abundance of archaea was determined at the genus level. 3. Results and discussion 3.1. Effects of HS-AcoD on process stability

2.5. High-throughput sequencing Samples were collected for DNA extraction using the Fast DNA® Spin Kit for Feces (MP Bio, USA) according to the manufacturer’s instructions. The spectrophotometer analysis (Thermo Fisher) and 1.0% agarose gel electrophoresis were used to check concentration and quality of the extracted DNA. DNA was stored at 20
C before measurement. High-throughput sequencing of the bacterial 16S rRNA gene was performed on the Illumina HiSeq platform. The hyper-variable V4 region of the bacterial 16S rRNA gene was amplified using the universal primers 515F and 806R (Caporaso et al., 2010). Raw data were assembled and low-quality sequences were filtered using Quantitative Insights into Microbial Ecology (QIIME) software. The remaining sequences were clustered into operational taxonomic units (OTUs) at 97% similarity using UPARSE pipeline. Each OTU was taxonomically assigned using the RDP classifier (Wang et al., 2007).

In the nine treatments of HS-AcoD, pH gradually increased from day 0 to day 30, and decreased with the increasing amount of lignite, which could be explained by a low pH of lignite (Fig. 1a). Similar results were previously reported in studies of lignite mixed with rice straw (Yoon et al., 2016). The highest pH was observed in 4% L treatment at day 12, due to the ammonia nitrogen production and the amount of lignite addition. Total alkalinity decreased with the increasing amount of lignite due to low alkalinity of lignite, but increased over time for the nine treatments from day 0 to day 30 (Fig. 1c). This was basically consistent with the change of pH. Yin et al. (2014) found that total alkalinity in pig manure increased from 5,308 to 7,746 mg CaCO3$kg1 in the first 29 days of anaerobic digestion, which was similar to this study. The contour plot of pH and alkalinity at the end of HS-AcoD (Fig. 1b and d), suggested that the trend of alkalinity with different amounts of pig manure and lignite was similar to that of pH.

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Fig. 1. pH changes (a), the contour plot of pH at the end of HS-AcoD (b) was drawn using data of Fig.1a (Day 30) for different treatments. Alkalinity changes (c), the contour plot of alkalinity at the end of HS-AcoD (d) was drawn using data of Fig.2a (Day 30) for different treatments Note: The letters (a, b, c … …A, B, C … …a, b, c … …) represent the significant difference (P < 0.05) among different treatments.

Ammonia nitrogen is vital for the stability of anaerobic digestion, and high ammonia nitrogen could inhibit the activity of microbes (Yangin-Gomec and Ozturk, 2013; Hu et al., 2019). If the TAN concentration reached the range of 1,500e3,000 mg L1 with pH above 7.4, it can result in ammonia inhibition. When TAN is higher than 3,000 mg L1, ammonia nitrogen is toxic irrespective of pH (Abouelenien et al., 2010). In this study (Fig. 2a), TAN was below 1,059.48 mg kg1 for all treatments at day 0; while it was higher than 1,500 mg L1 for CK, 2% L, 4% L, 8% L and 16% L at day 12 and 30, therefore, ammonia inhibition possibly occurred in the experiment. TAN significantly decreased with the increasing amount of lignite at day 0, 12, and 30, which might be due to the fact that pig manure contained a high level of protein and lignite had a low level of protein. TAN significantly increased during HS-AcoD for all treatments from day 0 to day 12, and slowly increased in all treatments after day 12. The results were in consistence with a previous study (Meng et al., 2018). FAN was one part of TAN and calculated by temperature, TAN and pH according to Eq. (1) (Zhang et al., 2017). FAN gradually decreased with the increasing amount of lignite at

day 0 and 30, because of the declining TAN. It was reported that FAN should be lower than the threshold value of 600 mg L1 (Han et al., 2017), and the results in this study were all below the threshold. The contour plot of TAN at the end of HS-AcoD (Fig. 2c) suggested that TAN increased with the increasing amount of pig manure. This tendency was similar to the pH trend (Fig. 1b), indicating that TAN and pH were closely related during HS-AcoD of pig manure with lignite. Methanogens are sensitive to pH changes, in this study pH was suitable for methanogens, because the optimum pH for the meth€ schl anogenesis process was around a neutral range (6.8e8.0) (Po et al., 2010; Hagos et al., 2017). Buffering capacity (alkalinity in anaerobic digestion) provided resistance to large and rapid changes in pH when volatile organic acids (VFAs) or ammonia nitrogen accumulated during anaerobic digestion (Sun et al., 2016b), and helped maintain optimal biological activity and anaerobic digestion stability (Hu et al., 2019). Pig manure is rich in protein and could release ammonia nitrogen from the hydrolysis of organic matter during anaerobic digestion (Duan et al., 2019). The increase of pH

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during HS-AcoD. Ammonia nitrogen existed in the equilibrium of ionized ammonium and free ammonia (FAN) in the anaerobic digestion system, depending on pH (Glanpracha and Annachhatre, 2016). According to Henry’s law, the higher concentration of FAN, the greater loss of FAN, it could be stripped out by the fine bubbles formed by biogas (Yang et al., 2018), resulting in the reduction of total nitrogen in substrates with pH increase during HS-AcoD. Therefore, lignite addition could reduce pH and FAN during HSAcoD, which likely enhances the preserved N in digestate and benefits land application. During anaerobic digestion, the particulate organic matter was hydrolyzed into soluble organics, converted into organic acids, and finally converted into methane and carbon dioxide (Weiland, 2010; Glanpracha and Annachhatre, 2016). There were no significant differences in the DOC concentration among the treatments of CK, 2% L, 4% L and 8% L and among the treatments of 2% L, 4% L, 8% L and 16% L at day 0 (Fig. 3a). At day 12 for all treatments, the DOC concentration gradually decreased with the increasing amount of lignite (Fig. 3b). The DOC concentration at day 30 decreased in CK, 2% L and 4% L compared with that at day 12, but the other treatments had no obvious change between day 12 and 30 (Fig. 3c). The major organic carbon during anaerobic digestion included proteins, carbohydrates and VFAs (Weiland, 2010). The decrease of DOC for all treatments from day 12e30 may be attributed to the following reasons: (1) there was low DOC in lignite and high DOC in pig manure; (2) the hydrolysis acidification of substrates gradually weakened at the middle and end stage of HS-AcoD, but DOC was still being degraded to produce methane. DIC was derived from the hydrolysis of inorganic carbon and degradation of organic carbon during anaerobic digestion. The DIC concentration showed a decreasing tendency with the increasing amount of lignite at days 0, 12 and 30 (Fig. 3), which was attributed to the high DIC in pig manure and the low DIC in lignite. For CK, 2% L and 4% L, there were no significant differences for DIC at days 0, 12 and 30, because the initial DIC concentration and cumulative methane yield had no obvious gap for the three treatments. DIC gradually increased from day 0e30 for CK, 2% L, 4% L, 8% L, 16% L and 32% L. DIC was not utilized by anaerobic microbes, leading to its increasing concentration. There was no obvious change in DIC for 40% L, 50% L and 64% L, because CMY was low with the high lignite, resulting in low degradation of organic carbon. 3.2. The performance of daily and cumulative methane yield

Fig. 2. TAN (a), FAN (b), and the contour plot of TAN at the end of HS-AcoD (c) was drawn using data of Fig.3a (Day 30) for different treatments. Note: The letters (a, b, c … …A, B, C … …a, b, c … …) represent the significant difference (P < 0.05) among different treatments.

during anaerobic digestion can be attributed to ammonia nitrogen production, consumption of VFAs and dissolution of CO2 produced by methanogenic bacteria (Lin et al., 2013). The high alkalinity level was a reflection of the high degradation of nitrogenous organics during anaerobic digestion (Aboudi et al., 2015), resulting in high ammonia nitrogen and high pH, which help to maintain a neutral pH and consequently prevent VFAs inhibition that may occur

The overall trends for the daily methane yield (DMY) were similar for all treatments (Fig. 4). It rapidly started at the beginning of HS-AcoD, and sharply increased until reaching the peak value and then rapidly decreased until reaching the steady value. This finding was similar to the results of anaerobic digestion of swine manure at TS contents of 14.18% and 17.60% (Hu et al., 2019), and the results of anaerobic digestion of pig manure at VS content of 2% (Shen et al., 2018). The highest DMY was 23.52 mL$g1VS$d1 (16% L) for all treatments, but 13.57 mL$g1VS$d1 for CK was lower than that previously observed Shen et al. (2018). The day reaching to the highest DMY gradually declined with the increasing amount of lignite from 11 to 6 days. This was probably because DOC concentration in the substrate decreased with the increasing amount of lignite (Fig. 3). The cumulative methane yield (CMY) increased rapidly at the first 12 days but then gradually slowed down (Fig. 4). The majority of methane was generated in the first 12 days of HS-AcoD for all treatments (Fig. S1a), which was similar to Shen et al. (2018). The CMY first enhanced and then decreased with the increasing amount of lignite. HS-AcoD of pig manure with 64% lignite resulted in the lowest CMY among all treatments during 12 and 30 days.

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manure with lower lignite (2% L, 4% L and 8% L) increased methane production compared with mono-pig manure digestion (CK) and mono-lignite digestion (CMY was zero, data not shown) during 30 days. Although lignite had an inherently lower CMY than pig manure, the addition of lignite did not negatively affect CMY when lignite amount was lower than 16%. It was reported that reducing the FAN in the substrate could enhance CMY (Montalvo et al., 2012), which was consistent with this study (CK, 2% L, 4% L and 8% L). In addition, CMY for CK was 161.00 mL$g1VS in this study, almost equal to the finding in which mono-digestion of pig manure was conducted at 20% TS and the inocula rates set at 25% based on VS (Jiang et al., 2018). The contour plot of CMY at the end of HS-AcoD (Fig. S1b) suggested that the trend of CMY variation with different amount of pig manure and lignite was similar to that of pH and alkalinity. 3.3. Kinetic analysis

Fig. 3. DOC and DIC concentration for different treatments during HS-AcoD. Note: The letters (a, b, c … …A, B, C … …) represent the significant difference (P < 0.05) among different treatments.

However, the highest CMY was generated in 8% L during 12 days, and in 2% L and 4% L during 30 days. During 12 day HS-AcoD, CMY for 2% L, 4% L, 8% L and 16% L increased by 20.14%, 26.99%, 36.51% and 18.44% compared with CK, respectively; while during 30 day HS-AcoD, CMY for 2% L, 4% L and 8% L increased by 9.26%, 7.62% and 3.97% compared with CK, respectively. The CMY for the treatments (2% L, 4% L, 8% L and 16% L) were higher than CK during the first 12 days of anaerobic digestion, but lower than CK during the rest of 18 days, because more DOC in the substrate of CK resulted in the increase of CMY than other treatments. Overall, HS-AcoD of pig

Understanding the kinetics of anaerobic digestion of substrates is crucial to optimizing the design of full-scale continuous systems. Mathematical models allowed for a quantitative description of methane generation rate, and this information can also be used to identify the rate limiting process in anaerobic digestion (Dennehy et al., 2016). Based on the results from DMY and CMY, the modified Gompertz model was applied to simulate the methane production process in HS-AcoD of pig manure with lignite (Table 3). The correlation coeficient factors (R2) in the modified Gompertz ranged from 0.9863 to 0.9948 across all treatments, which indicated that HS-AcoD process of pig manure and lignite could be fitted well using the model. The Ypresented values obtained from the model were all close to the corresponding Ymeasured, and the differences were lower than 6.12% for all treatments, implying that the model could well simulate CMY (Fig. 4). For the higher lignite treatments (32% L, 40% L, 50% L, and 64% L), the differences were more obvious than other treatments, which may be attributed to the higher DMY than the predicted DMY at the later period of HS-AcoD. The Ymax values from the model were close to the corresponding Ymeasured, indicating that the model could well simulate the potential max CMY. However, the Rmax values ranged from 3.99 to 17.13 mL$g1VS$d1 obtained from the model, and were lower than the Rmeasured, suggesting that this model could not fit well to the Rmeasured. This result was consistent with the results from other reports using the same model (Shen et al., 2018), because the DMY sharply declined when reaching to the DMYmax (Fig. 4). In the model, l ranged from 2.15 to 3.36 d (Table 3), which implied the fast starting for HS-AcoD treatments. The l also indicated the buffering capacity of anaerobic digestion system, the shorter l implied the better buffering capacity (Zhai et al., 2015). Although l of 2% L, 4% L and 8% L was longer than others, their CMY were also higher. The results maybe that all l values were close to each others. The process parameters and kinetic parameters were used for principal components analysis (PCA) (Fig. 5a). PCA1 and PCA2 explained 95.04% of the total variations of the process parameters (Fig. 5a). The changes of the process parameters could be divided into three stages in the PCA analysis, including the beginning phase, methanogenesis phase and the end of HS-AcoD. This result was similar to previous finding (Zhang et al., 2019b). Along with HSAcoD going on, the scores for the process parameters based on PCA1 gradually increased; while with the increasing amount of lignite, the scores for the process parameters at the same time samples gradually decreased based on PCA1. These results indicated that the concentration changes of the process parameters were under the interaction between HS-AcoD time and lignite amount. The samples at day 0 clustered and separated at days 12

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Fig. 4. Daily methane yield, cumulative methane yield, and simulative cumulative methane yield from modified Gompertz model for different treatments during HS-AcoD.

Table 3 Experimental measured CMY (Ymeasured), max DMY (Rmeasured), and outputs from the modified Gompertz model. Parameters

Trials

1

Ypredicted (mL$g VS) Ymeasured (mL$g1VS) Difference (%) Ymax (mL$g1VS) Rmax (mL$g1VS$d1) Rmeasured (mL$g1VS$d1) l (d) R2

CK

2% L

4% L

8% L

16% L

32% L

40% L

50% L

64% L

163.37 161.00 1.47 165.83 11.66 13.57 2.79 0.9948

178.67 175.90 1.57 179.89 14.88 17.51 3.36 0.9912

172.49 173.27 0.45 173.31 15.1 18.70 3.16 0.9920

165.27 167.40 1.27 165.52 17.13 21.53 3.25 0.9925

144.84 148.58 2.52 145.01 15.19 23.52 2.78 0.9952

107.94 114.77 5.95 108.00 12.14 17.16 2.15 0.9908

95.30 100.41 5.08 95.36 10.57 13.76 2.27 0.9944

76.24 80.64 5.46 76.33 7.95 10.65 2.50 0.9929

43.69 46.54 6.12 43.83 3.99 6.05 2.92 0.9863

and 30, implying that the influence of lignite on the process parameters was obvious during HS-AcoD. In addition, compared with other samples, the samples of CK, 2% L, 4% L and 8% L at day 30 contained higher DIC, TAN and FAN. The process parameters of the samples at day 30 and kinetic parameters were used for redundancy analysis (RDA), finally the orders with the highest explanation scores were selected. RDA indicated that kinetic parameters were significantly influenced by process parameters (Fig. 5b), with RDA1 and RDA2 explaining 99.99% of the differences in kinetic parameters, which was consistent with previous reports (Mao et al., 2019). The process parameters except the lignite were positively correlated with Ymax and Ypre, and negatively correlated with l and Rmax. On the contrary, there was a negative correlation between the lignite and other process parameters. Among the process parameters, pH, FAN

and lignite explained 70.5%, 26.3% and 1.7% variation, respectively. The process parameters except lignite were positively correlated and highly presented in CK, 2% L and 4% L. The scores for the kinetic parameters from 32% L to 64% L based on RDA1 and RDA2 increased gradually, indicating that the lag time increased and the maximum methane yield rate decreased from 32% L to 64% L. 3.4. Synergistic effects of HS-AcoD In this study, CMYL was zero because lignite was recalcitrant to biodegradation (Detman et al., 2018) and the pH was only 5.98 at the beginning of HS-AcoD for the mono-lignite digestion (data not shown). The synergism coefficient a was 0.8939, 0.8859, 0.8728, 0.8857, 0.9039, 0.9089, 0.9188 and 1.1212; while b was 11.87%, 12.88%, 14.58%, 12.91%, 10.64%, 10.21%, 8.84% and 10.81% for 2% L,

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Fig. 5. PCA (a) based on the process parameters with all samples, and RDA (b) based on the process parameters with the samples at day 30 (red arrows) and kinetic parameters (blue arrows),Ypre (the predicted cumulative methane yield). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

4% L, 8% L, 16% L, 32% L, 40% L, 50% L and 64% L, respectively. These results indicated that there were synergism effects at low level of lignite, and an antagonistic effect occurred for 64% L. The max synergistic effect with b of 14.58% occurred at 8% lignite. However, the lowest l (lag phase) was 2.15 d for 32% L (Fig. S2a), and the highest peak daily methane yield was 23.52 mL g1 VS$d1 for 16% L (Fig. S2b). The max synergism effect did not occur for the lowest l of 32% L. The superficial reason for this result was that the time of peak daily methane yield was ahead and lower peak daily methane yield (Fig. S2b); the underlying mechanism might be due to the low performance of mono-lignite digestion, which resulted in the limited influence caused by the addition of lignite. Therefore, HSAcoD of pig manure with lignite could increase the efficiency of anaerobic digestion by increasing the DMY and reducing the effective biogas production period (Xie et al., 2011). 3.5. Synergistic mechanisms of HS-AcoD Structural equation models (SEMs) was used to quantify the relative contribution of the process parameters to the changes of CMY (Fig. 6). Lignite, digestion time, C/N, pH and TAN together explained 96.2% of the change of CMY. Digestion time indirectly impacted CMY by significantly affecting pH (l ¼ 0.771***, P < 0.001) and TAN (l ¼ 0.416***, P < 0.001). Lignite directly and significantly impacted CMY (l ¼ 0.742***, P < 0.001) and indirectly impacted CMY by significantly affecting C/N (l ¼ 0.976***, P < 0.001), pH (l ¼ 0.246*, P < 0.05) and TAN (l ¼ -0.708***, P < 0.001). C/N (l ¼ -0.45*, P < 0.05), pH(l ¼ 0.539***, P < 0.001) and TAN(l ¼ 0.551***, P < 0.001) indirectly impacted CMY. These results indicated that an important role of lignite during HS-AcoD of pig manure with lignite. In addition, SEMs can be used to estimate the strength of these multiple (direct and indirect) effects, including the standardized direct effect, indirect effect and total effect (Fig. 6). Digestion time had the most dominant positive effects on CMY, followed by TAN and pH, while lignite and C/N had negative total effects on CMY. These results suggested that higher lignite inhibited gas production. The most negative indirect effect was 1.034 for lignite, which indicated that adding lignite changed the micro-environment (C/N, pH and TAN) during HS-AcoD, resulting in the change of CMY.

The changes of archaea in genus for methanogenesis in all treatments are shown in Fig. 7. Methanosaeta, using acetic acid as the substrates for methane production (Welte and Deppenmeier, 2014), was the main archaeal genus in all treatments. The HSAcoD of pig manure with lignite resulted in more Methanosaeta in their digestate compared with that the mono-digestion of pig manure at days 12 and 30, except for the 64% L at day 12. The relative abundance of archaea in all treatments was similar to that of methanosaeta. The relative abundance of archaea at day 30 for the HS-AcoD of pig manure with lignite was significantly higher than that at day 12 (1.95%e21.42%), and that for CK was the lowest at day 30 in all treatments (1.95%). Methanofastidiosum was able to produce H2 using methylotrophic methanogenesis (Evans et al., 2015). The relative abundance of Methanofastidiosum increased for lignite treatments compared with CK, suggesting that lignite may enhance the hydrogen production during HS-AcoD. Although lignite could increase the relative abundance of methanogenic microbes, no significant changes in the absolute abundances of them were observed (data not shown), which may explain why CMY did not increase with the increasing amount of lignite. The synergistic effect for HS-AcoD of pig manure and lignite was largely attributed to a higher DMY at the early digestion (Fig. 4), which was caused by direct effects of lignite on the pH, C/N and TAN. The pH was the important parameter reflecting the stability of the system and affecting the activity of methanogenic bacteria (Hagos et al., 2017). Too low C/N (<20 in this study) in the substrates could result in a release of excessive ammonia in the digester, which was potential inhibitor in the anaerobic digestion process and would decrease the activity of methanogens (Dennehy et al., 2017; Hu et al., 2019). In this study, lignite addition at low level had synergistic effects because of increasing the C/N and reducing the TAN in the co-digestion substrates (Xie et al., 2017); while lignite at high level had negative effects occurred because of lower nutrition (e.g. DOC) (Fig. 3). Moreover, DOC concentrations did not increase with the increase of lignite mass, but the relative abundance of archaeal genera increased after adding lignite, therefore, the positive synergistic effects and higher DMY might be due to the improvement of methanogenic activities, instead of hydrolysis and acidification.

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Fig. 6. Structural equation models (SEMs) showing the direct and indirect effects of lignite, digestion time, C/N, pH and TAN on CMY. The numbers adjacent to the arrows are standardized path coefficients and indicative of the effect size of the relationship. The width of the arrows is proportional to the strength of the path coefficients. R2 indicates the proportion of variance explained by the variable. Significance levels represent: *P < 0.05,**P < 0.01 and ***P < 0.001. Standardized effects (direct, indirect and total effects) are derived from SEMs. The hypothetical models fit our data well: c2/df ¼ 0.71, P ¼ 0.54, GFI ¼ 1.00, and RMSEA ¼ 0.00.

4. Conclusions This study evaluated the synergistic effects of HS-AcoD of pig manure with lignite on the methane production. Most of process parameters decreased with the increasing amount of lignite, but increased during HS-AcoD except DOC. Low lignite amount increased and high lignite amount decreased CMY compared with CK. CMY for 8% L was highest during the first 12 days of digestion, while 2% L was highest during 30 days digestion. Lignite could

enhance the methane production at the early stage of HS-AcoD. The modified Gompertz model could fit well to the methane production. The improvement of methanogenic activities induced by lignite impacting micro-environment (C/N, pH and TAN) was the most important reason of the synergistic effects, resulting in the increase of CMY. The findings confirmed the superiority of HS-AcoD of pig manure with lignite can greatly accelerate the methanogenesis process.

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Fig. 7. Changes of archaea in genus for five treatments at day 12 (a) and day 30 (b), the relative abundance of archaea accounted for the percentage of the bacteria and archea in total.

Declaration of competing interest We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “High-solid anaerobic codigestion of pig manure with lignite promotes methane production”. CRediT authorship contribution statement Hai-Gang Guo: Conceptualization, Methodology, Data curation, Formal analysis, Visualization, Investigation, Writing - original draft, Writing - review & editing. Qing-Lin Chen: Writing - review & editing. Hang-Wei Hu: Writing - review & editing. Ji-Zheng He: Writing - review & editing, Supervision. Acknowledgements This study was supported by National Natural Science Foundation of China (41907206), the Australia-China Joint Research Centre of Healthy Soils for Sustainable Food Production and Environmental Quality (ACSRF48165), and Key Research and Program of Hebei (31823701D). We acknowledge the Melbourne Trace Analysis for Chemical, Earth and Environmental Sciences (TrACEES), The University of Melbourne for analytical support. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2020.120695. References
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