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Performance and microbial community variations of a upflow anaerobic sludge blanket (UASB) reactor for treating monosodium glutamate wastewater: Effects of organic loading rate
Journal of Environmental Management 253 (2020) 109691
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Research article
Performance and microbial community variations of a upflow anaerobic sludge blanket (UASB) reactor for treating monosodium glutamate wastewater: Effects of organic loading rate Hong Chen a, b, c, Yanxiao Wei a, Peng Liang a, Chunyan Wang d, Yingbing Hu a, Min Xie a, Yiyu Wang a, Benyi Xiao b, *, Chunyan Du a, Hong Tian e a
Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, School of Hydraulic Engineering, Changsha University of Science & Technology, Changsha, 410004, China b Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China c Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan d Department of Biology and Chemical Engineering, Nanyang Institute of Technology, Nanyang, 473004, China e School of Energy & Power Engineering, Changsha University of Science & Technology, Changsha, 410114, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Anaerobic digestion Energy recovery Methanogenic degradation Specific methanogenic activity Up-flow anaerobic sludge blanket Wastewater treatment
To investigate the effect of the organic loading rate (OLR) on anaerobic treatment of monosodium glutamate (MSG) wastewater, a lab-scale up-flow anaerobic blanket (UASB) reactor was continuously operated over a 222day period. The overall performances of COD removal and methane recovery initially exhibited an increase and subsequently decreased when the OLR was increased from 1 g-COD/L/d to 24 g-COD/L/d. At the optimal OLR of 8 g-COD/L/d, superior performance was obtained with a maximum COD removal efficiency of 97%, a methane production rate of 2.3 L/L/d, and specific methanogenic activity of 86 mg-CH4/g-VSS/d (feeding on glutamate), with superior characteristics of sludge in VSS concentration, average diameter of granules, and settling velocity. According to the results of the specific methanogenic activity, the methanogenic pathway was more inclined to pass through acetate than through hydrogen. Methanosarcina instead of Methanosaeta, with Methanobacterium and greatly increased Firmicutes, dominated in the UASB reactor after long term operation. These results support that the OLR had a substantial effect on both the treatment and energy recovery efficiency of MSG wastewater as well as on microbial community variations in the UASB reactor.
1. Introduction Anaerobic technology for high-strength organic wastewater treat ment has become a hot topic of study owing to its cost-efficiency, energy recovery, and ability to reduce sludge production and greenhouse gas emissions (Li and Yu, 2016; Tchobanoglous et al., 2014; van Lier, 2008). In order to satisfy the strict requirements and multi-objective demands of environmental conservation, the anaerobic degradation of biode gradable pollutants in wastewater, including those successfully degraded under aerobic or anoxic conditions, has increasingly gaining garnered research interest (Li et al., 2015; Shen et al., 2015; Taki et al., 2019; Yang et al., 2010; Yi et al., 2017). For example, phthalic esters, a group of chemicals widely used as additives during plastic manufacturing, were effectively removed using a continuous up-flow
anaerobic sludge blanket (UASB) reactor (Liang et al., 2007). The biodegradation of N, N-dimethyformamide, which was previously re ported to only be possible under aerobic conditions, could also be ach ieved with complete methanogenic degradation (Kong et al., 2018). In contrast to aerobic degradation in which oxygen is used as a strong electron acceptor (Cheng et al., 2016; Rittmann and McCarty, 2001), anaerobic degradation is more susceptible to environmental factors, such as diverse and complicated electron transfer paths (Ketheesan and Stuckey, 2015; Qiao et al., 2016). Operational parameters, including the organic loading rate (OLR), have great effects on anaerobic removal performance during continuous long-term operation periods (Ketheesan €y, 2008). Moreover, the OLR was and Stuckey, 2015; Sponza and Uluko shown to critically affect methanogenic activity, extracellular polymer content, settling velocity, and granulation of the sludge (Ghangrekar
* Corresponding author. E-mail address: [email protected] (B. Xiao). https://doi.org/10.1016/j.jenvman.2019.109691 Received 26 March 2019; Received in revised form 3 October 2019; Accepted 6 October 2019 Available online 17 October 2019 0301-4797/© 2019 Elsevier Ltd. All rights reserved.
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Journal of Environmental Management 253 (2020) 109691
et al., 2005; Zhou et al., 2007). In anaerobic reactors, the complicated microbial communities and their competitive dynamics are still unclear (Chen et al., 2019; Tian et al., 2015). In addition, anaerobic degradation is currently mainly used for the treatment of hydrocarbon-containing wastewater but not for the treatment of amino acid-containing waste water. Thus, it is of practical significance to research the operational performance and microbial community shifts of the anaerobic process for the treatment of amino acid-containing wastewater. Monosodium glutamate (MSG) wastewater, a typical amino acidcontaining organic wastewater, contains high concentrations of chemi cal oxygen demand (COD), NH4–N, and SO24 that can cause serious environmental and human health issues. It is reported that 2.2 million tons of MSG wastewater (approximately 80% of the total global pro duction) are produced in China annually, and this wastewater has po tential for resource reutilization and can be successfully treated by aerobic methods (Jia et al., 2007; Jiang et al., 2015; Dong et al., 2018). Although MSG wastewater can be treated by physical or chemical methods, only a few studies have focused on the anaerobic treatment of MSG wastewater, and those have primarily focused on treatment feasi bility and the startup stage of the bioreactor (Jia et al., 2007; Tseng and Lin, 1990). Current studies rarely investigate anaerobic performance and process characteristics, as well as the effects of operational param eters on MSG removal. Therefore, further studies on MSG wastewater anaerobic treatment and energy recovery are still urgently needed. To investigate the anaerobic degradation and energy recovery of MSG from wastewater, a laboratory-scale UASB reactor was operated for 222 days with different OLRs, from 1 g-COD/L/d to 24 g-COD/L/d. The effects of OLR on MSG removal, methane production, and sludge char acteristics were evaluated. The removal capacity and the specific methanogenic activity (SMA), as well as the microbial community of the MSG-degrading sludge were also investigated.
Fig. 1. Schematic diagram of the UASB reactor.
1.0 g-COD/L/d and an HRT of 48 h; (II–VIII) OLR stress periods during days 36–73, 74–97, 98–129, 130–153, 154–175, 176–198, and 199–222 under an HRT of 24 h and OLRs of 2.0, 4.0, 6.0, 8.0, 12.0, 16.0, and 24.0 g-COD/L/d, respectively (Table 1). 2.3. Analytical methods The pH of influent and effluent and the temperature of the mixed liquid were measured with a pH meter (PHSJ-3F, Leici) and a ther mometer (TP101, ER-TET46005P), respectively. Volatile fatty acids (VFAs) and biogas composition were analyzed by gas chromatography (GC9790 II, Fuli) with a flame ionization detector and a thermal con ductivity detector using helium as the carrier gas. The alkalinity, COD, TSS, VSS, and ammonia nitrogen were measured according to standard methods (APHA, 2005). Extracellular polymeric substance (EPS) extraction and analysis were carried out mainly based the literature (Basuvaraj et al., 2015; Lu et al., 2015). Proteins (PN) and poly saccharides (PS) were determined by the Lowry procedure and the phenol-sulfuric acid method (Lu et al., 2015; Xiao et al., 2018), respectively. The particle size distribution of the granules taken from the bottom sampling port of the column was determined by sieve analysis (pore sizes of meshes were approximately 3.0, 2.0, 1.0, and 0.5 mm). The mean settling velocity of the granules in the UASB reactor was evaluated by using settling column tests (Lu et al., 2015; Wu et al., 2018).
2. Materials and methods 2.1. Synthetic wastewater and seed sludge Digested sludge taken from the Xinkaipu wastewater treatment plant (WWTP) (Changsha City, China) mixed with anaerobic granular sludge obtained from a UASB reactor of a starch processing WWTP (Kaitian, Hohhot City, China) were used as the seeding cultures (mixed ratio 2:3). The initial granular sludge, consisting of granular pellets with a diam eter of 0.3–3.0 mm and a sedimentation velocity of 1.5–3.0 cm/s, had a mixed liquor volatile suspended solids (VSS) of 30.05 g/L. Analytical reagents, including L-glutamate, were used for synthesis of the MSG wastewater. NaHCO3 was added at a concentration of 1500 mg/L to ensure pH stability. The other constituents of the synthetic wastewater were as follows: K2HPO4, 250 mg/L; KH2PO4, 100 mg/L; KCl, 300 mg/L; MgCl2�6H2O, 50 mg/L; ZnCl2, 0.6 mg/L; NiCl2�6H2O, 0.81 mg/L; CuCl2�2H2O, 0.3 mg/L; (NH4)6Mo7O24�4H2O, 0.65 mg/L; FeCl2�4H2O, 3.56 mg/L; CaCl2, 15 mg/L; and CoCl2�6H2O, 0.4 mg/L.
2.4. Specific methanogenic activity test
2.2. Experimental apparatus and its operation
The specific methanogenic activity (SMA) of the granular sludge sampled from the UASB reactor was evaluated by serum bottle tests using L-glutamic acid, methanol, sodium acetate, propionic acid, butyric acid, valeric acid, and H2–CO2 as substrates. First, an inoculum of 2 g wet sludge, the nutrient solution and buffers with constant target con centrations were successively placed into 120 mL serum bottles. After individually adding the specific substrates of 2000 mg/L (expect for H2–CO2), the vials were subsequently diluted to 80 mL with deoxygen ated distilled water and sealed with rubber stoppers secured by an aluminum crimp. Then, the serum bottles were flushed with nitrogen gas for 5 min to remove oxygen in the headspace and 0.2 mL sodium sulfate solution (2500 mg/L) was injected as the reducing agent to maintain absolutely anaerobic conditions. For feeding with H2–CO2 as a substrate, after the addition of the inoculum, nutrient solution, and buffers, the vial was diluted to 80 mL and sealed, and the headspace of the bottle was
A schematic diagram of the lab-scale UASB reactor is illustrated in Fig. 1. The reactor was made of plexiglass with an effective volume of 6 L. The setup consisted of a main UASB body, a synthetic wastewaterfeeding unit, a temperature control unit, a biogas gauge, and discharge unit. The main body included a rectangular column with a height of 800 mm, an inner diameter of 100 mm, and a three-phase separator mounted at the top. A wet gas meter (LMF-1, Jingzhiye) was installed to monitor the daily biogas. Then, the biogas volume produced under normal conditions was converted to STP (T ¼ 273.15 K, P ¼ 100 kPa) using the ideal gas equation. The operational temperature was maintained at 35 � 2 � C by the temperature control unit. The UASB reactor was continuously operated for 222 days divided into eight phases: (I) the startup stage from day 0 to day 35 at an OLR of 2
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Table 1 Operating conditions of the lab-scale UASB reactor during the 222-day period. Phases
OLR (g-COD/L/d)
Flow Rate (m3/d)
COD (mg/L)
HRT (h)
Temperature (� C)
pH
Days
I II III IV V VI VII VIII
0.96 � 0.25 2.08 � 0.12 4.93 � 0.26 6.03 � 0.36 7.92 � 0.25 12.05 � 0.50 16.17 � 0.87 24.07 � 0.64
0.003 0.006 0.006 0.006 0.006 0.006 0.006 0.006
1912.57 � 251.59 2078.44 � 124.89 4930.52 � 263.30 6031.84 � 362.09 7915.85 � 254.87 12054.89 � 502.20 16171.43 � 869.86 24070.31 � 638.41
48 24 24 24 24 24 24 24
35 � 2
7.0 � 0.1 7.0 � 0.1 7.0 � 0.1 7.0 � 0.1 7.0 � 0.1 7.0 � 0.1 7.0 � 0.1 7.0 � 0.1
1–35 36–73 74–97 98–129 130–153 154–175 176–198 199–222
replaced with pressurized gas of H2–CO2 (80:20, v/v) to obtain a target pressure of 1.4 atm. Finally, all bottles were incubated in a shaking water bath at 35 � 1 � C at 100 � 5 rpm. Biogas production and compo sition were measured at 2–6 h intervals and expressed as the value at STP. More details can be found in the literature (Kong et al., 2018; Lu et al., 2015; Wu et al., 2018).
mg/L) was calculated with Eq. (3). ΔN ¼ Nin
ORR ¼
Sludge samples collected from the bottom of the UASB reactor at the end of phases I and VIII were used to analyze the taxonomic patterns of microbial communities during the operation. Genomic DNA was extracted by a FastDNA® SPIN Kit (FastDNA® SPIN kit for soil, MPbio, Inc., CA). The V3–V4 hypervariable regions of the bacteria 16S rRNA gene were amplified with primers 338 F (50 -ACTCCTACGGGAGGCAG CAG-30 ) and 806 R (50 -GGACTACHVGGGTWTCTAAT-30 ) using a ther mocycler PCR system (GeneAmp 9700, ABI, USA). PCR reactions were performed in triplicate with 20 μL mixtures containing 4 μL of 5 � FastPfu Buffer, 2 ìL of 2.5 mM dNTPs, 0.8 ìL of each primer (5 ìM), 0.4 ìL of FastPfu polymerase and 10 ng of template DNA. The PCR re actions were conducted using the following program: 3 min of dena turation at 95 � C, 35 cycles of 30 s at 95 � C, 30 s for annealing at 55 � C, and 45 s for elongation at 72 � C, and a final extension at 72 � C for 10 min. The V3–V4 hypervariable regions of the archaea 16S rRNA gene were amplified with primers 524F10extF (TGYCAGCCGCCGCGGTAA) and Arch958RmodR (YCCGGCGTTGAVTCCAATT) by a thermocycler PCR system (GeneAmp 9700, ABI, USA). The resulting PCR products were extracted from a 2% agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™ -ST (Promega, USA) according to the manufacturer’s protocol. The final DNA concentration was determined using a NanoDrop 2000 UV–vis spectrophotometer (Thermo Scientific, Wilmington, USA). Sequencing was accomplished using Illu mina’s Miseq PE300 platform (Shanghai Meiji Biomedical Technology Co., Ltd.). UPARSE software (version 7.1 http://drive5.com/uparse/) was used to perform OTU clustering on sequences based on 97% simi larity. Chimeras were eliminated using the UCHIME software.
η¼
Cout Þ � 1000 � V V � T=24
(4)
Cin
Cout � 100 Cin
(5)
The specific methanogenic activity (SMA, g-CODCH4/g-VSS/d) was calculated with Eq. (6). SMA ¼
24 � Rmax CF � V2 � VSS
(6)
where Rmax is the maximum methane production rate (mL/h) obtained using the modified Gompertz function, CF is the conversion coefficient between methane (mL) and COD (g) with saturated water vapor (418 mL CH4/(g-CODCH4) @ 35 � C), VSS is the sludge concentration (g-VSS/L) and V2 (L) is the volume of the mixture in the serum bottle. 3. Results and discussion 3.1. Overall performance of the UASB reactor under various OLRs The overall performance of the UASB reactor in terms of pH, tem perature, alkalinity, COD, gas production rate, NHþ 4 -N, and VFAs is illustrated in Fig. 2. As the OLR increased, the pH and alkalinity (both bicarbonate alkalinity and total alkalinity) in the influent remained stable, whereas those in the effluent increased (Fig. 2A and B). This increment resulted from the increase in products, including carbon di oxide or hydro-carbonate and ammonia, because of decomposition of the feeding glutamate. Fig. 2C reflects the dynamic changes of COD in the influent and effluent, as well as the removal efficiency under different OLRs. When the OLR increased from 1 g-COD/L/d to 8 g-COD/ L/d, the COD removal efficiency reached up to 97.9%. In comparison, over 93% of chemical oxygen demand (COD) was effectively removed through anaerobic degradation of dimethyl phthalate, and the highest COD removal was 76.6% in biodegradation of MSG wastewater (Kong et al., 2018; Jia et al., 2007). As the OLR increased to 24 g-COD/L/d, the removal efficiency markedly dropped to approximately 60%. Biogas and methane production rates increased with the increase in OLR, which reached as high as 8.6 L/L/d and 4.5 L/L/d in phase VIII (Fig. 2D). The ammonia nitrogen concentrations in the influent and effluent, as well as the free ammonia (FA) concentration in the system are illustrated in Fig. 2E. As the OLR increased during the whole operation period, concentrations of ammonia nitrogen and FA in the system also itera tively increased. When the OLR exceeded 8 g-COD/L/d, the effluent
The methane yield was calculated with Eq. (1). (1)
whereY (L/g-COD) is methane yield, V1 (L/d) is the volume of methane produced daily, Cin (mg-COD/L) is the influent concentration, V (L) is the working volume of the reactor, and HRT (h) is the hydraulic reten tion time. The methane production rate was calculated as follows. y ¼ V1 =V
ðCin
where Cout (mg COD/L) is the effluent concentration. The COD removal efficiency (η, %) was calculated using Eq. (5).
2.6. Calculations
V1 24 Cin � 1000 � V � HRT
(3)
where Nin and Nout (mg/L) are the influent and effluent ammonia ni trogen concentrations (mg/L), respectively. The organic removal rate (ORR, g-COD/L/d) was calculated with Eq. (4).
2.5. Microbial community analyses
Y¼
Nout
(2)
where y (L CH4/g COD) is the CH4 production rate. The difference between influent and effluent ammonia nitrogen (ΔN, 3
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Fig. 2. Operational performance of the UASB reactor run continuously under different OLRs. (A) pH and temperature. (B) Alkalinity. (C) COD concentration and removal efficiency. (D) Gas production rate. (E) Ammonia nitrogen and free ammonia concentrations. (F) VFA concentrations.
ammonia nitrogen and the FA reached over 1200 mg/L and 100 mg/L, respectively. However, methane production was inhibited in this system when FA accumulated at a level of 40–100 mg/L or the ammonia ni trogen concentration was higher than 6000 mg/L (Shi et al., 2017; Tian et al., 2018). Fig. 2F clearly shows that VFA accumulation began during phase V (OLR of 8.0 kg COD/m3/d), and the total VFA concentration reached over 2000 mg/L in phase VIII (OLR of 24.0 kg COD/m3/d). Among VFAs, acetate was the largest fraction. Heavy VFA accumulation can decrease the ORR and COD removal efficiency, as well as repress the biochemical reactions in anaerobic digestion (Ketheesan and Stuckey, 2015; Lu et al., 2015; Niu et al., 2015). These results indicated that methanogenesis was the rate limiting step for anaerobic degradation under high OLRs (Xiao et al., 2018; Zhang and Fang, 2015).
0.52 � 0.04 L/L/d to 4.22 � 0.28 L/L/d with the increase of OLR from 2 g-COD/L/d to 24 g-COD/L/d, the CH4 yield increased from 0.25 � 0.03 L/g-COD (2 g-COD/L/d) to 0.28 � 0.03 L/g-COD (8 g-COD/ L/d) and then decreased to 0.18 � 0.02 L/g-COD (24 g-COD/L/d) (Fig. 3B). The above results indicate that 8 g-COD/L/d OLR corre sponded to the maximum removal efficiency and the maximum methane yield, implying that superior glutamate removal by the UASB reactor. The methane component in the biogas produced under various OLRs is displayed in Fig. 3C. The percentage of CH4 in the biogas continuously decreased from 74.1% to 50.8% with the increase of OLR from 2 g-COD/ L/d to 24 g-COD/L/d. Overall, the good methane production is expected to supplement energy requirements, despite the reduced methaneproducing performance at high OLRs. Removal capacity is an important index for pollutant removal by bioreactors (Chen et al., 2018; Cheng et al., 2016). The removal ca pacities of glutamate from wastewater by the UASB reactor under various OLRs are presented in Fig. 4. When the OLR was below 8 g-COD/L/d, the glutamate removal efficiency was close to 100%, and the corresponding removal capacity was approximately equal to the OLR. However, the removal capacity was 11.2 g-COD/L/d, 14.1 g-COD/L/d and 17.1 g-COD/L/d at OLRs of 12 g-COD/L/d, 16 g-COD/L/d and 24 g-COD/L/d, respectively. In this study, the maximum removal capacity reached was approximately 18 g-COD/L/d,
3.2. Organic removal and biogas production under various OLRs The effects of OLR on the reactor performance including organic removal and biogas production are shown in Fig. 3. The ORR increased from 1.95 � 0.13 g/L/d to 15.61 � 1.02 g/L/d with an increase of OLR from 2 g-COD/L/d to 24 g-COD/L/d, whereas the COD removal effi ciency increased from 93.79 � 4.13% (2 g-COD/L/d) to 97.96 � 0.61% (8 g-COD/L/d) and then decreased to 65.36 � 5.16% (24 g-COD/L/d) (Fig. 3A). Although the CH4 production rate increased from 4
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Journal of Environmental Management 253 (2020) 109691
increase in OLR, the COD in the effluent increased to the highest value in phase VIII, up to 34.8 � 5.2%. The recovered CH4-COD in the gas phase dominated the COD flow of the substrate in each operational phase, which demonstrated good energy recovery from the glutamate waste water treated by the UASB reactor. The distribution of COD flow was different among the different OLRs over the steady-state period, which is consistent with a lab-scale UASB reactor treating starch wastewater under different hydraulic retention times (HRTs) (Lu et al., 2015). In phases II–VII, the CH4-COD recovery decreased with the increase of OLR, although it still reached up to 70.6 � 5.6% in phase V. In phase VIII, the CH4-COD recovery reached a minimum of 54.0 � 4.2%, which was still the largest part of the COD distribution. In terms of removal efficiency and energy recovery, the optimal OLR for MSG wastewater treatment in a UASB is no more than 8 g-COD/L/d. 3.4. Characteristics and composition of sludge The results for the sludge component, VSS concentration, VSS/TSS ratio, sludge volume fraction in particle size, and average settling ve locity at different OLRs from 1 g-COD/L/d to 24 g-COD/L/d are shown in Fig. 5. During the operation of the reactor, the EPS content of the sludge in the reactor first increased and then decreased slightly with the increase of OLR (Fig. 5A). At a moderate OLR (6–12 g-COD/L/d), both the PN and the PS in the EPS were maintained at a high level that could provide better conditions for sludge granulation. The ratio of PN/PS in the EPS component was in the range of 1.3–2.3 with a tendency to have an initial decrease and a subsequent increase. A relatively low PN/PS ratio was obtained in phases II–V, which implied a good performance in sludge granulation (Basuvaraj et al., 2015). As shown in Fig. 5B, when the reactor operated at OLRs of 1–8 gCOD/L/d, the VSS of the sludge in the reactor increased from 20.4 g/L to 47.6 g/L, and the VSS/TSS ratio increased from 40.9% to 60.8%. As the OLR continuously increased to 24 g-COD/L/d, the VSS concentration and the VSS/TSS ratio were reduced to 22.5 g/L and 44.9%, respec tively. The results herein indicate that the activated biomass (Lu et al., 2015) that accumulated in the reactor increased at first and then decreased. These suggest that high OLRs (over 16 g-COD/L/d) were inferior for the accumulation and activity of the granules in the UASB reactor. The sludge fraction ratio in particle size, as well as the granule settling velocity under different OLRs from 1 g-COD/L/d to 24 g-COD/ L/d are displayed in Fig. 5C. The volume fraction of particle diameter greater than 0.5 mm was 59.8%, 62.2%, 65.3%, 72.2%, 79.6%, 77.8%, 74.3%, and 75.6% in each phase, respectively. The average particle size of the sludge increased with the increase of OLR at its middle-low level and provided more substrate entering the interior of the granules (Li and Yang, 2007). However, a high OLR could lead to a decrease of average granule particle diameter for the high concentrations of substrate and intermediate products (Lu et al., 2017). The average settling velocity of the granular sludge in each phase was 59.8 m/h, 62.2 m/h, 65.3 m/h, 72.2 m/h, 79.6 m/h, 77.8 m/h, 74.3 m/h, and 75.6 m/h, respectively. The maximum settling velocity of the granules appeared in phase V, which indicated good sedimentation performance (Li et al., 2018). The trend of an initial increase and subsequent decrease observed in both the sludge sedimentation rate and the particle size indicates that a superior sludge granulation (Ghangrekar et al., 2005; Zhou et al., 2007) was obtained under moderate OLRs, whereas high OLRs had a negative effect on the particle size of granular sludge. These results are consistent with the overall performance of the reactor (Fig. 2C).
Fig. 3. Effect of OLR on UASB performance. Profiles of (A) organic removal rate (ORR) and COD removal efficiency, (B) CH4 production rate and CH4 yield, and (C) biogas composition.
Fig. 4. Removal capacities of the UASB reactor for MSG wastewater treatment.
whereas the critical load ranged from 1 g-COD/L/d and 8 g-COD/L/d. 3.3. COD mass balance
3.5. Specific methanogenic activity and probable degradation pathway
The mass balance of COD in glutamate treatment by the UASB reactor under various OLRs is summarized in Table 2. The COD flowing out of the reactor was mainly composed of three parts: the ratio of COD in the effluent, the ratios converted into methane recovered through the gas phase, and methane flushed out by dissolving in the effluent. With an
The SMA tests were conducted in phases I, V, and VII using Lglutamate, methanol, acetate, propionate, butyrate, valerate, and H2–CO2 as substrates. The results are shown in Table 3. 5
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Table 2 Distributions of COD under various OLRs. Phases I II III IV V VI VII VIII
CODinf (mg/L) 1927.6 � 180.8 2078.5 � 124.9 3919.8 � 187.3 6023.1 � 362.1 7919.4 � 254.9 12086.5 � 477.1 16695.4 � 841.9 23944.5 � 590.7
CODmeth
CODeff
CODsol
CODCH4
recovery
(mg/L)
(%)
(mg/L)
(%)
(mg/L)
(%)
(mg/L)
(%)
1397.5 � 57.8 1538.1 � 95.6 2877.1 � 78.4 4366.7 � 96.4 5424.8 � 388.1 7614.5 � 471.4 9867.0 � 1235.5 12546.9 � 885.9
72.5 � 3.0 74.0 � 4.6 73.4 � 2.0 72.5 � 1.6 68.5 � 4.9 63.0 � 3.9 59.1 � 7.4 52.4 � 3.7
171.6 � 69.4 124.7 � 66.5 294.0 � 78.4 283.1 � 114.4 166.3 � 71.3 979.0 � 217.5 2521.0 � 450.8 8332.7 � 1245.1
8.9 � 3.6 6.0 � 3.2 7.5 � 2.0 4.7 � 1.9 2.1 � 0.9 8.1 � 1.8 15.1 � 2.7 34.8 � 5.2
48.2 � 7.7 56.1 � 12.5 101.9 � 11.8 144.6 � 12.0 166.3 � 55.4 229.6 � 60.4 300.5 � 150.3 383.1 � 119.7
2.5 � 0.4 2.7 � 0.6 2.6 � 0.3 2.4 � 0.2 2.1 � 0.7 1.9 � 0.5 1.8 � 0.9 1.6 � 0.5
1445.7 � 65.5 1594.2 � 108.0 2979.1 � 90.2 4511.3 � 108.4 5591.1 � 443.5 7844.1 � 531.8 10167.5 � 1385.7 12930.0 � 1005.7
75.0 � 3.4 76.7 � 5.2 76.0 � 2.3 74.9 � 1.8 70.6 � 5.6 64.9 � 4.4 60.9 � 8.3 54.0 � 4.2
Note: CODmeth, recovered CH4-COD in gas phase; CODsol, soluble CH4-COD calculated in the effluent; CODCH4 recovery, which represents the CH4-COD recovery. Table 3 Specific methanogenic activity (SMA) detected in phases I, V, and VII. Substrates Methanol Acetate Propionate Butyrate Valerate L-Glutamate H2–CO2
SMA (g-CODCH4/g-VSS/d)
Rmax (mL/h)
I
V
VII
I
V
VII
0.038 0.020 0.010 N-Da N-Da 0.013 0.051
0.033 N-Da 0.009 0.047 0.017 0.086 0.040
0.033 0.085 0.008 0.074 0.013 0.070 0.030
1.50 0.79 0.40 N-Da N-Da 0.50 1.98
1.51 N-Da 0.43 2.12 0.79 3.89 1.82
1.14 2.98 0.26 2.59 0.46 2.47 1.06
N-Da: not detected.
resulting from H2–CO2. When using H2–CO2, the SMA exhibited a slightly decreasing trend over time that indicated a shrinking meth anogenic pathway of via H2–CO2. However, when fed using L-glutamate, the SMA increased from 0.013 g-CODCH4/g-VSS/d in phase I to 0.086 gCODCH4/g-VSS/d in phase V, and then decreased to 0.070 g-CODCH4/gVSS/d in phase VII. These results indicated that the sludge degrading ability on L-glutamate had been reduced and was likely inhibited by high concentrations of intermediate products (NHþ 4 , VFAs). SMA may also reveal the possible degradation pathway (Liang et al., 2007). The glutamate-degrading sludge was capable of degrading L-glutamate, butyrate, and acetate, but not propionate and valerate. Although amino acids can act as both acceptors and donors when degraded via a Stickland reaction under anaerobic conditions (Zhang and Fang, 2015), glutamate degraded in the UASB reactor seemed to follow other paths, as shown by the small value of SMA achieved on valerate (Table 2) and the lack of valerate that accumulated in the reactor under high OLRs (Fig. 2F). According to the SMA in phase I, the methanogenic pathway through H2 and CO2 is preferred over the pathway than through acetate. However, judging from the glutamate-degrading sludge’s SMA data in phase VIII, glutamate was likely degraded to butyrate after deamination, which then further preferred to degrade into acetate. Along with the VFA data shown in Fig. 2F, these results reveal that a large amount of acetate and some proportion of butyrate accumulated in phases VII and VIII, which also supports the proposed degradation pathways. The results in Table 3 also indicate that the SMA of the sludge in phase V was the highest for degrading L-glutamate, whereas the SMA (0.070 g-CODCH4/g-VSS/d) in phase VII (with high OLR) was lower than the SMA values for degrading the downstream intermediates, i.e., 0.074 g-CODCH4/g-VSS/d for acetate and 0.085 g-CODCH4/g-VSS/d for butyrate. High OLR could lead to the unrecoverable decay of meth anogenic activity and a serious imbalance between substrate uptake and biological activities that are directly affected by high concentrations of ammonia and VFAs (Zhou et al., 2007). The results revealed that the initial deamination was not a rate-limiting step, but the probable degradation pathway was affected by high OLRs during glutamate anaerobic degradation.
Fig. 5. Characteristics of the granular sludge in the UASB reactor under various OLRs. (A) EPS content and PN/PS ratio. (B) VSS concentration and VSS/TSS ratio. (C) Sludge volume fraction in particle size and average settling velocity of the granules.
The glutamate-degrading sludges taken from the UASB reactor at different phases, varied substantially in their substrate degrading abili ties. The maximum methane production rate (Rmax) of the sludge increased markedly with time when using acetate or butyrate, whereas it decreased significantly when using propionate, valerate, or H2–CO2. The SMA also increased with time when fed with acetate or butyrate, ranging from 0.020 to 0.085 g-CODCH4/g-VSS/d and 0.047–0.074 g-CODCH4/gVSS/d, respectively. The highest SMA of sludge in phase VII resulted from using acetate as the substrate and was much higher than that 6
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aromatic compounds, into acetate (Tian et al., 2018; Tsavkelova et al., 2018). The observation that Firmicutes dominated in phase VIII is consistent with the aforementioned VFAs’ massive surge at an OLR of 24 g-COD/L/d (Fig. 2F). However, Actinobacteria, which are capable of utilizing organic matter, such as glucose, maltose, starch, and dextrin (Gupta et al., 2014), markedly decreased from 24.29% in phase I to 0.12% in phase VIII. The probable explanation is the lack of substrate supply and inhibition caused by the high ammonia concentration. These results suggest that the operational OLR had a substantial effect on the bacterial community structure and the probable degradation pathway of glutamate. The Shannon diversity index for archaeal and bacterial for commu nity structure in the sludge samples was determined at 97% sequence similarity (Table 4). The observed number of operational taxonomic units (OTUs) of bacterial sequences were as high as 525, indicating high bacterial communities in both the sludge samples. The sludge sample taken during phase I showed a slightly higher alpha diversity of archaeal community and a much lower alpha diversity of bacterial community compared to phase VIII, which reflects the response of the constructed ecosystem to changes in its environmental conditions. Under high OLR conditions, the obvious decline in the biodiversity of the bacterial community could be attributed to the increase in glutamate loadings, as well as ammonia and VFA accumulation, indicating the vulnerability of the ecosystem and a reduced ability to resist environmental stress (Feranchuk et al., 2018). With the gradual increase of OLR, VFAs (mainly acetic acid) increased, which were conducive to the growth of
3.6. Microbial community structure and diversity The archaeal and bacterial microbial community structure and di versity in the sludge were analyzed by high throughput sequencing. Samples were taken from the UASB reactor during phase I and phase VIII. Genus Archaea and phylum Bacteria, which occupied a relative abundance �1% in both phases, are illustrated in Fig. 6, whereas un cultured sequences were classified as others. In the case of Archaea, the predominance of genera shifted from Methanobacterium (with a relative abundance of 59.83%) and Meth anosaeta (34.40%) to Methanobacterium (47.98%) and Methanosarcina 35.12%) (Fig. 6A). The hydrogenotrophoic genus Methnobacterium dominated both phases, and can utilize hydrogen as a substrate during the anaerobic degradation process (Luo and Angelidaki, 2012; Zhang and Fang, 2015). The acetotrophic genus Methanosaeta (Kong et al., 2018; Niu et al., 2015) significantly decreased in abundance, whereas Methanosarcina, which possesses all three known pathways for meth anogenesis (Gupta et al., 2014), sharply increased during phase VIII. This supports the idea that Methanosarcina easily dominate the archaeal community at the highest ammonia levels (Tian et al., 2018). These results revealed that the OLR had a substantial effect on the shift in the archaeal community during the entire operational period. Eight known bacterial phyla, namely Firmicutes, Actinobacteria, Synergistetes, Proteobacteria, Chloroflexi, Bacteroidetes, Thermotogae, and Candidatus Cloacimonetes are found to coexist in the granular sludge in both phases (Fig. 6B). Among them, Firmicutes, Proteobac teria, Chloroflexi and Bacteroidetes regularly appeared in the anaerobic systems (Sun et al., 2015; Tian et al., 2015; Wu et al., 2018). It should be noted that Firmicutes, Synergistetes, and Bacteroidetes were much higher in abundance in phase VII than in phase I, and Firmicutes was clearly predominant in phase VIII, with 51.40% abundance. Highly correlated with hydrogenotrophic methanogens (Hattori, 2008), the predominant Firmicutes, including the class Clostridia (with 21.36% abundance in phase I and 47.51% abundance in phase VIII), were mainly responsible for converting monomers, such as alcohol, propionate, other short-chain fatty acids (VFAs), some amino acids, and
Table 4 Alpha diversity index of the microbial community structure in the sludge samples. Samples Archaea Bacteria
Phase I Phase VIII Phase I Phase VIII
Effective reads
OTUs
Shannon
Coverage
66791 62790 62177 65,308
24 24 525 525
1.488 1.506 3.719 3.516
0.999 1 0.999 0.998
Fig. 6. Microbial community structure in the continuous UASB reactor. Genus Archaea (A) and phylum bacteria (B) in phases I and phase VIII. 7
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acetotrophic methanogens. In contrast, the relative abundance of ace totrophic methanogens increased as the SMA value of hydrotrophic methanogens decreased, indicating that the dominant methanogenic degradation pathway changed. Meanwhile, biological metabolism was influenced by the increasing concentration of ammonia nitrogen. Aside from Methanobacterium and Methanosarcina, other methanogens were suppressed to varying degrees. Therefore, the OLR had a substantial influence on the diversity of the microbial community structure in the UASB reactor.
Hattori, S., 2008. Syntrophic acetate-oxidizing microbes in methanogenic environments. Microb. Environ. 23 (2), 118–127. Jia, C.Y., Kang, R.J., Zhang, Y.H., Cong, W., Cai, Z.L., 2007. Synergic treatment for monosodium glutamate wastewater by Saccharomyces cerevisiae and Coriolus versicolor. Bioresour. Technol. 98, 967–970. Jiang, L.Q., Pei, H.Y., Hu, W.R., Ji, Y., Han, L., Ma, G.X., 2015. The feasibility of using complex wastewater from a monosodium glutamate factory to cultivate Spirulina subsalsa and accumulate biochemical composition. Bioresour. Technol. 180, 304–310. Ketheesan, B., Stuckey, D.C., 2015. Effects of hydraulic/organic shock/transient loads in anaerobic wastewater treatment: a review. Crit. Rev. Environ. Sci. Technol. 45, 2693–2727. Kong, Z., Li, L., Kurihara, R., Kubota, K., Li, Y.Y., 2018. Anaerobic treatment of N, Ndimethylformamide-containing wastewater by co-culturing two sources of inoculum. Water Res. 139, 228–239. Li, X.Y., Yang, S.F., 2007. 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Evaluation of functional microbial community’s difference in full-scale and lab-scale anaerobic digesters feeding with different organic solid waste: effects of substrate and operation factors. Bioresour. Technol. 193, 110–118. Qiao, W., Tkayanagi, K., Li, Q., Shofie, M., Gao, F., Dong, R.J., Li, Y.Y., 2016. Thermodynamically enhancing propionic acid degradation by using sulfate as an external electron acceptor in a thermophilic anaerobic membrane reactor. Water Res. 106, 320–329. Rittmann, B.E., McCarty, P.L., 2001. Environmental Biotechnology: Principles and Applications. McGraw-Hill Education, New York. Shen, Y.W., Linville, J.L., Urgun-Demirtas, M., Mintz, M.M., Snyder, S.W., 2015. An overview of biogas production and utilization at full-scale wastewater treatment plants (WWTPs) in the United States: challenges and opportunities towards energyneutral WWTPs. Renew. Sustain. Energy Rev. 50, 346–362. Shi, X.C., Lin, J., Zuo, J.N., Li, P., Li, X.X., Guo, X.L., 2017. Effects of free ammonia on volatile fatty acid accumulation and process performance in the anaerobic digestion of two typical bio-wastes. J. Environ. Sci. 55, 49–57. Sponza, D.T., Uluk€ oy, A., 2008. Kinetic of carbonaceous substrate in an upflow anaerobic sludge sludge blanket (UASB) reactor treating 2,4 dichlorophenol (2,4 DCP). J. Environ. Manag. 86 (1), 121–131, 2008. Sun, R., Zhou, A.J., Jia, J.N., Liang, Q., Liu, Q., Xing, D.F., Ren, N.Q., 2015. Characterization of methane production and microbial community shifts during waste activated sludge degradation in microbial electrolysis cells. Bioresour. Technol. 175, 68–74. Taki, K., Gogoi, A., Mazumder, P., Bhattacharya, S.S., Kumar, M., 2019. Efficacy of vermitechnology integration with Upflow Anaerobic Sludge Blanket (UASB) and activated sludge for metal stabilization: a compliance study on fractionation and biosorption. J. Environ. Manag. 236, 603–612. Tchobanoglous, G., Stensel, H.D., Tsuchihashi, R., Burton, F., 2014. Wastewater Engineering: Treatment and Resource Recovery, fifth ed. Metcalf & Eddy Inc. McGraw-Hill, New York. Tian, Z., Zhang, Y., Li, Y.Y., Chi, Y.Z., Yang, M., 2015. Rapid establishment of thermophilic anaerobic microbial community during the one-step startup of thermophilic anaerobic digestion from a mesophilic digester. Water Res. 69, 9–19. Tian, H.L., Fotidis, I.A., Mancini, E., Treu, L., Mahdy, A., Ballesteros, M., Gonz� alezFern� andez, C., Angelidaki, I., 2018. Acclimation to extremely high ammonia levels in continuous biomethanation process and the associated microbial community dynamics. Bioresour. Technol. 247, 616–623. Tsavkelova, E., Prokudina, L., Egorova, M., Leontieva, M., Malakhova, D., Netrusov, A., 2018. The structure of the anaerobic thermophilic microbial community for the bioconversion of the cellulose-containing substrates into biogas. Process Biochem. 66, 183–196. Tseng, S.K., Lin, M.R., 1990. Treatment of monosodium glutamate fermentation wastewater with anaerobic biological fluidized bed process. Water Sci. Technol. 22 (9), 149–155.
4. Conclusions We conducted systematic treatment of monosodium glutamate (MSG) wastewater using a lab-scale up-flow anaerobic sludge blanket reactor under various organic loading rates (OLRs). The optimal OLR was lower than 8 g-COD/L/d with a maximum CH4 yield of 0.28 � 0.03 L/g-COD, which was consistent with highest COD removal efficiency up to 97.9%. The maximum removal capacity reached was approximately 18 g-COD/L/d. Increased accumulation of free ammonia and volatile fatty acids resulted in a decrease in the reactor performance, specifically in the glutamate removal efficiency, methane production rate, and granular sludge diameter. Based on the SMA data and the changes in the microbial community, the OLR had a substantial influ ence on the reactor performance and the microbial community varia tions of the UASB for MSG wastewater treatment because of the accumulation of VFAs and ammonia nitrogen degraded from glutamate. Declaration of interests The authors have no conflict of interest to declare. Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 51308068, 51706022) and the China Hunan Provincial Science & Technology Department (No. 2017SK2361). The authors gratefully acknowledge the support from professor Yu-You Li (Tohoku University, Japan), the Japan Society for the Promotion of Science and the Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). We would like to thank Editage (www.editage.cn) for English language editing. References APHA, 2005. Standard Methods for the Examination of Water and Wastewater. APHAAWWA-WEF, Washington, DC, USA. Basuvaraj, M., Fein, J., Liss, S.N., 2015. Protein and polysaccharide content of tightly and loosely bound extracellular polymeric substances and the development of a granular activated sludge floc. Water Res. 82, 104–117. Chen, H., Wei, Y.X., Peng, L., Ni, J.L., Guo, Y., Ji, J.Y., Jiang, B., Yu, G.L., 2018. Longterm MIBK removal in a tubular biofilter: effects of organic loading rates and gas empty bed residence times. Process Saf. Environ. 119, 87–95. Chen, H., Wu, J., Liu, B., Li, Y.Y., Yasui, H., 2019. Long-term competitive dynamics of anaerobic microbials in a UASB for a sulfate-rich wastewater treatment by extending Anaerobic Digestion Model No. 1 (ADM1) with sulfate reduction. Bioresour. Technol. 280, 173–182, 2019. Cheng, Y., He, H.J., Yang, C.P., Zeng, G.M., Li, X., Chen, H., Yu, G.L., 2016. Challenges and solutions for biofiltration of hydrophobic volatile organic compounds. Biotechnol. Adv. 34 (6), 1091–1102. Dong, L., Li, Y., Wang, P., Feng, Z., Ding, N., 2018. Cleaner production of monosodium glutamate in China. J. Clean. Prod. 190, 452–461. Feranchuk, S., Belkova, N., Potapova, U., Kuzmin, D., Belikov, S., 2018. Evaluating the use of diversity indices to distinguish between microbial communities with different traits. Res. Microbiol. 169, 254–261. Ghangrekar, M.M., Asolekar, S.R., Joshi, S.G., 2005. Characteristics of sludge developed under different loading conditions during UASB reactor start-up and granulation. Water Res. 9 (6), 1123–1133. Gupta, M., Velayutham, P., Elbeshbishy, E., Hafez, H., Khafipour, E., Derakhshani, H., Naggar, M.H.E., Levin, D.B., Nakhla, G., 2014. Co-fermentation of glucose, starch, and cellulose for mesophilic biohydrogen production. Int. J. Hydrogen Energy 39, 20958–20967.
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van Lier, J.B., 2008. High-rate anaerobic wastewater treatment: diversifying from end-ofthe-pipe treatment to resource-oriented conversion techniques. Water Sci. Technol. 57 (8), 1137–1148. Wu, J., Niu, Q., Li, L., Hu, Y., Mribet, C., Hojo, T., Li, Y.Y., 2018. A gradual change between methanogenesis and sulfidogenesis during a long-term UASB treatment of sulfate-rich chemical wastewater. Sci. Total Environ. 636, 168–176. Xiao, B.Y., Dai, Q., Yu, X., Yu, P.F., Zhai, S.M., Liu, R.Z., Guo, X.S., Liu, J.X., Chen, H., 2018. Effects of sludge thermal-alkaline pretreatment on cationic red X-GRL adsorption onto pyrolysis biochar of sewage sludge. J. Hazard Mater. 343, 347–355.
Yang, C.P., Chen, H., Zeng, G.M., Yu, G.L., Luo, S.L., 2010. Biomass accumulation and control strategies in gas biofiltration. Biotechnol. Adv. 28 (4), 531–540. Yi, Q.Z., Zhang, Y., Gao, Y.X., Tian, Z., Yang, M., 2017. Anaerobic treatment of antibiotic production wastewater pretreated with enhanced hydrolysis: simultaneous reduction of COD and ARGs. Water Res. 110, 211–217. Zhang, T., Fang, H.H.P., 2015. Anaerobic Biotechnology: Environmental Protection and Resource Recovery. Imperial College Press, London. Zhou, W.L., Imai, T., Ukita, M., Li, F.S., Yuasa, A., 2007. Effect of loading rate on the granulation process and granular activity in a bench scale UASB reactor. Bioresour. Technol. 98 (7), 1386–1392.
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Research article
Performance and microbial community variations of a upflow anaerobic sludge blanket (UASB) reactor for treating monosodium glutamate wastewater: Effects of organic loading rate Hong Chen a, b, c, Yanxiao Wei a, Peng Liang a, Chunyan Wang d, Yingbing Hu a, Min Xie a, Yiyu Wang a, Benyi Xiao b, *, Chunyan Du a, Hong Tian e a
Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, School of Hydraulic Engineering, Changsha University of Science & Technology, Changsha, 410004, China b Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China c Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan d Department of Biology and Chemical Engineering, Nanyang Institute of Technology, Nanyang, 473004, China e School of Energy & Power Engineering, Changsha University of Science & Technology, Changsha, 410114, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Anaerobic digestion Energy recovery Methanogenic degradation Specific methanogenic activity Up-flow anaerobic sludge blanket Wastewater treatment
To investigate the effect of the organic loading rate (OLR) on anaerobic treatment of monosodium glutamate (MSG) wastewater, a lab-scale up-flow anaerobic blanket (UASB) reactor was continuously operated over a 222day period. The overall performances of COD removal and methane recovery initially exhibited an increase and subsequently decreased when the OLR was increased from 1 g-COD/L/d to 24 g-COD/L/d. At the optimal OLR of 8 g-COD/L/d, superior performance was obtained with a maximum COD removal efficiency of 97%, a methane production rate of 2.3 L/L/d, and specific methanogenic activity of 86 mg-CH4/g-VSS/d (feeding on glutamate), with superior characteristics of sludge in VSS concentration, average diameter of granules, and settling velocity. According to the results of the specific methanogenic activity, the methanogenic pathway was more inclined to pass through acetate than through hydrogen. Methanosarcina instead of Methanosaeta, with Methanobacterium and greatly increased Firmicutes, dominated in the UASB reactor after long term operation. These results support that the OLR had a substantial effect on both the treatment and energy recovery efficiency of MSG wastewater as well as on microbial community variations in the UASB reactor.
1. Introduction Anaerobic technology for high-strength organic wastewater treat ment has become a hot topic of study owing to its cost-efficiency, energy recovery, and ability to reduce sludge production and greenhouse gas emissions (Li and Yu, 2016; Tchobanoglous et al., 2014; van Lier, 2008). In order to satisfy the strict requirements and multi-objective demands of environmental conservation, the anaerobic degradation of biode gradable pollutants in wastewater, including those successfully degraded under aerobic or anoxic conditions, has increasingly gaining garnered research interest (Li et al., 2015; Shen et al., 2015; Taki et al., 2019; Yang et al., 2010; Yi et al., 2017). For example, phthalic esters, a group of chemicals widely used as additives during plastic manufacturing, were effectively removed using a continuous up-flow
anaerobic sludge blanket (UASB) reactor (Liang et al., 2007). The biodegradation of N, N-dimethyformamide, which was previously re ported to only be possible under aerobic conditions, could also be ach ieved with complete methanogenic degradation (Kong et al., 2018). In contrast to aerobic degradation in which oxygen is used as a strong electron acceptor (Cheng et al., 2016; Rittmann and McCarty, 2001), anaerobic degradation is more susceptible to environmental factors, such as diverse and complicated electron transfer paths (Ketheesan and Stuckey, 2015; Qiao et al., 2016). Operational parameters, including the organic loading rate (OLR), have great effects on anaerobic removal performance during continuous long-term operation periods (Ketheesan €y, 2008). Moreover, the OLR was and Stuckey, 2015; Sponza and Uluko shown to critically affect methanogenic activity, extracellular polymer content, settling velocity, and granulation of the sludge (Ghangrekar
* Corresponding author. E-mail address: [email protected] (B. Xiao). https://doi.org/10.1016/j.jenvman.2019.109691 Received 26 March 2019; Received in revised form 3 October 2019; Accepted 6 October 2019 Available online 17 October 2019 0301-4797/© 2019 Elsevier Ltd. All rights reserved.
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et al., 2005; Zhou et al., 2007). In anaerobic reactors, the complicated microbial communities and their competitive dynamics are still unclear (Chen et al., 2019; Tian et al., 2015). In addition, anaerobic degradation is currently mainly used for the treatment of hydrocarbon-containing wastewater but not for the treatment of amino acid-containing waste water. Thus, it is of practical significance to research the operational performance and microbial community shifts of the anaerobic process for the treatment of amino acid-containing wastewater. Monosodium glutamate (MSG) wastewater, a typical amino acidcontaining organic wastewater, contains high concentrations of chemi cal oxygen demand (COD), NH4–N, and SO24 that can cause serious environmental and human health issues. It is reported that 2.2 million tons of MSG wastewater (approximately 80% of the total global pro duction) are produced in China annually, and this wastewater has po tential for resource reutilization and can be successfully treated by aerobic methods (Jia et al., 2007; Jiang et al., 2015; Dong et al., 2018). Although MSG wastewater can be treated by physical or chemical methods, only a few studies have focused on the anaerobic treatment of MSG wastewater, and those have primarily focused on treatment feasi bility and the startup stage of the bioreactor (Jia et al., 2007; Tseng and Lin, 1990). Current studies rarely investigate anaerobic performance and process characteristics, as well as the effects of operational param eters on MSG removal. Therefore, further studies on MSG wastewater anaerobic treatment and energy recovery are still urgently needed. To investigate the anaerobic degradation and energy recovery of MSG from wastewater, a laboratory-scale UASB reactor was operated for 222 days with different OLRs, from 1 g-COD/L/d to 24 g-COD/L/d. The effects of OLR on MSG removal, methane production, and sludge char acteristics were evaluated. The removal capacity and the specific methanogenic activity (SMA), as well as the microbial community of the MSG-degrading sludge were also investigated.
Fig. 1. Schematic diagram of the UASB reactor.
1.0 g-COD/L/d and an HRT of 48 h; (II–VIII) OLR stress periods during days 36–73, 74–97, 98–129, 130–153, 154–175, 176–198, and 199–222 under an HRT of 24 h and OLRs of 2.0, 4.0, 6.0, 8.0, 12.0, 16.0, and 24.0 g-COD/L/d, respectively (Table 1). 2.3. Analytical methods The pH of influent and effluent and the temperature of the mixed liquid were measured with a pH meter (PHSJ-3F, Leici) and a ther mometer (TP101, ER-TET46005P), respectively. Volatile fatty acids (VFAs) and biogas composition were analyzed by gas chromatography (GC9790 II, Fuli) with a flame ionization detector and a thermal con ductivity detector using helium as the carrier gas. The alkalinity, COD, TSS, VSS, and ammonia nitrogen were measured according to standard methods (APHA, 2005). Extracellular polymeric substance (EPS) extraction and analysis were carried out mainly based the literature (Basuvaraj et al., 2015; Lu et al., 2015). Proteins (PN) and poly saccharides (PS) were determined by the Lowry procedure and the phenol-sulfuric acid method (Lu et al., 2015; Xiao et al., 2018), respectively. The particle size distribution of the granules taken from the bottom sampling port of the column was determined by sieve analysis (pore sizes of meshes were approximately 3.0, 2.0, 1.0, and 0.5 mm). The mean settling velocity of the granules in the UASB reactor was evaluated by using settling column tests (Lu et al., 2015; Wu et al., 2018).
2. Materials and methods 2.1. Synthetic wastewater and seed sludge Digested sludge taken from the Xinkaipu wastewater treatment plant (WWTP) (Changsha City, China) mixed with anaerobic granular sludge obtained from a UASB reactor of a starch processing WWTP (Kaitian, Hohhot City, China) were used as the seeding cultures (mixed ratio 2:3). The initial granular sludge, consisting of granular pellets with a diam eter of 0.3–3.0 mm and a sedimentation velocity of 1.5–3.0 cm/s, had a mixed liquor volatile suspended solids (VSS) of 30.05 g/L. Analytical reagents, including L-glutamate, were used for synthesis of the MSG wastewater. NaHCO3 was added at a concentration of 1500 mg/L to ensure pH stability. The other constituents of the synthetic wastewater were as follows: K2HPO4, 250 mg/L; KH2PO4, 100 mg/L; KCl, 300 mg/L; MgCl2�6H2O, 50 mg/L; ZnCl2, 0.6 mg/L; NiCl2�6H2O, 0.81 mg/L; CuCl2�2H2O, 0.3 mg/L; (NH4)6Mo7O24�4H2O, 0.65 mg/L; FeCl2�4H2O, 3.56 mg/L; CaCl2, 15 mg/L; and CoCl2�6H2O, 0.4 mg/L.
2.4. Specific methanogenic activity test
2.2. Experimental apparatus and its operation
The specific methanogenic activity (SMA) of the granular sludge sampled from the UASB reactor was evaluated by serum bottle tests using L-glutamic acid, methanol, sodium acetate, propionic acid, butyric acid, valeric acid, and H2–CO2 as substrates. First, an inoculum of 2 g wet sludge, the nutrient solution and buffers with constant target con centrations were successively placed into 120 mL serum bottles. After individually adding the specific substrates of 2000 mg/L (expect for H2–CO2), the vials were subsequently diluted to 80 mL with deoxygen ated distilled water and sealed with rubber stoppers secured by an aluminum crimp. Then, the serum bottles were flushed with nitrogen gas for 5 min to remove oxygen in the headspace and 0.2 mL sodium sulfate solution (2500 mg/L) was injected as the reducing agent to maintain absolutely anaerobic conditions. For feeding with H2–CO2 as a substrate, after the addition of the inoculum, nutrient solution, and buffers, the vial was diluted to 80 mL and sealed, and the headspace of the bottle was
A schematic diagram of the lab-scale UASB reactor is illustrated in Fig. 1. The reactor was made of plexiglass with an effective volume of 6 L. The setup consisted of a main UASB body, a synthetic wastewaterfeeding unit, a temperature control unit, a biogas gauge, and discharge unit. The main body included a rectangular column with a height of 800 mm, an inner diameter of 100 mm, and a three-phase separator mounted at the top. A wet gas meter (LMF-1, Jingzhiye) was installed to monitor the daily biogas. Then, the biogas volume produced under normal conditions was converted to STP (T ¼ 273.15 K, P ¼ 100 kPa) using the ideal gas equation. The operational temperature was maintained at 35 � 2 � C by the temperature control unit. The UASB reactor was continuously operated for 222 days divided into eight phases: (I) the startup stage from day 0 to day 35 at an OLR of 2
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Table 1 Operating conditions of the lab-scale UASB reactor during the 222-day period. Phases
OLR (g-COD/L/d)
Flow Rate (m3/d)
COD (mg/L)
HRT (h)
Temperature (� C)
pH
Days
I II III IV V VI VII VIII
0.96 � 0.25 2.08 � 0.12 4.93 � 0.26 6.03 � 0.36 7.92 � 0.25 12.05 � 0.50 16.17 � 0.87 24.07 � 0.64
0.003 0.006 0.006 0.006 0.006 0.006 0.006 0.006
1912.57 � 251.59 2078.44 � 124.89 4930.52 � 263.30 6031.84 � 362.09 7915.85 � 254.87 12054.89 � 502.20 16171.43 � 869.86 24070.31 � 638.41
48 24 24 24 24 24 24 24
35 � 2
7.0 � 0.1 7.0 � 0.1 7.0 � 0.1 7.0 � 0.1 7.0 � 0.1 7.0 � 0.1 7.0 � 0.1 7.0 � 0.1
1–35 36–73 74–97 98–129 130–153 154–175 176–198 199–222
replaced with pressurized gas of H2–CO2 (80:20, v/v) to obtain a target pressure of 1.4 atm. Finally, all bottles were incubated in a shaking water bath at 35 � 1 � C at 100 � 5 rpm. Biogas production and compo sition were measured at 2–6 h intervals and expressed as the value at STP. More details can be found in the literature (Kong et al., 2018; Lu et al., 2015; Wu et al., 2018).
mg/L) was calculated with Eq. (3). ΔN ¼ Nin
ORR ¼
Sludge samples collected from the bottom of the UASB reactor at the end of phases I and VIII were used to analyze the taxonomic patterns of microbial communities during the operation. Genomic DNA was extracted by a FastDNA® SPIN Kit (FastDNA® SPIN kit for soil, MPbio, Inc., CA). The V3–V4 hypervariable regions of the bacteria 16S rRNA gene were amplified with primers 338 F (50 -ACTCCTACGGGAGGCAG CAG-30 ) and 806 R (50 -GGACTACHVGGGTWTCTAAT-30 ) using a ther mocycler PCR system (GeneAmp 9700, ABI, USA). PCR reactions were performed in triplicate with 20 μL mixtures containing 4 μL of 5 � FastPfu Buffer, 2 ìL of 2.5 mM dNTPs, 0.8 ìL of each primer (5 ìM), 0.4 ìL of FastPfu polymerase and 10 ng of template DNA. The PCR re actions were conducted using the following program: 3 min of dena turation at 95 � C, 35 cycles of 30 s at 95 � C, 30 s for annealing at 55 � C, and 45 s for elongation at 72 � C, and a final extension at 72 � C for 10 min. The V3–V4 hypervariable regions of the archaea 16S rRNA gene were amplified with primers 524F10extF (TGYCAGCCGCCGCGGTAA) and Arch958RmodR (YCCGGCGTTGAVTCCAATT) by a thermocycler PCR system (GeneAmp 9700, ABI, USA). The resulting PCR products were extracted from a 2% agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™ -ST (Promega, USA) according to the manufacturer’s protocol. The final DNA concentration was determined using a NanoDrop 2000 UV–vis spectrophotometer (Thermo Scientific, Wilmington, USA). Sequencing was accomplished using Illu mina’s Miseq PE300 platform (Shanghai Meiji Biomedical Technology Co., Ltd.). UPARSE software (version 7.1 http://drive5.com/uparse/) was used to perform OTU clustering on sequences based on 97% simi larity. Chimeras were eliminated using the UCHIME software.
η¼
Cout Þ � 1000 � V V � T=24
(4)
Cin
Cout � 100 Cin
(5)
The specific methanogenic activity (SMA, g-CODCH4/g-VSS/d) was calculated with Eq. (6). SMA ¼
24 � Rmax CF � V2 � VSS
(6)
where Rmax is the maximum methane production rate (mL/h) obtained using the modified Gompertz function, CF is the conversion coefficient between methane (mL) and COD (g) with saturated water vapor (418 mL CH4/(g-CODCH4) @ 35 � C), VSS is the sludge concentration (g-VSS/L) and V2 (L) is the volume of the mixture in the serum bottle. 3. Results and discussion 3.1. Overall performance of the UASB reactor under various OLRs The overall performance of the UASB reactor in terms of pH, tem perature, alkalinity, COD, gas production rate, NHþ 4 -N, and VFAs is illustrated in Fig. 2. As the OLR increased, the pH and alkalinity (both bicarbonate alkalinity and total alkalinity) in the influent remained stable, whereas those in the effluent increased (Fig. 2A and B). This increment resulted from the increase in products, including carbon di oxide or hydro-carbonate and ammonia, because of decomposition of the feeding glutamate. Fig. 2C reflects the dynamic changes of COD in the influent and effluent, as well as the removal efficiency under different OLRs. When the OLR increased from 1 g-COD/L/d to 8 g-COD/ L/d, the COD removal efficiency reached up to 97.9%. In comparison, over 93% of chemical oxygen demand (COD) was effectively removed through anaerobic degradation of dimethyl phthalate, and the highest COD removal was 76.6% in biodegradation of MSG wastewater (Kong et al., 2018; Jia et al., 2007). As the OLR increased to 24 g-COD/L/d, the removal efficiency markedly dropped to approximately 60%. Biogas and methane production rates increased with the increase in OLR, which reached as high as 8.6 L/L/d and 4.5 L/L/d in phase VIII (Fig. 2D). The ammonia nitrogen concentrations in the influent and effluent, as well as the free ammonia (FA) concentration in the system are illustrated in Fig. 2E. As the OLR increased during the whole operation period, concentrations of ammonia nitrogen and FA in the system also itera tively increased. When the OLR exceeded 8 g-COD/L/d, the effluent
The methane yield was calculated with Eq. (1). (1)
whereY (L/g-COD) is methane yield, V1 (L/d) is the volume of methane produced daily, Cin (mg-COD/L) is the influent concentration, V (L) is the working volume of the reactor, and HRT (h) is the hydraulic reten tion time. The methane production rate was calculated as follows. y ¼ V1 =V
ðCin
where Cout (mg COD/L) is the effluent concentration. The COD removal efficiency (η, %) was calculated using Eq. (5).
2.6. Calculations
V1 24 Cin � 1000 � V � HRT
(3)
where Nin and Nout (mg/L) are the influent and effluent ammonia ni trogen concentrations (mg/L), respectively. The organic removal rate (ORR, g-COD/L/d) was calculated with Eq. (4).
2.5. Microbial community analyses
Y¼
Nout
(2)
where y (L CH4/g COD) is the CH4 production rate. The difference between influent and effluent ammonia nitrogen (ΔN, 3
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Fig. 2. Operational performance of the UASB reactor run continuously under different OLRs. (A) pH and temperature. (B) Alkalinity. (C) COD concentration and removal efficiency. (D) Gas production rate. (E) Ammonia nitrogen and free ammonia concentrations. (F) VFA concentrations.
ammonia nitrogen and the FA reached over 1200 mg/L and 100 mg/L, respectively. However, methane production was inhibited in this system when FA accumulated at a level of 40–100 mg/L or the ammonia ni trogen concentration was higher than 6000 mg/L (Shi et al., 2017; Tian et al., 2018). Fig. 2F clearly shows that VFA accumulation began during phase V (OLR of 8.0 kg COD/m3/d), and the total VFA concentration reached over 2000 mg/L in phase VIII (OLR of 24.0 kg COD/m3/d). Among VFAs, acetate was the largest fraction. Heavy VFA accumulation can decrease the ORR and COD removal efficiency, as well as repress the biochemical reactions in anaerobic digestion (Ketheesan and Stuckey, 2015; Lu et al., 2015; Niu et al., 2015). These results indicated that methanogenesis was the rate limiting step for anaerobic degradation under high OLRs (Xiao et al., 2018; Zhang and Fang, 2015).
0.52 � 0.04 L/L/d to 4.22 � 0.28 L/L/d with the increase of OLR from 2 g-COD/L/d to 24 g-COD/L/d, the CH4 yield increased from 0.25 � 0.03 L/g-COD (2 g-COD/L/d) to 0.28 � 0.03 L/g-COD (8 g-COD/ L/d) and then decreased to 0.18 � 0.02 L/g-COD (24 g-COD/L/d) (Fig. 3B). The above results indicate that 8 g-COD/L/d OLR corre sponded to the maximum removal efficiency and the maximum methane yield, implying that superior glutamate removal by the UASB reactor. The methane component in the biogas produced under various OLRs is displayed in Fig. 3C. The percentage of CH4 in the biogas continuously decreased from 74.1% to 50.8% with the increase of OLR from 2 g-COD/ L/d to 24 g-COD/L/d. Overall, the good methane production is expected to supplement energy requirements, despite the reduced methaneproducing performance at high OLRs. Removal capacity is an important index for pollutant removal by bioreactors (Chen et al., 2018; Cheng et al., 2016). The removal ca pacities of glutamate from wastewater by the UASB reactor under various OLRs are presented in Fig. 4. When the OLR was below 8 g-COD/L/d, the glutamate removal efficiency was close to 100%, and the corresponding removal capacity was approximately equal to the OLR. However, the removal capacity was 11.2 g-COD/L/d, 14.1 g-COD/L/d and 17.1 g-COD/L/d at OLRs of 12 g-COD/L/d, 16 g-COD/L/d and 24 g-COD/L/d, respectively. In this study, the maximum removal capacity reached was approximately 18 g-COD/L/d,
3.2. Organic removal and biogas production under various OLRs The effects of OLR on the reactor performance including organic removal and biogas production are shown in Fig. 3. The ORR increased from 1.95 � 0.13 g/L/d to 15.61 � 1.02 g/L/d with an increase of OLR from 2 g-COD/L/d to 24 g-COD/L/d, whereas the COD removal effi ciency increased from 93.79 � 4.13% (2 g-COD/L/d) to 97.96 � 0.61% (8 g-COD/L/d) and then decreased to 65.36 � 5.16% (24 g-COD/L/d) (Fig. 3A). Although the CH4 production rate increased from 4
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increase in OLR, the COD in the effluent increased to the highest value in phase VIII, up to 34.8 � 5.2%. The recovered CH4-COD in the gas phase dominated the COD flow of the substrate in each operational phase, which demonstrated good energy recovery from the glutamate waste water treated by the UASB reactor. The distribution of COD flow was different among the different OLRs over the steady-state period, which is consistent with a lab-scale UASB reactor treating starch wastewater under different hydraulic retention times (HRTs) (Lu et al., 2015). In phases II–VII, the CH4-COD recovery decreased with the increase of OLR, although it still reached up to 70.6 � 5.6% in phase V. In phase VIII, the CH4-COD recovery reached a minimum of 54.0 � 4.2%, which was still the largest part of the COD distribution. In terms of removal efficiency and energy recovery, the optimal OLR for MSG wastewater treatment in a UASB is no more than 8 g-COD/L/d. 3.4. Characteristics and composition of sludge The results for the sludge component, VSS concentration, VSS/TSS ratio, sludge volume fraction in particle size, and average settling ve locity at different OLRs from 1 g-COD/L/d to 24 g-COD/L/d are shown in Fig. 5. During the operation of the reactor, the EPS content of the sludge in the reactor first increased and then decreased slightly with the increase of OLR (Fig. 5A). At a moderate OLR (6–12 g-COD/L/d), both the PN and the PS in the EPS were maintained at a high level that could provide better conditions for sludge granulation. The ratio of PN/PS in the EPS component was in the range of 1.3–2.3 with a tendency to have an initial decrease and a subsequent increase. A relatively low PN/PS ratio was obtained in phases II–V, which implied a good performance in sludge granulation (Basuvaraj et al., 2015). As shown in Fig. 5B, when the reactor operated at OLRs of 1–8 gCOD/L/d, the VSS of the sludge in the reactor increased from 20.4 g/L to 47.6 g/L, and the VSS/TSS ratio increased from 40.9% to 60.8%. As the OLR continuously increased to 24 g-COD/L/d, the VSS concentration and the VSS/TSS ratio were reduced to 22.5 g/L and 44.9%, respec tively. The results herein indicate that the activated biomass (Lu et al., 2015) that accumulated in the reactor increased at first and then decreased. These suggest that high OLRs (over 16 g-COD/L/d) were inferior for the accumulation and activity of the granules in the UASB reactor. The sludge fraction ratio in particle size, as well as the granule settling velocity under different OLRs from 1 g-COD/L/d to 24 g-COD/ L/d are displayed in Fig. 5C. The volume fraction of particle diameter greater than 0.5 mm was 59.8%, 62.2%, 65.3%, 72.2%, 79.6%, 77.8%, 74.3%, and 75.6% in each phase, respectively. The average particle size of the sludge increased with the increase of OLR at its middle-low level and provided more substrate entering the interior of the granules (Li and Yang, 2007). However, a high OLR could lead to a decrease of average granule particle diameter for the high concentrations of substrate and intermediate products (Lu et al., 2017). The average settling velocity of the granular sludge in each phase was 59.8 m/h, 62.2 m/h, 65.3 m/h, 72.2 m/h, 79.6 m/h, 77.8 m/h, 74.3 m/h, and 75.6 m/h, respectively. The maximum settling velocity of the granules appeared in phase V, which indicated good sedimentation performance (Li et al., 2018). The trend of an initial increase and subsequent decrease observed in both the sludge sedimentation rate and the particle size indicates that a superior sludge granulation (Ghangrekar et al., 2005; Zhou et al., 2007) was obtained under moderate OLRs, whereas high OLRs had a negative effect on the particle size of granular sludge. These results are consistent with the overall performance of the reactor (Fig. 2C).
Fig. 3. Effect of OLR on UASB performance. Profiles of (A) organic removal rate (ORR) and COD removal efficiency, (B) CH4 production rate and CH4 yield, and (C) biogas composition.
Fig. 4. Removal capacities of the UASB reactor for MSG wastewater treatment.
whereas the critical load ranged from 1 g-COD/L/d and 8 g-COD/L/d. 3.3. COD mass balance
3.5. Specific methanogenic activity and probable degradation pathway
The mass balance of COD in glutamate treatment by the UASB reactor under various OLRs is summarized in Table 2. The COD flowing out of the reactor was mainly composed of three parts: the ratio of COD in the effluent, the ratios converted into methane recovered through the gas phase, and methane flushed out by dissolving in the effluent. With an
The SMA tests were conducted in phases I, V, and VII using Lglutamate, methanol, acetate, propionate, butyrate, valerate, and H2–CO2 as substrates. The results are shown in Table 3. 5
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Table 2 Distributions of COD under various OLRs. Phases I II III IV V VI VII VIII
CODinf (mg/L) 1927.6 � 180.8 2078.5 � 124.9 3919.8 � 187.3 6023.1 � 362.1 7919.4 � 254.9 12086.5 � 477.1 16695.4 � 841.9 23944.5 � 590.7
CODmeth
CODeff
CODsol
CODCH4
recovery
(mg/L)
(%)
(mg/L)
(%)
(mg/L)
(%)
(mg/L)
(%)
1397.5 � 57.8 1538.1 � 95.6 2877.1 � 78.4 4366.7 � 96.4 5424.8 � 388.1 7614.5 � 471.4 9867.0 � 1235.5 12546.9 � 885.9
72.5 � 3.0 74.0 � 4.6 73.4 � 2.0 72.5 � 1.6 68.5 � 4.9 63.0 � 3.9 59.1 � 7.4 52.4 � 3.7
171.6 � 69.4 124.7 � 66.5 294.0 � 78.4 283.1 � 114.4 166.3 � 71.3 979.0 � 217.5 2521.0 � 450.8 8332.7 � 1245.1
8.9 � 3.6 6.0 � 3.2 7.5 � 2.0 4.7 � 1.9 2.1 � 0.9 8.1 � 1.8 15.1 � 2.7 34.8 � 5.2
48.2 � 7.7 56.1 � 12.5 101.9 � 11.8 144.6 � 12.0 166.3 � 55.4 229.6 � 60.4 300.5 � 150.3 383.1 � 119.7
2.5 � 0.4 2.7 � 0.6 2.6 � 0.3 2.4 � 0.2 2.1 � 0.7 1.9 � 0.5 1.8 � 0.9 1.6 � 0.5
1445.7 � 65.5 1594.2 � 108.0 2979.1 � 90.2 4511.3 � 108.4 5591.1 � 443.5 7844.1 � 531.8 10167.5 � 1385.7 12930.0 � 1005.7
75.0 � 3.4 76.7 � 5.2 76.0 � 2.3 74.9 � 1.8 70.6 � 5.6 64.9 � 4.4 60.9 � 8.3 54.0 � 4.2
Note: CODmeth, recovered CH4-COD in gas phase; CODsol, soluble CH4-COD calculated in the effluent; CODCH4 recovery, which represents the CH4-COD recovery. Table 3 Specific methanogenic activity (SMA) detected in phases I, V, and VII. Substrates Methanol Acetate Propionate Butyrate Valerate L-Glutamate H2–CO2
SMA (g-CODCH4/g-VSS/d)
Rmax (mL/h)
I
V
VII
I
V
VII
0.038 0.020 0.010 N-Da N-Da 0.013 0.051
0.033 N-Da 0.009 0.047 0.017 0.086 0.040
0.033 0.085 0.008 0.074 0.013 0.070 0.030
1.50 0.79 0.40 N-Da N-Da 0.50 1.98
1.51 N-Da 0.43 2.12 0.79 3.89 1.82
1.14 2.98 0.26 2.59 0.46 2.47 1.06
N-Da: not detected.
resulting from H2–CO2. When using H2–CO2, the SMA exhibited a slightly decreasing trend over time that indicated a shrinking meth anogenic pathway of via H2–CO2. However, when fed using L-glutamate, the SMA increased from 0.013 g-CODCH4/g-VSS/d in phase I to 0.086 gCODCH4/g-VSS/d in phase V, and then decreased to 0.070 g-CODCH4/gVSS/d in phase VII. These results indicated that the sludge degrading ability on L-glutamate had been reduced and was likely inhibited by high concentrations of intermediate products (NHþ 4 , VFAs). SMA may also reveal the possible degradation pathway (Liang et al., 2007). The glutamate-degrading sludge was capable of degrading L-glutamate, butyrate, and acetate, but not propionate and valerate. Although amino acids can act as both acceptors and donors when degraded via a Stickland reaction under anaerobic conditions (Zhang and Fang, 2015), glutamate degraded in the UASB reactor seemed to follow other paths, as shown by the small value of SMA achieved on valerate (Table 2) and the lack of valerate that accumulated in the reactor under high OLRs (Fig. 2F). According to the SMA in phase I, the methanogenic pathway through H2 and CO2 is preferred over the pathway than through acetate. However, judging from the glutamate-degrading sludge’s SMA data in phase VIII, glutamate was likely degraded to butyrate after deamination, which then further preferred to degrade into acetate. Along with the VFA data shown in Fig. 2F, these results reveal that a large amount of acetate and some proportion of butyrate accumulated in phases VII and VIII, which also supports the proposed degradation pathways. The results in Table 3 also indicate that the SMA of the sludge in phase V was the highest for degrading L-glutamate, whereas the SMA (0.070 g-CODCH4/g-VSS/d) in phase VII (with high OLR) was lower than the SMA values for degrading the downstream intermediates, i.e., 0.074 g-CODCH4/g-VSS/d for acetate and 0.085 g-CODCH4/g-VSS/d for butyrate. High OLR could lead to the unrecoverable decay of meth anogenic activity and a serious imbalance between substrate uptake and biological activities that are directly affected by high concentrations of ammonia and VFAs (Zhou et al., 2007). The results revealed that the initial deamination was not a rate-limiting step, but the probable degradation pathway was affected by high OLRs during glutamate anaerobic degradation.
Fig. 5. Characteristics of the granular sludge in the UASB reactor under various OLRs. (A) EPS content and PN/PS ratio. (B) VSS concentration and VSS/TSS ratio. (C) Sludge volume fraction in particle size and average settling velocity of the granules.
The glutamate-degrading sludges taken from the UASB reactor at different phases, varied substantially in their substrate degrading abili ties. The maximum methane production rate (Rmax) of the sludge increased markedly with time when using acetate or butyrate, whereas it decreased significantly when using propionate, valerate, or H2–CO2. The SMA also increased with time when fed with acetate or butyrate, ranging from 0.020 to 0.085 g-CODCH4/g-VSS/d and 0.047–0.074 g-CODCH4/gVSS/d, respectively. The highest SMA of sludge in phase VII resulted from using acetate as the substrate and was much higher than that 6
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aromatic compounds, into acetate (Tian et al., 2018; Tsavkelova et al., 2018). The observation that Firmicutes dominated in phase VIII is consistent with the aforementioned VFAs’ massive surge at an OLR of 24 g-COD/L/d (Fig. 2F). However, Actinobacteria, which are capable of utilizing organic matter, such as glucose, maltose, starch, and dextrin (Gupta et al., 2014), markedly decreased from 24.29% in phase I to 0.12% in phase VIII. The probable explanation is the lack of substrate supply and inhibition caused by the high ammonia concentration. These results suggest that the operational OLR had a substantial effect on the bacterial community structure and the probable degradation pathway of glutamate. The Shannon diversity index for archaeal and bacterial for commu nity structure in the sludge samples was determined at 97% sequence similarity (Table 4). The observed number of operational taxonomic units (OTUs) of bacterial sequences were as high as 525, indicating high bacterial communities in both the sludge samples. The sludge sample taken during phase I showed a slightly higher alpha diversity of archaeal community and a much lower alpha diversity of bacterial community compared to phase VIII, which reflects the response of the constructed ecosystem to changes in its environmental conditions. Under high OLR conditions, the obvious decline in the biodiversity of the bacterial community could be attributed to the increase in glutamate loadings, as well as ammonia and VFA accumulation, indicating the vulnerability of the ecosystem and a reduced ability to resist environmental stress (Feranchuk et al., 2018). With the gradual increase of OLR, VFAs (mainly acetic acid) increased, which were conducive to the growth of
3.6. Microbial community structure and diversity The archaeal and bacterial microbial community structure and di versity in the sludge were analyzed by high throughput sequencing. Samples were taken from the UASB reactor during phase I and phase VIII. Genus Archaea and phylum Bacteria, which occupied a relative abundance �1% in both phases, are illustrated in Fig. 6, whereas un cultured sequences were classified as others. In the case of Archaea, the predominance of genera shifted from Methanobacterium (with a relative abundance of 59.83%) and Meth anosaeta (34.40%) to Methanobacterium (47.98%) and Methanosarcina 35.12%) (Fig. 6A). The hydrogenotrophoic genus Methnobacterium dominated both phases, and can utilize hydrogen as a substrate during the anaerobic degradation process (Luo and Angelidaki, 2012; Zhang and Fang, 2015). The acetotrophic genus Methanosaeta (Kong et al., 2018; Niu et al., 2015) significantly decreased in abundance, whereas Methanosarcina, which possesses all three known pathways for meth anogenesis (Gupta et al., 2014), sharply increased during phase VIII. This supports the idea that Methanosarcina easily dominate the archaeal community at the highest ammonia levels (Tian et al., 2018). These results revealed that the OLR had a substantial effect on the shift in the archaeal community during the entire operational period. Eight known bacterial phyla, namely Firmicutes, Actinobacteria, Synergistetes, Proteobacteria, Chloroflexi, Bacteroidetes, Thermotogae, and Candidatus Cloacimonetes are found to coexist in the granular sludge in both phases (Fig. 6B). Among them, Firmicutes, Proteobac teria, Chloroflexi and Bacteroidetes regularly appeared in the anaerobic systems (Sun et al., 2015; Tian et al., 2015; Wu et al., 2018). It should be noted that Firmicutes, Synergistetes, and Bacteroidetes were much higher in abundance in phase VII than in phase I, and Firmicutes was clearly predominant in phase VIII, with 51.40% abundance. Highly correlated with hydrogenotrophic methanogens (Hattori, 2008), the predominant Firmicutes, including the class Clostridia (with 21.36% abundance in phase I and 47.51% abundance in phase VIII), were mainly responsible for converting monomers, such as alcohol, propionate, other short-chain fatty acids (VFAs), some amino acids, and
Table 4 Alpha diversity index of the microbial community structure in the sludge samples. Samples Archaea Bacteria
Phase I Phase VIII Phase I Phase VIII
Effective reads
OTUs
Shannon
Coverage
66791 62790 62177 65,308
24 24 525 525
1.488 1.506 3.719 3.516
0.999 1 0.999 0.998
Fig. 6. Microbial community structure in the continuous UASB reactor. Genus Archaea (A) and phylum bacteria (B) in phases I and phase VIII. 7
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acetotrophic methanogens. In contrast, the relative abundance of ace totrophic methanogens increased as the SMA value of hydrotrophic methanogens decreased, indicating that the dominant methanogenic degradation pathway changed. Meanwhile, biological metabolism was influenced by the increasing concentration of ammonia nitrogen. Aside from Methanobacterium and Methanosarcina, other methanogens were suppressed to varying degrees. Therefore, the OLR had a substantial influence on the diversity of the microbial community structure in the UASB reactor.
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4. Conclusions We conducted systematic treatment of monosodium glutamate (MSG) wastewater using a lab-scale up-flow anaerobic sludge blanket reactor under various organic loading rates (OLRs). The optimal OLR was lower than 8 g-COD/L/d with a maximum CH4 yield of 0.28 � 0.03 L/g-COD, which was consistent with highest COD removal efficiency up to 97.9%. The maximum removal capacity reached was approximately 18 g-COD/L/d. Increased accumulation of free ammonia and volatile fatty acids resulted in a decrease in the reactor performance, specifically in the glutamate removal efficiency, methane production rate, and granular sludge diameter. Based on the SMA data and the changes in the microbial community, the OLR had a substantial influ ence on the reactor performance and the microbial community varia tions of the UASB for MSG wastewater treatment because of the accumulation of VFAs and ammonia nitrogen degraded from glutamate. Declaration of interests The authors have no conflict of interest to declare. Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 51308068, 51706022) and the China Hunan Provincial Science & Technology Department (No. 2017SK2361). The authors gratefully acknowledge the support from professor Yu-You Li (Tohoku University, Japan), the Japan Society for the Promotion of Science and the Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). We would like to thank Editage (www.editage.cn) for English language editing. References APHA, 2005. Standard Methods for the Examination of Water and Wastewater. APHAAWWA-WEF, Washington, DC, USA. Basuvaraj, M., Fein, J., Liss, S.N., 2015. Protein and polysaccharide content of tightly and loosely bound extracellular polymeric substances and the development of a granular activated sludge floc. Water Res. 82, 104–117. Chen, H., Wei, Y.X., Peng, L., Ni, J.L., Guo, Y., Ji, J.Y., Jiang, B., Yu, G.L., 2018. Longterm MIBK removal in a tubular biofilter: effects of organic loading rates and gas empty bed residence times. Process Saf. Environ. 119, 87–95. Chen, H., Wu, J., Liu, B., Li, Y.Y., Yasui, H., 2019. Long-term competitive dynamics of anaerobic microbials in a UASB for a sulfate-rich wastewater treatment by extending Anaerobic Digestion Model No. 1 (ADM1) with sulfate reduction. Bioresour. Technol. 280, 173–182, 2019. Cheng, Y., He, H.J., Yang, C.P., Zeng, G.M., Li, X., Chen, H., Yu, G.L., 2016. Challenges and solutions for biofiltration of hydrophobic volatile organic compounds. Biotechnol. Adv. 34 (6), 1091–1102. Dong, L., Li, Y., Wang, P., Feng, Z., Ding, N., 2018. Cleaner production of monosodium glutamate in China. J. Clean. Prod. 190, 452–461. Feranchuk, S., Belkova, N., Potapova, U., Kuzmin, D., Belikov, S., 2018. Evaluating the use of diversity indices to distinguish between microbial communities with different traits. Res. Microbiol. 169, 254–261. Ghangrekar, M.M., Asolekar, S.R., Joshi, S.G., 2005. Characteristics of sludge developed under different loading conditions during UASB reactor start-up and granulation. Water Res. 9 (6), 1123–1133. Gupta, M., Velayutham, P., Elbeshbishy, E., Hafez, H., Khafipour, E., Derakhshani, H., Naggar, M.H.E., Levin, D.B., Nakhla, G., 2014. Co-fermentation of glucose, starch, and cellulose for mesophilic biohydrogen production. Int. J. Hydrogen Energy 39, 20958–20967.
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