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Stabilizing lactate production through repeated batch fermentation of food waste and waste activated sludge
Bioresource Technology 300 (2020) 122709
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Stabilizing lactate production through repeated batch fermentation of food waste and waste activated sludge
T
Xianbao Xua, Wenjuan Zhanga, Xia Gua, Zhichao Guoa, Jian Songa, Daan Zhua, Yanbiao Liua, ⁎ Yanan Liua, Gang Xuea,b, Xiang Lia, , Jacek Makiniac a
College of Environmental Science and Engineering, Donghua University, 2999 North Renmin Road, Shanghai 201620, China Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China c Faculty of Civil and Environmental Engineering, Gdansk University of Technology, ul. Narutowicza 11/12, 80-233 Gdansk, Poland b
A R T I C LE I N FO
A B S T R A C T
Keywords: Lactic acid Co-fermentation Waste activated sludge Food waste
Bio-valorization of organic waste streams, such as food waste and waste activated sludge, to lactic acid (LA) has recently drawn much attention. It offers an opportunity for resource recovery, alleviates environmental issues and potentially turns a profit. In this study, both stable and high LA yield (0.72 ± 0.15 g/g total chemical oxygen demand) and productivity rate (0.53 g/L•h) were obtained through repeated batch fermentation. Moreover, stable solubilization and increase in the critical hydrolase activities were achieved. Depletions of ammonia and phosphorus were correlated with the LA production. The relative abundance of the key LA bacteria genera (i.e., Alkaliphilus, Dysgonomonas, Enterococcus and Bifidobacterium) stabilized in the repeated batch reactor at a higher level (44.5 ± 2.53%) in comparison with the batch reactor (26.2 ± 4.74%). This work show a practical way for the sustainable valorization of organic wastes to LA by applying the repeated batch mode during biological treatment.
1. Introduction Huge amounts of food waste (FW) may result in public and environmental health concerns when treated inappropriately (Gunasekera, 2015; Li et al., 2019). Valorization of FW, rich in carbon and nutrients, has received a particular interest in recent years (Dahiya et al., 2018; Xu et al., 2018). Lactic acid (LA) is one of the high valueadded platform molecules with an increasing global demand in industry (Tang et al., 2016). Particularly, biological nutrient removal processes can be significantly enhanced using lactate-enriched organic acids derived from FW (Tang et al., 2019). Normally, mixed culture fermentation is considered a sustainable and cost-effective method for LA recovery from FW. However, the short-term batch co-fermentation of FW and waste activated sludge (WAS) from municipal wastewater treatment plants (WWTPs), has recently shown a synergistic effect on enhancement of both hydrolysis and lactate acidification (Fitamo et al., 2016; Li et al., 2015; Xue et al., 2018; Zhang et al., 2017). Considering the application and commercialization for FW treatment, the stable and long-term continuous fermentation of LA is also imperatively required. However, LA is a fast metabolizing intermediate that can be easily converted to volatile fatty acids (VFAs) (Asunis et al., 2019; Huang et al., 2016, ⁎
2019b; Tang et al., 2017). Although lactate fermentation activities could be temporally enhanced by adding WAS during the short-term batch fermentation, addition of fresh WAS inevitably provides active microorganisms both consuming lactate and producing VFAs. Thus, the composition of the mixed microbial community may shift towards competing VFAs producers (Tamis et al., 2015; Zhang et al., 2017), resulting in a challenge of separating lactate acidogenesis from VFAs generation. Till now, the long-term performance of lactate recovery from a mixture of FW and WAS has remained largely unknown. Continuous culture is usually used in large scale application (Cho et al., 2019) and provides a relatively stable and chemostat cultivation for mixed microbiome (Dolejs et al., 2014; Seo et al., 2017; Urbance et al., 2004). It has been reported that the hydraulic retention time (HRT) of 10 d was required for lactate production during semi-continuous fermentation using unsterilized artificial garbage under thermophilic conditions (55 °C) (Akao et al., 2007). Alternatively, repeated batch operation was also applied for improving LA productivity by inoculating pure strain (Abdel-Rahman et al., 2013; Ma et al., 2014; Reddy et al., 2016; Zhang et al., 2014). However, no any reference is available regarding preferable long-term reactor for LA production by co-fermentation of FW and WAS. To develop a stable and reliable approach for lactate production,
Corresponding author. E-mail address: [email protected] (X. Li).
https://doi.org/10.1016/j.biortech.2019.122709 Received 31 October 2019; Received in revised form 25 December 2019; Accepted 27 December 2019 Available online 28 December 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.
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semi-continuous and repeated batch fermentation were first compared in this study. Then, the mechanism of high rate LA production was elaborated through unveiling the fermentatition pathways involved the solubilization, hydrolysis and acidification. The correlations between LA production and several crucial nutrients and intermediates were analyzed. The stability of the microbial community shift and predictive functional profiles were finally evaluated. This paper provides a stable and practical approach for sustainable valorization of organic wastes to LA during biological treatment.
protein and carbohydrates were determined. To evaluate the efficiency of FW solubilization, the increase in soluble substrate (SCOD) and reduction of VSS were recorded in each cycle of the repeated batch reactor. The activities of hydrolase (i.e., α-glucosidase and protease) were evaluated after two days in each cycle (Zhang et al., 2017). Correlation analyses (95% confidence interval) between the LA production and SCOD, carbohydrate, protein, NH4+-N, SOP and pyruvic acid were carried out. 2.3. Microbial community and predictive functional profiles
2. Materials and methods
Illumina Miseq pyrosequencing method was used to evaluate the microbial community structure. Samples were withdrawn from the reactor respectively on the 3rd day from the batch reactor, and the 3rd day during the 4th cycle (lowest LA production) and the 6th cycle (highest LA production) from the repeated batch reactor. Genomic DNA was extracted by TIANamp Soil DNA Kit (TIANGEN) from each of the triplicate samples (the batch reactor, the 4th and 6th cycle of the repeated batch reactor) in the corresponding sets, and pooled together to minimize the potential variation (Huang et al., 2019a). The extracted DNA was amplified by PCR using the primer 338F (5′-ACTCCTACGG GAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) in V3-V4 region. The closest matching sequences were compared with the reference sequences in the GenBank database of BLAST (http://blast. ncbi.nlm.nih.gov). Raw sequences were deposited in the NCBI Short Read Archive (Dahiya et al., 2018) database under the accession number SRR7778920, SRR7778919 and SRR7778924. Functional capabilities of the microbial community were analyzed according to PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) using 16S rRNA marker gene sequences (Langille et al., 2013). The hierarchical database adopted in the analysis is provided at three levels which can be collected from the KEGG PATHWAY Database.
2.1. WAS and FW The WAS used in this study was collected from a municipal WWTP in Shanghai, China and the sludge was settled at 4 °C for 24 h to decant the supernatant before use. The main characteristics of concentrated WAS were as follows: pH 6.9 ± 0.2, total suspended solids (TSS) 16.6 ± 1.45 g/L, volatile suspended solids (VSS) 10.3 ± 0.64 g/L, total chemical oxygen demand (TCOD) 14.6 ± 1.29 g/L. FW, collected from the campus canteen in Donghua University, was milled into a slurry state and diluted with tap water to make the characteristics as follows: TSS 239 ± 2.36 g/L, VSS 238 ± 1.93 g/L, TCOD 181 ± 2.89 g/L. The treated WAS and FW were stored at 4 °C in a refrigerator prior to use. 2.2. Operation of the batch reactor, semi-continuous reactor and repeated batch reactor Co-substrates were first prepared by mixing FW and WAS at the VSS ratios of 6:1 (Li et al., 2015) and diluted with tap water to make the final TCOD around 40 ± 1 g/L, similar to previous study (Li et al., 2017). A batch reactor (working volume of 1 L), containing 1 L of cosubstrates, was mechanically stirred (120 rpm) and operated under the optimal conditions for 4 days as proposed by (Zhang et al., 2017). The process temperature was controlled at 35 ± 1 °C in water bath and pH was adjusted to 9.0 ± 0.1 using 10 M sodium hydroxide solution or 3 M hydrochloric acid solution every 6 h. To start semi-continuous operation, 750 mL of the fermentation mixture was withdrawn from the batch reactor and mixed with 250 mL of the fresh co-substrates in a semi-continuous reactor (working volume of 1 L). Every day, 250 mL of the fermentation mixture was replaced by the same volume of fresh cosubstrates. Then, the semi-continuous reactor was operated for 20 d under the same conditions as the batch reactor. On the other hand, 800 mL of the fresh co-substrates and 200 mL of the fermentation mixture from the batch reactor were mixed in the repeated batch reactor (working volume 1 L). Then, 800 mL of the fermentation broth was withdrawn from the repeated batch reactor (residual 200 mL was retained) every 4 days, which was the optimal duration in previous study (Zhang et al., 2017), and replaced by 800 mL of the fresh cosubstrates. The repeated batch reactor was operated at eight cycles for 32 days under the same conditions as the batch reactor. The withdrawn samples from all the reactors were used for chemical analyses. The concentrations of levorotatory lactic acid (L-LA), dextrorotatory lactic acid (D-LA), VFAs, NH4+-N, soluble orthophosphate (SOP), pyruvate,
2.4. Analytical methods Samples withdrawn from each reactor were filtered by membrane filters with a pore size of 0.45 μm for further chemical analyses. LA with L- and D-isomer were measured by a high-performance liquid chromatograph (Thermol Ultimate 3000, USA), which was equipped with Astec CLC-D (5 mm, 15 cm × 4.6 mm) column and detected at the wavelength of 254 UV. The mobile phase was copper sulfate solution (5 mM) with a flow rate of 1.0 mL/min. The VFAs were detected by Agilent GC 7820 with flame ionization detector and equipped with a DB-WAX column (30 m × 1.0 mm × 0.53 mm). The methods for TSS, VSS, SCOD, carbohydrate, protein, NH4+-N, and SOP were referred to previous studies (Li et al., 2015; Zhang et al., 2017). The method for αglucosidase and protease was listed in Table 1 according to (Goel et al., 1998). 2.5. Calculations The optical activity (OA) of L-LA (OA-L) was calculated according to the following equation:
Table 1 Method for α-glucosidase and protease measurement (Goel et al., 1998). Enzyme assay
Substrate
Incubation conditions
Mixture composition
Termination
α-glucosidase
0.1% p-nitrophenyl α-D glucopyranoside 0.5% azocasein
Incubation time: 60 min, temperature: 37 °C, centrifuge after assay and retain the supernatant absorbance at 410
0.2 M Tris-HCl and pH 7.6 (2 mL), 1 mL substrate, 1 mL activated sludge
Heating in boiling water for 3 min
Incubation time: 90 min, temperature: 37 °C, centrifuge after assay and retain mix, 2 mL supernatant in 2 mL, 2 M NaOH absorbance at 440
substrate 1 mL, 3 mL activated sludge
2 mL 10% TCA
Protease
2
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Fig. 1. Performance of semi-continuous fermentation. (A) Lactic acid concentration and optical activity; (B) Pyruvate and VFAs concentration. The batch reactor was conducted during Day 1 to Day 4, followed by semi-continuous operation from Day 5 to Day 20.
OA - L = ([L] − [D])/([L] + [D]) × 100%
3.1. Comparison of semi-continuous and repeated batch fermentation for LA and VFAs production
indicates both shift from LA to VFAs production and a trend towards chain elongation during semi-continuous fermentation. In this study, solubilization, hydrolysis, glycolysis and acidification were integrated in one system, resulting in the variations of VSS, SCOD, carbohydrate, protein, nutrients and several intermediates. LA is a fast metabolizing intermediate in mixed microbial consortia, which can be easily converted to pyruvate through NAD-independent lactate dehydrogenase (iLDH) (Gao et al., 2010). A transient accumulation of pyruvate (68.79 ± 48.10 mg/L) was observed when LA was accumulated before Day 11 (Fig. 1B). However, the pyruvate level was declining gradually to a low concentration (< 10 ± 3.07 mg/L) during the VFA production period (Fig. 1B). This indicates that pyruvate and lactate may quickly undergo the VFA generation pathway, particularly trending towards the chain elongation due to the reverse-β-oxidation and acrylate pathways (Kucek et al., 2016). VFAs could easily accumulate during semi-continuous co-fermentation of FW and WAS under the following conditions: HRT = 8.9 d, pH = 6.99 and T = 35 °C (Karthikeyan et al., 2016). Thus, semi-continuous fermentation of WAS and FW still remains a challenge for achieving a stable lactate yield.
3.1.1. Performance of the semi-continuous reactor As shown in Fig. 1A, LA, dominated by L-isomer (OA-L 59.3 ± 10.34%), slightly decreased from 31.7 ± 1.31 to 22.3 ± 1.84 g COD/L during semi-continuous fermentation from Day 5 to 11. But LA rapidly declined to 11.0 ± 1.68 g COD/L after Day 12 and its concentration was lower than the limit of detection (< 0.1 g COD/L) after Day 16. At the same time, a relatively low fraction of VFAs (3.8 ± 1.08 g COD/L) enriched by acetic acid (> 98%) coincided with the stable LA production till Day 11 (Fig. 1B). Subsequently, the VFA concentration increased to 17.2 ± 2.09 g COD/L and the main components were acetate (36.9 ± 7.74%), propionate (31.7 ± 10.93%) and butyrate (27.1 ± 17.14%). This explicitly
3.1.2. Performance of the repeated batch reactor As shown in Fig. 2A, during each cycle of the repeated batch reactor, LA was first accumulating (during the first 2–3 d) and then was consumed rapidly. The fermentation time to obtain the maximum concentration of LA (28.7 ± 5.94 g/L) was shortened to 2 d after the 2nd cycle, accomopanying with a stable and high yield of LA (0.72 ± 0.15 g/g TCOD). Moreover, the LA productivity rate (0.53 ± 0.17 g/L•h) improved in comparison with the batch reactor (0.42 ± 0.03 g/L•h), indicating its advantage for time-saving and the high volume efficiency. On the other hand, L-lactate dominated in the products with a high OA (52.3 ± 11.98%). This may attract industry
where [L] and [D] indicated the concentration of L-LA and D-LA, respectively. 2.6. Statistical analysis The data for regression analysis (95% confidence interval) were selected when LA was accumulated. The exponential and linear regression curves have been compared. All the assays were performed in triplicate, and the results were expressed as the mean ± standard deviation. An analysis of variance (ANOVA) was used to test the significance of results and p < 0.05 was considered to be statistically significant. 3. Results and discussion
3
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Fig. 2. Performance of repeated batch reactor. (A) Lactic acid concentration and optical activity; (B) Pyruvate and VFAs concentration. The batch reactor was conducted during Day 1 to Day 4, followed by repeated batch operation (8 cycles) from Day 5 to Day 36.
and 3) acidification for LA production and VFAs production (Tamis et al., 2015; Xue et al., 2018). Substrates could only be assimilated by microorganism after particulate substrate being solubilized into the liquid phase. The stable reduction of VSS (34.0 ± 6.41%) and increase in SCOD (13.7 ± 3.76%) in each cycle guaranteed a sufficient amount of soluble organic compounds for further microbial utilization (Fig. 3A and B). Hydrolysis is the rate-limiting step during fermentation: soluble polysaccharide is hydrolyzed to disaccharide and monosaccharide by αglucosidase, and free ammonia is released during protein hydrolysis (Li et al., 2017; Liu and Chen, 2018). During the repeated batch fermentation, there were non-significant trends towards an increased relative activity of α-glucosidase and protease compared to the batch fermentation (Fig. 3C). It should be noted that carbohydrates, the dominant component in FW (Kim et al., 2015), declined rapidly from 17.5 ± 0.53 to 0.59 ± 0.20 g COD/L after 1 day (Fig. 3B), indicating a fast glycolysis step during the repeated batch fermentation. Protein fluctuated at a lower level (1.09 ± 0.80 g COD/L), which implies that protein is a minor substrate obtained from a mixture of FW and WAS (Liu and Chen, 2018). The behaviour of pyruvate is similar to LA, i.e. the initial increase followed by a fast decline (Fig. 2B). Depletion of NH4+-N was observed at the early stage, followed by a sharp increase in the ammonia concentration at the end in each cycle. On the other hand, SOP was continously decreasing during fermentation in each cycle. Therefore, it is necessary to recognize a correlation between the nutrient variation and LA production in this study.
interest as the precursor for poly-L-lactic acid (PLLA), which is a promising biodegradable plastic material for a wide use (Klotz et al., 2016). LA can be easily converted to pyruvate, which can further undergo the reverse-β-oxidation pathway (acetic acid generation) or acrylate pathway (propionic acid generation) (Kucek et al., 2016). These patways may be responsible for VFAs accumulation (14.2 ± 5.31 g COD/ L) and LA consumption after 3 d in each cycle. Especially, acetate (53.5 ± 10.93%) and propionate (38.2 ± 14.44%) were enriched after the 2nd cycle. During the repeated batch fermentation test, 80% of the initial fermentative mixture was replaced by fresh substrates every 4 d, indicating that part (20%) of the mixed microbial consortium from a previous batch was inoculated to the next batch. Thus, the LA behaviour was similar to the batch results (Fig. 2A). The semi-continuous fermentation strategy only replaced a small fraction (20%) of the original fermentative mixture by fresh substrates every day. This resulted in a lower dilution rate and thus a higher concentration of lactate remaining for VFAs producers. Therefore, the repeated batch fermentation mode is appropriate for stabilizing lactate in the long-term operation. For downstream recovery of LA from the fermentation liquid, several methods have bee suggested in the literature, including precipitation, reactive extraction and separation by emulsion liquid membrane (ELM) (Kumar et al., 2019). This study was meant to provide a suitable approach for LA accumulation from organic waste during the long-term operation. 3.2. Fermentative pathway for stabilizing lactate production during the repeated batch fermentation
3.3. Correlation analysis between LA production and the crucial intermediates and nutrients
The fermentative pathways associated with lactate production include: 1) solubilization of particulate substrates to soluble compounds, 2) hydrolysis of soluble compounds to readily biodegradable monomers
The correlation analysis is used to quantify a relationship between two continuous variables, which is a useful tool to understand the 4
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Fig. 3. Solubilization, hydrolysis, glycolysis, and acidification during repeated batch fermentation. (A) Variation of TSS, VSS and VSS/TSS ratio; (B) Variation of SCOD, carbohydrates and protein, NH4+-N and SOP; (C) Variation of relative activity of protease and α-glucosidase.
pyruvate level was correlated (Fig. 4B, k = 0.08, p < 0.05) with a higher lactate concentration. Although pyruvate is the direct electron acceptor responsible for lactate production, pyruvate can also undergo the alternative pathway with VFAs production (Kucek et al., 2016). This explains several dots in the high level of pyruvate associated with a relatively low lactate production. Interestingly, LA production increased with the depletion of NH4+N (Fig. 4C, k = −0.32, p < 0.05) and SOP (Fig. 4D, k = −0.178, p < 0.05), which normally occurred at the early stage in each cycle of
specific relationship between products and intermediates (Ma et al., 2013). In this study, correlations between LA and several important intermediates and nutrients, including carbohydrates (Fig. 4A), pyruvic acid (Fig. 4B), NH4+-N (Fig. 4C) and SOP (Fig. 4D) were analyzed during the repeated batch fermentation. Negative correlation (Fig. 4A, k = −1.19, p < 0.05) was observed between LA and carbohydrates, indicating the depletion of carbohydrates correlated with LA production. The behaviour of pyruvate was similar to lactate, i.e. the initial increase followed by a fast decline at end (Fig. 2B). Therefore, a higher 5
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Fig. 4. Regression analysis of the correlation between lactic acid production and (A) carbohydrates, (B) pyruvic acid, (C) NH4+-N and (D) SOP. (P-value < 0.05 from A to D).
the repeated batch test (Fig. 3C). The release of nutrients may implicitly be due to hydrolysis of organophosphorus compounds and protein (Chen et al., 2011). Since nitrogen and phosphorus are important and essential nutrients for microbial growth and reproduction (Ji et al., 2018; Liu et al., 2015), the depletion of nutrients might be attributed to the microbial anabolism. Ammonia and phosphorus may be overlooked bio-factors in the mixed culture fermentation of organic waste to simultaneously enhance resource recovery of valuable fermentation products and nutrient removal from waste streams.
Table 2 Summary of pyrosequencing data for the fermentation reactors of batch reactor, 4th cycle and 6th cycle. Samples
Chao 1a
Aceb
Shannonc
Simpsond
Batch reactor 4th Cycle 6th Cycle
500 517 527
497 517 540
4.72 5.41 4.59
0.88 0.94 0.91
Values in parentheses are 97% confidence intervals. a Chao1 richness estimator: the total number of OTUs estimated by infinite sampling. A higher number indicates higher richness. b ACE richness estimator: the total number of OTUs estimated by infinite sampling. A higher number represents higher richness. c Shannon diversity index: an index to characterize species diversity. A higher value represents more diversity. d Simpson richness estimator: the total number of OTUs estimated by infinite sampling. A higher number indicates higher diversity.
3.4. Microbial community shift and predictive functional profiles during the repeated batch fermentation Mixed culture fermentation of LA was largely determined by the microbial community structure (Ye et al., 2018). The indexes for αdiversity show both similar richness (Chao1 and Ace) and diversity (Shannon and Simpson) for the batch reactor, and the 4th and 6th cycle in the repeated batch reactor (Table 2). This could be attributed to the addition of fresh WAS in each cycle which sustained the richness and diversity. The community distribution at phylum and genus levels from the batch and repeated batch reactors is illustrated in Fig. 5. At the phylum level, the community were mainly affiliated to Firmicutes, Actinobacteria, Bacteroidetes and Proteobacteria, which have been commonly observed in activated sludge (Ye et al., 2018). Firmicutes dominated in all the reactors, accounting for 87.8%, 78.3% and 83.8%, repectively, from the batch reactor, the 4th cycle and the 6th cycle of the repeated batch reactor. These values are similar to previous LA fermentation
systems (Li et al., 2018; Ye et al., 2018; Zhang et al., 2017). At the genus level, the predominant phylotypes (the column in purple) included Alkaliphilus, Dysgonomonas, Enterococcus and Bifidobacterium, which were considered as LA bacteria (Fisher et al., 2008; Hofstad et al., 2000; Lin et al., 2019; Vicosa et al., 2019). The relative abundance of the LA bacteria were higher in the repeated batch reactor (sum of 42.66% in 4th and 46.24% in 6th cycle) compared to the batch reactor (sum of 26.23%). Interestingly, Streptococcus, considered as the LA producers (Sert et al., 2017), dominated in the batch reactor (25.3%) but disappeared in the repeated batch reactor (Fig. 5). Its 6
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Fig. 5. Microbial community shift in phylum (pie chart) and genus (column, abundance > 0.1%) level from batch reactor, 4th Cycle and 6th Cycle. The purple columns represent the genera that are most likely to produce lactic acid. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Methodology. Xia Gu: Writing - original draft. Zhichao Guo: Investigation. Jian Song: Investigation. Daan Zhu: Validation. Yanbiao Liu: Conceptualization, Supervision. Yanan Liu: Conceptualization, Methodology. Gang Xue: Conceptualization, Project administration. Xiang Li: Conceptualization, Supervision, Funding acquisition, Resources. Jacek Makinia: Writing - review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by Natural Science Foundation (NSF) of China (51878137, 51878136); the Fundamental Research Funds for the Central Universities and the Donghua University Distinguished Young Professor Program; Shanghai Chen-Guang Program (17CG34); and the Natural Science Foundation of Shanghai (No. 18ZR1401000). Fig. 6. Relative abundance of partial KEGG pathways: predictive functional profiles of L- and D-lactate dehydrogenase (cytochrome).
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biortech.2019.122709.
presence could explain a lower LA production rate in the beginning stage from the 1st to the 4th cycle in the repeated batch reactor (Fig. 2A). However, Alkaliphilus, capable of producing lactate under alkaline conditions (Fisher et al., 2008), played a crucial role in LA fermentation in the repeated batch reactor. The relative abundance of Alkaliphilus in the 6th cycle increased 2.7 times compared to the 4th cycle (Fig. 5), thus resulting in a 1.8-fold increase (6th versus 4th cycle) in LA production (Fig. 2A). Clostridium competes substrates against LA producers for VFAs production (Lan et al., 2018). A similar relative abundance of Clostridium was observed in the 4th cycle (15.2%) and the 6th cycle (13.1%). However, the relative abundance of lactate dehydrogenase (cytochrome), responsible for conversion of lactate to pyruvate, increased significantly (Fig. 6). Pyruvate could be further converted to VFAs during fermentation. The maximum concentration of VFAs in the 6th cycle was 1.6 times higher than in the 4th cycle (Fig. 2B). In addition, a further study would be required to verify the role of Tyzzerella (11.8% in the 4th cycle and 21.0% in the 6th cycle) during repeated batch fermentation.
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4. Conclusion This study addressed the challenge for stabilizing LA production from a mixture of FW and WAS during long-term fermentation. This work demonstrated that the high LA yield of 0.72 g/g TCOD, combined with the high LA productivity of 0.53 g/L•h, could be achieved via the repeated batch fermentation. This was accompanied by the stable substrate solubilization and hydrolysis. The depletion of ammonia and phosphorus coincided with LA accumulation. Moreover, the relatively abundance of key LA bacteria in the repeated batch reactor was stabilized (42.7–46.2%) and increased when compared to the batch reactor. CRediT authorship contribution statement Xianbao Xu: Conceptualization, Investigation, Methodology, Writing - review & editing. Wenjuan Zhang: Investigation, 8
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propagation of antibiotic resistance genes during sludge anaerobic digestion: possible role of stimulated signal transduction. Environ. Sci.-Nano 6 (2), 528–539. Huang, J., Zhou, R., Chen, J., Han, W., Chen, Y., Wen, Y., Tang, J., 2016. Volatile fatty acids produced by co-fermentation of waste activated sludge and henna plant biomass. Bioresour. Technol. 211, 80–86. Huang, X., Zhao, J., Xu, Q., Li, X., Wang, D., Yang, Q., Liu, Y., Tao, Z., 2019b. Enhanced volatile fatty acids production from waste activated sludge anaerobic fermentation by adding tofu residue. Bioresour. Technol. 274, 430–438. Ji, J., Peng, Y., Mai, W., He, J., Wang, B., Li, X., Zhang, Q., 2018. Achieving advanced nitrogen removal from low C/N wastewater by combining endogenous partial denitrification with anammox in mainstream treatment. Bioresour. Technol. 270, 570–579. Karthikeyan, O.P., Selvam, A., Wong, J.W.C., 2016. Hydrolysis-acidogenesis of food waste in solid-liquid-separating continuous stirred tank reactor (SLS-CSTR) for volatile organic acid production. Bioresour. Technol. 200, 366–373. Kim, M., Chowdhury, M.M.I., Nakhla, G., Keleman, M., 2015. Characterization of typical household food wastes from disposers: fractionation of constituents and implications for resource recovery at wastewater treatment. Bioresour. Technol. 183, 61–69. Klotz, S., Kaufmann, N., Kuenz, A., Pruesse, U., 2016. Biotechnological production of enantiomerically pure d-lactic acid. Appl. Microbiol. Biotechnol. 100 (22), 9423–9437. Kucek, L.A., Nguyen, M., Angenent, L.T., 2016. Conversion of L-lactate into n-caproate by a continuously fed reactor microbiome. Water Res. 93, 163–171. Kumar, A., Thakur, A., Panesar, P.S., 2019. Lactic acid and its separation and purification techniques: a review. Rev. Environ. Sci. Bio-Technol. 18 (4), 823–853. Lan, Q., Gan, X., Tang, H., Luo, L., Chen, J., Yang, G., Huang, J., 2018. Genome sequence and annotation of Sporolactobacillus pectinivorans GD201205 (T), a lactic acidproducing bacterium. Ann. Microbiol. 68 (3), 159–162. Langille, M.G.I., Zaneveld, J., Caporaso, J.G., McDonald, D., Knights, D., Reyes, J.A., Clemente, J.C., Burkepile, D.E., Thurber, R.L.V., Knight, R., Beiko, R.G., Huttenhower, C., 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31 (9), 814-+. Li, J., Zhang, W., Li, X., Ye, T., Gan, Y., Zhang, A., Chen, H., Xue, G., Liu, Y., 2018. Production of lactic acid from thermal pretreated food waste through the fermentation of waste activated sludge: effects of substrate and thermal pretreatment temperature. Bioresour. Technol. 247, 890–896. Li, X., Chen, Y., Zhao, S., Chen, H., Zheng, X., Luo, J., Liu, Y., 2015. Efficient production of optically pure L-lactic acid from food waste at ambient temperature by regulating key enzyme activity. Water Res. 70, 148–157. Li, X., Zhang, W., Xue, S., Lai, S., Li, J., Chen, H., Liu, Z., Xue, G., 2017. Enrichment of Dlactic acid from organic wastes catalyzed by zero-valent iron: an approach for sustainable lactate isomerization. Green Chem. 19 (4), 928–936. Li, Y., Jin, Y., Borrion, A., Li, H., 2019. Current status of food waste generation and management in China. Bioresour. Technol. 273, 654–665. Lin, T.-C., Chen, B.-Y., Chen, C.-Y., Chen, Y.-S., Wu, H., 2019. Comparative analysis of spray-drying microencapsulation of Bifidobacterium adolescentis and Lactobacillus acidophilus cultivated in different growth media. J. Food Process Eng. 42 (7). Liu, H., Chen, Y., 2018. Enhanced methane production from food waste using cysteine to increase biotransformation of L-monosaccharide, volatile fatty acids, and biohydrogen. Environ. Sci. Technol. 52 (6), 3777–3785. Liu, Q., Chen, W., Zhang, X., Yu, L., Zhou, J., Xu, Y., Qian, G., 2015. Phosphate enhancing fermentative hydrogen production from substrate with municipal solid waste composting leachate as a nutrient. Bioresour. Technol. 190, 431–437. Ma, D., Gao, B., Sun, S., Wang, Y., Yue, Q., Li, Q., 2013. Effects of dissolved organic matter size fractions on trihalomethanes formation in MBR effluents during chlorine
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Stabilizing lactate production through repeated batch fermentation of food waste and waste activated sludge
T
Xianbao Xua, Wenjuan Zhanga, Xia Gua, Zhichao Guoa, Jian Songa, Daan Zhua, Yanbiao Liua, ⁎ Yanan Liua, Gang Xuea,b, Xiang Lia, , Jacek Makiniac a
College of Environmental Science and Engineering, Donghua University, 2999 North Renmin Road, Shanghai 201620, China Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China c Faculty of Civil and Environmental Engineering, Gdansk University of Technology, ul. Narutowicza 11/12, 80-233 Gdansk, Poland b
A R T I C LE I N FO
A B S T R A C T
Keywords: Lactic acid Co-fermentation Waste activated sludge Food waste
Bio-valorization of organic waste streams, such as food waste and waste activated sludge, to lactic acid (LA) has recently drawn much attention. It offers an opportunity for resource recovery, alleviates environmental issues and potentially turns a profit. In this study, both stable and high LA yield (0.72 ± 0.15 g/g total chemical oxygen demand) and productivity rate (0.53 g/L•h) were obtained through repeated batch fermentation. Moreover, stable solubilization and increase in the critical hydrolase activities were achieved. Depletions of ammonia and phosphorus were correlated with the LA production. The relative abundance of the key LA bacteria genera (i.e., Alkaliphilus, Dysgonomonas, Enterococcus and Bifidobacterium) stabilized in the repeated batch reactor at a higher level (44.5 ± 2.53%) in comparison with the batch reactor (26.2 ± 4.74%). This work show a practical way for the sustainable valorization of organic wastes to LA by applying the repeated batch mode during biological treatment.
1. Introduction Huge amounts of food waste (FW) may result in public and environmental health concerns when treated inappropriately (Gunasekera, 2015; Li et al., 2019). Valorization of FW, rich in carbon and nutrients, has received a particular interest in recent years (Dahiya et al., 2018; Xu et al., 2018). Lactic acid (LA) is one of the high valueadded platform molecules with an increasing global demand in industry (Tang et al., 2016). Particularly, biological nutrient removal processes can be significantly enhanced using lactate-enriched organic acids derived from FW (Tang et al., 2019). Normally, mixed culture fermentation is considered a sustainable and cost-effective method for LA recovery from FW. However, the short-term batch co-fermentation of FW and waste activated sludge (WAS) from municipal wastewater treatment plants (WWTPs), has recently shown a synergistic effect on enhancement of both hydrolysis and lactate acidification (Fitamo et al., 2016; Li et al., 2015; Xue et al., 2018; Zhang et al., 2017). Considering the application and commercialization for FW treatment, the stable and long-term continuous fermentation of LA is also imperatively required. However, LA is a fast metabolizing intermediate that can be easily converted to volatile fatty acids (VFAs) (Asunis et al., 2019; Huang et al., 2016, ⁎
2019b; Tang et al., 2017). Although lactate fermentation activities could be temporally enhanced by adding WAS during the short-term batch fermentation, addition of fresh WAS inevitably provides active microorganisms both consuming lactate and producing VFAs. Thus, the composition of the mixed microbial community may shift towards competing VFAs producers (Tamis et al., 2015; Zhang et al., 2017), resulting in a challenge of separating lactate acidogenesis from VFAs generation. Till now, the long-term performance of lactate recovery from a mixture of FW and WAS has remained largely unknown. Continuous culture is usually used in large scale application (Cho et al., 2019) and provides a relatively stable and chemostat cultivation for mixed microbiome (Dolejs et al., 2014; Seo et al., 2017; Urbance et al., 2004). It has been reported that the hydraulic retention time (HRT) of 10 d was required for lactate production during semi-continuous fermentation using unsterilized artificial garbage under thermophilic conditions (55 °C) (Akao et al., 2007). Alternatively, repeated batch operation was also applied for improving LA productivity by inoculating pure strain (Abdel-Rahman et al., 2013; Ma et al., 2014; Reddy et al., 2016; Zhang et al., 2014). However, no any reference is available regarding preferable long-term reactor for LA production by co-fermentation of FW and WAS. To develop a stable and reliable approach for lactate production,
Corresponding author. E-mail address: [email protected] (X. Li).
https://doi.org/10.1016/j.biortech.2019.122709 Received 31 October 2019; Received in revised form 25 December 2019; Accepted 27 December 2019 Available online 28 December 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.
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semi-continuous and repeated batch fermentation were first compared in this study. Then, the mechanism of high rate LA production was elaborated through unveiling the fermentatition pathways involved the solubilization, hydrolysis and acidification. The correlations between LA production and several crucial nutrients and intermediates were analyzed. The stability of the microbial community shift and predictive functional profiles were finally evaluated. This paper provides a stable and practical approach for sustainable valorization of organic wastes to LA during biological treatment.
protein and carbohydrates were determined. To evaluate the efficiency of FW solubilization, the increase in soluble substrate (SCOD) and reduction of VSS were recorded in each cycle of the repeated batch reactor. The activities of hydrolase (i.e., α-glucosidase and protease) were evaluated after two days in each cycle (Zhang et al., 2017). Correlation analyses (95% confidence interval) between the LA production and SCOD, carbohydrate, protein, NH4+-N, SOP and pyruvic acid were carried out. 2.3. Microbial community and predictive functional profiles
2. Materials and methods
Illumina Miseq pyrosequencing method was used to evaluate the microbial community structure. Samples were withdrawn from the reactor respectively on the 3rd day from the batch reactor, and the 3rd day during the 4th cycle (lowest LA production) and the 6th cycle (highest LA production) from the repeated batch reactor. Genomic DNA was extracted by TIANamp Soil DNA Kit (TIANGEN) from each of the triplicate samples (the batch reactor, the 4th and 6th cycle of the repeated batch reactor) in the corresponding sets, and pooled together to minimize the potential variation (Huang et al., 2019a). The extracted DNA was amplified by PCR using the primer 338F (5′-ACTCCTACGG GAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) in V3-V4 region. The closest matching sequences were compared with the reference sequences in the GenBank database of BLAST (http://blast. ncbi.nlm.nih.gov). Raw sequences were deposited in the NCBI Short Read Archive (Dahiya et al., 2018) database under the accession number SRR7778920, SRR7778919 and SRR7778924. Functional capabilities of the microbial community were analyzed according to PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) using 16S rRNA marker gene sequences (Langille et al., 2013). The hierarchical database adopted in the analysis is provided at three levels which can be collected from the KEGG PATHWAY Database.
2.1. WAS and FW The WAS used in this study was collected from a municipal WWTP in Shanghai, China and the sludge was settled at 4 °C for 24 h to decant the supernatant before use. The main characteristics of concentrated WAS were as follows: pH 6.9 ± 0.2, total suspended solids (TSS) 16.6 ± 1.45 g/L, volatile suspended solids (VSS) 10.3 ± 0.64 g/L, total chemical oxygen demand (TCOD) 14.6 ± 1.29 g/L. FW, collected from the campus canteen in Donghua University, was milled into a slurry state and diluted with tap water to make the characteristics as follows: TSS 239 ± 2.36 g/L, VSS 238 ± 1.93 g/L, TCOD 181 ± 2.89 g/L. The treated WAS and FW were stored at 4 °C in a refrigerator prior to use. 2.2. Operation of the batch reactor, semi-continuous reactor and repeated batch reactor Co-substrates were first prepared by mixing FW and WAS at the VSS ratios of 6:1 (Li et al., 2015) and diluted with tap water to make the final TCOD around 40 ± 1 g/L, similar to previous study (Li et al., 2017). A batch reactor (working volume of 1 L), containing 1 L of cosubstrates, was mechanically stirred (120 rpm) and operated under the optimal conditions for 4 days as proposed by (Zhang et al., 2017). The process temperature was controlled at 35 ± 1 °C in water bath and pH was adjusted to 9.0 ± 0.1 using 10 M sodium hydroxide solution or 3 M hydrochloric acid solution every 6 h. To start semi-continuous operation, 750 mL of the fermentation mixture was withdrawn from the batch reactor and mixed with 250 mL of the fresh co-substrates in a semi-continuous reactor (working volume of 1 L). Every day, 250 mL of the fermentation mixture was replaced by the same volume of fresh cosubstrates. Then, the semi-continuous reactor was operated for 20 d under the same conditions as the batch reactor. On the other hand, 800 mL of the fresh co-substrates and 200 mL of the fermentation mixture from the batch reactor were mixed in the repeated batch reactor (working volume 1 L). Then, 800 mL of the fermentation broth was withdrawn from the repeated batch reactor (residual 200 mL was retained) every 4 days, which was the optimal duration in previous study (Zhang et al., 2017), and replaced by 800 mL of the fresh cosubstrates. The repeated batch reactor was operated at eight cycles for 32 days under the same conditions as the batch reactor. The withdrawn samples from all the reactors were used for chemical analyses. The concentrations of levorotatory lactic acid (L-LA), dextrorotatory lactic acid (D-LA), VFAs, NH4+-N, soluble orthophosphate (SOP), pyruvate,
2.4. Analytical methods Samples withdrawn from each reactor were filtered by membrane filters with a pore size of 0.45 μm for further chemical analyses. LA with L- and D-isomer were measured by a high-performance liquid chromatograph (Thermol Ultimate 3000, USA), which was equipped with Astec CLC-D (5 mm, 15 cm × 4.6 mm) column and detected at the wavelength of 254 UV. The mobile phase was copper sulfate solution (5 mM) with a flow rate of 1.0 mL/min. The VFAs were detected by Agilent GC 7820 with flame ionization detector and equipped with a DB-WAX column (30 m × 1.0 mm × 0.53 mm). The methods for TSS, VSS, SCOD, carbohydrate, protein, NH4+-N, and SOP were referred to previous studies (Li et al., 2015; Zhang et al., 2017). The method for αglucosidase and protease was listed in Table 1 according to (Goel et al., 1998). 2.5. Calculations The optical activity (OA) of L-LA (OA-L) was calculated according to the following equation:
Table 1 Method for α-glucosidase and protease measurement (Goel et al., 1998). Enzyme assay
Substrate
Incubation conditions
Mixture composition
Termination
α-glucosidase
0.1% p-nitrophenyl α-D glucopyranoside 0.5% azocasein
Incubation time: 60 min, temperature: 37 °C, centrifuge after assay and retain the supernatant absorbance at 410
0.2 M Tris-HCl and pH 7.6 (2 mL), 1 mL substrate, 1 mL activated sludge
Heating in boiling water for 3 min
Incubation time: 90 min, temperature: 37 °C, centrifuge after assay and retain mix, 2 mL supernatant in 2 mL, 2 M NaOH absorbance at 440
substrate 1 mL, 3 mL activated sludge
2 mL 10% TCA
Protease
2
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Fig. 1. Performance of semi-continuous fermentation. (A) Lactic acid concentration and optical activity; (B) Pyruvate and VFAs concentration. The batch reactor was conducted during Day 1 to Day 4, followed by semi-continuous operation from Day 5 to Day 20.
OA - L = ([L] − [D])/([L] + [D]) × 100%
3.1. Comparison of semi-continuous and repeated batch fermentation for LA and VFAs production
indicates both shift from LA to VFAs production and a trend towards chain elongation during semi-continuous fermentation. In this study, solubilization, hydrolysis, glycolysis and acidification were integrated in one system, resulting in the variations of VSS, SCOD, carbohydrate, protein, nutrients and several intermediates. LA is a fast metabolizing intermediate in mixed microbial consortia, which can be easily converted to pyruvate through NAD-independent lactate dehydrogenase (iLDH) (Gao et al., 2010). A transient accumulation of pyruvate (68.79 ± 48.10 mg/L) was observed when LA was accumulated before Day 11 (Fig. 1B). However, the pyruvate level was declining gradually to a low concentration (< 10 ± 3.07 mg/L) during the VFA production period (Fig. 1B). This indicates that pyruvate and lactate may quickly undergo the VFA generation pathway, particularly trending towards the chain elongation due to the reverse-β-oxidation and acrylate pathways (Kucek et al., 2016). VFAs could easily accumulate during semi-continuous co-fermentation of FW and WAS under the following conditions: HRT = 8.9 d, pH = 6.99 and T = 35 °C (Karthikeyan et al., 2016). Thus, semi-continuous fermentation of WAS and FW still remains a challenge for achieving a stable lactate yield.
3.1.1. Performance of the semi-continuous reactor As shown in Fig. 1A, LA, dominated by L-isomer (OA-L 59.3 ± 10.34%), slightly decreased from 31.7 ± 1.31 to 22.3 ± 1.84 g COD/L during semi-continuous fermentation from Day 5 to 11. But LA rapidly declined to 11.0 ± 1.68 g COD/L after Day 12 and its concentration was lower than the limit of detection (< 0.1 g COD/L) after Day 16. At the same time, a relatively low fraction of VFAs (3.8 ± 1.08 g COD/L) enriched by acetic acid (> 98%) coincided with the stable LA production till Day 11 (Fig. 1B). Subsequently, the VFA concentration increased to 17.2 ± 2.09 g COD/L and the main components were acetate (36.9 ± 7.74%), propionate (31.7 ± 10.93%) and butyrate (27.1 ± 17.14%). This explicitly
3.1.2. Performance of the repeated batch reactor As shown in Fig. 2A, during each cycle of the repeated batch reactor, LA was first accumulating (during the first 2–3 d) and then was consumed rapidly. The fermentation time to obtain the maximum concentration of LA (28.7 ± 5.94 g/L) was shortened to 2 d after the 2nd cycle, accomopanying with a stable and high yield of LA (0.72 ± 0.15 g/g TCOD). Moreover, the LA productivity rate (0.53 ± 0.17 g/L•h) improved in comparison with the batch reactor (0.42 ± 0.03 g/L•h), indicating its advantage for time-saving and the high volume efficiency. On the other hand, L-lactate dominated in the products with a high OA (52.3 ± 11.98%). This may attract industry
where [L] and [D] indicated the concentration of L-LA and D-LA, respectively. 2.6. Statistical analysis The data for regression analysis (95% confidence interval) were selected when LA was accumulated. The exponential and linear regression curves have been compared. All the assays were performed in triplicate, and the results were expressed as the mean ± standard deviation. An analysis of variance (ANOVA) was used to test the significance of results and p < 0.05 was considered to be statistically significant. 3. Results and discussion
3
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Fig. 2. Performance of repeated batch reactor. (A) Lactic acid concentration and optical activity; (B) Pyruvate and VFAs concentration. The batch reactor was conducted during Day 1 to Day 4, followed by repeated batch operation (8 cycles) from Day 5 to Day 36.
and 3) acidification for LA production and VFAs production (Tamis et al., 2015; Xue et al., 2018). Substrates could only be assimilated by microorganism after particulate substrate being solubilized into the liquid phase. The stable reduction of VSS (34.0 ± 6.41%) and increase in SCOD (13.7 ± 3.76%) in each cycle guaranteed a sufficient amount of soluble organic compounds for further microbial utilization (Fig. 3A and B). Hydrolysis is the rate-limiting step during fermentation: soluble polysaccharide is hydrolyzed to disaccharide and monosaccharide by αglucosidase, and free ammonia is released during protein hydrolysis (Li et al., 2017; Liu and Chen, 2018). During the repeated batch fermentation, there were non-significant trends towards an increased relative activity of α-glucosidase and protease compared to the batch fermentation (Fig. 3C). It should be noted that carbohydrates, the dominant component in FW (Kim et al., 2015), declined rapidly from 17.5 ± 0.53 to 0.59 ± 0.20 g COD/L after 1 day (Fig. 3B), indicating a fast glycolysis step during the repeated batch fermentation. Protein fluctuated at a lower level (1.09 ± 0.80 g COD/L), which implies that protein is a minor substrate obtained from a mixture of FW and WAS (Liu and Chen, 2018). The behaviour of pyruvate is similar to LA, i.e. the initial increase followed by a fast decline (Fig. 2B). Depletion of NH4+-N was observed at the early stage, followed by a sharp increase in the ammonia concentration at the end in each cycle. On the other hand, SOP was continously decreasing during fermentation in each cycle. Therefore, it is necessary to recognize a correlation between the nutrient variation and LA production in this study.
interest as the precursor for poly-L-lactic acid (PLLA), which is a promising biodegradable plastic material for a wide use (Klotz et al., 2016). LA can be easily converted to pyruvate, which can further undergo the reverse-β-oxidation pathway (acetic acid generation) or acrylate pathway (propionic acid generation) (Kucek et al., 2016). These patways may be responsible for VFAs accumulation (14.2 ± 5.31 g COD/ L) and LA consumption after 3 d in each cycle. Especially, acetate (53.5 ± 10.93%) and propionate (38.2 ± 14.44%) were enriched after the 2nd cycle. During the repeated batch fermentation test, 80% of the initial fermentative mixture was replaced by fresh substrates every 4 d, indicating that part (20%) of the mixed microbial consortium from a previous batch was inoculated to the next batch. Thus, the LA behaviour was similar to the batch results (Fig. 2A). The semi-continuous fermentation strategy only replaced a small fraction (20%) of the original fermentative mixture by fresh substrates every day. This resulted in a lower dilution rate and thus a higher concentration of lactate remaining for VFAs producers. Therefore, the repeated batch fermentation mode is appropriate for stabilizing lactate in the long-term operation. For downstream recovery of LA from the fermentation liquid, several methods have bee suggested in the literature, including precipitation, reactive extraction and separation by emulsion liquid membrane (ELM) (Kumar et al., 2019). This study was meant to provide a suitable approach for LA accumulation from organic waste during the long-term operation. 3.2. Fermentative pathway for stabilizing lactate production during the repeated batch fermentation
3.3. Correlation analysis between LA production and the crucial intermediates and nutrients
The fermentative pathways associated with lactate production include: 1) solubilization of particulate substrates to soluble compounds, 2) hydrolysis of soluble compounds to readily biodegradable monomers
The correlation analysis is used to quantify a relationship between two continuous variables, which is a useful tool to understand the 4
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Fig. 3. Solubilization, hydrolysis, glycolysis, and acidification during repeated batch fermentation. (A) Variation of TSS, VSS and VSS/TSS ratio; (B) Variation of SCOD, carbohydrates and protein, NH4+-N and SOP; (C) Variation of relative activity of protease and α-glucosidase.
pyruvate level was correlated (Fig. 4B, k = 0.08, p < 0.05) with a higher lactate concentration. Although pyruvate is the direct electron acceptor responsible for lactate production, pyruvate can also undergo the alternative pathway with VFAs production (Kucek et al., 2016). This explains several dots in the high level of pyruvate associated with a relatively low lactate production. Interestingly, LA production increased with the depletion of NH4+N (Fig. 4C, k = −0.32, p < 0.05) and SOP (Fig. 4D, k = −0.178, p < 0.05), which normally occurred at the early stage in each cycle of
specific relationship between products and intermediates (Ma et al., 2013). In this study, correlations between LA and several important intermediates and nutrients, including carbohydrates (Fig. 4A), pyruvic acid (Fig. 4B), NH4+-N (Fig. 4C) and SOP (Fig. 4D) were analyzed during the repeated batch fermentation. Negative correlation (Fig. 4A, k = −1.19, p < 0.05) was observed between LA and carbohydrates, indicating the depletion of carbohydrates correlated with LA production. The behaviour of pyruvate was similar to lactate, i.e. the initial increase followed by a fast decline at end (Fig. 2B). Therefore, a higher 5
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Fig. 4. Regression analysis of the correlation between lactic acid production and (A) carbohydrates, (B) pyruvic acid, (C) NH4+-N and (D) SOP. (P-value < 0.05 from A to D).
the repeated batch test (Fig. 3C). The release of nutrients may implicitly be due to hydrolysis of organophosphorus compounds and protein (Chen et al., 2011). Since nitrogen and phosphorus are important and essential nutrients for microbial growth and reproduction (Ji et al., 2018; Liu et al., 2015), the depletion of nutrients might be attributed to the microbial anabolism. Ammonia and phosphorus may be overlooked bio-factors in the mixed culture fermentation of organic waste to simultaneously enhance resource recovery of valuable fermentation products and nutrient removal from waste streams.
Table 2 Summary of pyrosequencing data for the fermentation reactors of batch reactor, 4th cycle and 6th cycle. Samples
Chao 1a
Aceb
Shannonc
Simpsond
Batch reactor 4th Cycle 6th Cycle
500 517 527
497 517 540
4.72 5.41 4.59
0.88 0.94 0.91
Values in parentheses are 97% confidence intervals. a Chao1 richness estimator: the total number of OTUs estimated by infinite sampling. A higher number indicates higher richness. b ACE richness estimator: the total number of OTUs estimated by infinite sampling. A higher number represents higher richness. c Shannon diversity index: an index to characterize species diversity. A higher value represents more diversity. d Simpson richness estimator: the total number of OTUs estimated by infinite sampling. A higher number indicates higher diversity.
3.4. Microbial community shift and predictive functional profiles during the repeated batch fermentation Mixed culture fermentation of LA was largely determined by the microbial community structure (Ye et al., 2018). The indexes for αdiversity show both similar richness (Chao1 and Ace) and diversity (Shannon and Simpson) for the batch reactor, and the 4th and 6th cycle in the repeated batch reactor (Table 2). This could be attributed to the addition of fresh WAS in each cycle which sustained the richness and diversity. The community distribution at phylum and genus levels from the batch and repeated batch reactors is illustrated in Fig. 5. At the phylum level, the community were mainly affiliated to Firmicutes, Actinobacteria, Bacteroidetes and Proteobacteria, which have been commonly observed in activated sludge (Ye et al., 2018). Firmicutes dominated in all the reactors, accounting for 87.8%, 78.3% and 83.8%, repectively, from the batch reactor, the 4th cycle and the 6th cycle of the repeated batch reactor. These values are similar to previous LA fermentation
systems (Li et al., 2018; Ye et al., 2018; Zhang et al., 2017). At the genus level, the predominant phylotypes (the column in purple) included Alkaliphilus, Dysgonomonas, Enterococcus and Bifidobacterium, which were considered as LA bacteria (Fisher et al., 2008; Hofstad et al., 2000; Lin et al., 2019; Vicosa et al., 2019). The relative abundance of the LA bacteria were higher in the repeated batch reactor (sum of 42.66% in 4th and 46.24% in 6th cycle) compared to the batch reactor (sum of 26.23%). Interestingly, Streptococcus, considered as the LA producers (Sert et al., 2017), dominated in the batch reactor (25.3%) but disappeared in the repeated batch reactor (Fig. 5). Its 6
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Fig. 5. Microbial community shift in phylum (pie chart) and genus (column, abundance > 0.1%) level from batch reactor, 4th Cycle and 6th Cycle. The purple columns represent the genera that are most likely to produce lactic acid. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Methodology. Xia Gu: Writing - original draft. Zhichao Guo: Investigation. Jian Song: Investigation. Daan Zhu: Validation. Yanbiao Liu: Conceptualization, Supervision. Yanan Liu: Conceptualization, Methodology. Gang Xue: Conceptualization, Project administration. Xiang Li: Conceptualization, Supervision, Funding acquisition, Resources. Jacek Makinia: Writing - review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by Natural Science Foundation (NSF) of China (51878137, 51878136); the Fundamental Research Funds for the Central Universities and the Donghua University Distinguished Young Professor Program; Shanghai Chen-Guang Program (17CG34); and the Natural Science Foundation of Shanghai (No. 18ZR1401000). Fig. 6. Relative abundance of partial KEGG pathways: predictive functional profiles of L- and D-lactate dehydrogenase (cytochrome).
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biortech.2019.122709.
presence could explain a lower LA production rate in the beginning stage from the 1st to the 4th cycle in the repeated batch reactor (Fig. 2A). However, Alkaliphilus, capable of producing lactate under alkaline conditions (Fisher et al., 2008), played a crucial role in LA fermentation in the repeated batch reactor. The relative abundance of Alkaliphilus in the 6th cycle increased 2.7 times compared to the 4th cycle (Fig. 5), thus resulting in a 1.8-fold increase (6th versus 4th cycle) in LA production (Fig. 2A). Clostridium competes substrates against LA producers for VFAs production (Lan et al., 2018). A similar relative abundance of Clostridium was observed in the 4th cycle (15.2%) and the 6th cycle (13.1%). However, the relative abundance of lactate dehydrogenase (cytochrome), responsible for conversion of lactate to pyruvate, increased significantly (Fig. 6). Pyruvate could be further converted to VFAs during fermentation. The maximum concentration of VFAs in the 6th cycle was 1.6 times higher than in the 4th cycle (Fig. 2B). In addition, a further study would be required to verify the role of Tyzzerella (11.8% in the 4th cycle and 21.0% in the 6th cycle) during repeated batch fermentation.
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