Performance of a novel photobioreactor for nutrient removal from piggery biogas slurry: Operation parameters, microbial diversity and nutrient recovery potential

Bioresource Technology 272 (2019) 421–432

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Bioresource Technology journal homepage: www.elsevier.com/locate/biortech

Performance of a novel photobioreactor for nutrient removal from piggery biogas slurry: Operation parameters, microbial diversity and nutrient recovery potential Longzao Luoa,b, Xiaoai Linc, Fanjian Zengb, Shuang Luod, Zongbao Chena, Guangming Tianb,

T

⁎

a

School of Chemistry and Environmental Science, Shangrao Normal University, Shangrao 334001, China Department of Environmental Engineering, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China c College of Life Science, Shangrao Normal University, Shangrao 334001, China d College of Resources and Environment, Hunan Agricultural University, Changsha 410128, China b

G R A P H I C A L A B S T R A C T

A R T I C LE I N FO

A B S T R A C T

Keywords: Piggery biogas slurry Nutrient recovery Photobioreactors Operation parameters

Photobioreactor is deemed to be one of limiting factors for the commercial application of wastewater treatment based on microalgae cultivation. In this study, a novel Flat-Plate Continuous Open Photobioreactor (FPCO-PBR) was developed to treat piggery biogas slurry. The operation parameters, microbial stability and nutrient recovery potential of FPCO-PBR were investigated. Results showed that the appropriate influent mode for FPCO-PBR was multi-point or spraying mode. The optimal hydraulic retention time and interval time for biomass harvesting of FPCO-PBR were both 2 d. Nitrogen and phosphorus recovery rate were 30 mg L−1 d−1 and 7 mg L−1 d−1 respectively under optimal operating parameters. Microbial diversity remained relatively stable in FPCO-PBR. Biomass production rate of FPCO-PBR was 0.47 g L−1 d−1 under optimal operating parameters. The revenue generated from biomass was estimated to be 15.06 $ kg−1, which means that treating one ton of wastewater can generate $ 7.08 in revenue.

1. Introduction With the rapid development of pig industry in China, a large ⁎

number of piggery biogas slurry has been produced. Piggery biogas slurry is rich in nitrogen (N) and phosphorus (P), which will pose a serious threat to the surrounding environment, such as water

Corresponding author. E-mail address: [email protected] (G. Tian).

https://doi.org/10.1016/j.biortech.2018.10.057 Received 20 September 2018; Received in revised form 21 October 2018; Accepted 23 October 2018 Available online 26 October 2018 0960-8524/ © 2018 Elsevier Ltd. All rights reserved.

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Table 1 Average influent characteristics during the experimental period (mg L−1, except for pH). Parameter

Ammonium nitrogen (NH4-N)

Total phosphorus (TP)

Nitrates (NO3-N)

Nitrites (NO2-N)

Chemical oxygen demand COD

pH

Mean ± SD

765.18 ± 5.68

36.74 ± 3.14

32.32 ± 2.57

5.30 ± 0.87

588.83 ± 11.54

7.56 ± 0.71

its great potential for nutrient removal from piggery wastewater (Cheng et al., 2013; Luo et al., 2018a). This microalgae was isolated from a local piggery wastewater treatment pond in Hangzhou City, Zhejiang Province, China (Cheng et al., 2013). Microalgae were pre-cultured in BG11 medium until to logarithmic growth phase. Then microalgae cells were collected by filtration with a nylon sheet of 20 μm (Shanghai Xinya, China) mesh for inoculation. Commercially available bacterial inoculum known for degrading organic pollutant (Probiotic Company, Shanghai, China) were used as bio-augmentation for enhancing the process of microalgae-based wastewater treatment. The main bacteria strains in the bacterial inoculum are Bacillus and Pseudomonas. The inoculation concentrations for microalgae and bacterial inoculum were 0.1 g L−1 microalgae cell (dry weight) and 10 g L−1 bacteria agent (3 × 109 Colony-Forming Units g−1) respectively. The piggery biogas slurry used in this study was the effluent from an anaerobic digester treating piggery wastewater from a local pig farm in Hangzhou, China. The effluent was obtained and stored at 4 °C. Table 1 shows the main characteristics of influent to FPCO-PBR.

eutrophication, air pollution and soil degradation (Godos et al., 2010). The traditional treatment technology (such as activated sludge) can treat piggery biogas slurry (Lee and Han, 2015), but fail to recover nutrients from wastewater effectively. Therefore, how to recover these nutrients from piggery biogas slurry is an inevitable choice for the sustainable development of the pig industry. Some wetland plants can recycle nutrients in waste water effectively (Luo et al., 2018b), but wetlands occupy large area and easily cause secondary pollution. Microalgae has been found to show great potential in assimilating nutrients (such as N and P) from wastewater (Luo et al., 2019, 2018a). Microalgae have the competence to use water, sun-light and CO2 to synthesize biomass through photosynthesis (Zhu, 2015). There are diverse advantages treating wastewater by microalgae, such as high nutrient removal efficiencies, production of high-value products and mitigating CO2 released from wastewater treatment plant (Zhu et al., 2016). Thus, the nutrients in piggery biogas slurry can be recovered by microalgae. The combination of wastewater treatment and microalgae cultivation can both reduce the cost of wastewater treatment and microalgae cultivation (Zhu, 2015), thus is considered to be an effective and economical strategy for wastewater treatment and biomass production. However, the wastewater treatment based on microalgae assimilation is mainly applied in the wastewater with low nutrient concentration. Moreover, carbon (C) source demand and dissolved oxygen inhibition are other two frequently overlooked challenges for microalgae. In order to overcome these challenges, much efforts have been made, such as screening of strains with high nutrient removal ability (Hena et al., 2015), aeration with carbon dioxide on cultivation of microalgae (Mohsenpour and Willoughby, 2016), optimization of lighting strategies (Yan et al., 2016a, 2016b) and improvement of photobioreactor (PBR) (Liu et al., 2018). PBR is deemed to be one of the limiting factors for the commercial application of wastewater treatment based on microalgae cultivation. Different types of PBRs have been designed for microalgae cultivation recently and some of them have achieved commercial application (Gupta et al., 2015), but most of them were designed for microalgae pure cultivation, few were for wastewater treatment (Han et al., 2017). PBRs for microalgae pure cultivation do not always work well in wastewater treatment, as the composition of wastewater is complicated, this may lead to the different environment for microalgal growth compared with those grow in a pure cultivation medium. Therefore, PBRs for wastewater treatment need to be resigned and improved based on the existing PBRs that are used for microalgae pure cultivation. In order to treat piggery biogas slurry with high N and P contents economically and effectively, a novel flat-plate continuous open photobioreactor (FPCO-PBR) was developed. Compared with traditional PBR, no aerator was set up in FPCO-PBR. Moreover, the carbon source was provided by respiration of bacteria. So the cost of aeration and carbon source supply can be reduced. The operation parameters, microbial diversity and nutrient recovery potential of FPCO-PBR were investigated. The aims were to evaluate the feasibility of nutrient recovery from piggery biogas slurry by FPCO-PBR.

2.2. Lab-scale reactor Aeration with air or carbon dioxide was used for mixing or providing C source in traditional PBR. However, no aeration tool was designed in FPCO-PBR, for which oxygen and carbon dioxide sources were produced by the microalgae-bacteria system, and mixing was achieved by rational influent mode. Therefore, FPCO-PBR can save the operation cost. Lab-scale FPCO-PBR with dimensions of 40 (Length) cm × 10 cm (Width) × 4 cm (Height) were constructed out of plexiglass as shown in Fig. 1. The water depth in each reactor was maintained at 2.5 cm, and the working volume of the reactor was 1.0 L. Two fluorescent lamps were placed on the top of the reactor, with the maximum light intensity at the water level of reactor was 400 μmol photons m−2 s−1. The selfflocculating ability of Desmodesmus sp. CHX1 was observed in this study, so the biomass could be harvested from the bottom of the reactor. The biomass harvesting tank was located at the bottom of the reactor body and could be pulled out for harvesting when it was full of biomass. The effluent collection tank was set at the right of the reactor body, which was used for the precipitation of the remaining algae cell. 2.3. Influence of operation parameters Effects of hydraulic retention time (HRT), influent mode and time interval for biomass harvesting on nutrient RE were conducted through the following experiments (Table 2). HRT was set at 1 d, 2 d and 3 d respectively. Three influent modes were investigated in this study, they were multi-point mode (MM), sprinkling mode (SM) and underflow mode (UM). For MM, five holes (with a spacing of 4 cm for each hole and the height from hole to the reactor of 5 cm) were set uniformly in the inlet pipe, which were above the reactor. The influent fell evenly from the five holes into the reactor. For SM, five sprayers (with a spacing of 4 cm for each sprayer and the height from sprayer to the reactor of 5 cm) were set uniformly in the inlet pipe, which were above the reactor. The influent was sprayed into the reactor from the five sprayers. For UM, the inlet pipe was set on the bottom of the reactor body, and the influent flowed into the reactor in the form of underflow. The time interval for biomass harvesting was set at 2 d, 4 d and 6 d. FPCO-PBR was placed in a room with the temperature of 30 °C. The light intensity and light/dark cycle were 400 μmol photons m−2 s−1

2. Materials and methods 2.1. Microorganisms and wastewater The microalgae Desmodesmus sp. CHX1 was used in this study due to 422

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Fig. 1. Schematic diagram of FPCO-PBR (1) wastewater collection tank, (2) pump, (3) water inlet, (4) lamp, (5) effluent collection tank, (6) main part of FPCO-PBR, (7) biomass harvesting tank.

CA, U.S.) according to the manufacturer’s instructions and quantified using QuantiFluor™-ST (Promega, U.S.). Purified amplicons were pooled in equimolar and paired-end sequenced (2 × 250) on an Illumina MiSeq platform according to the standard protocols.

Table 2 Operation parameter experimental design. Parameter

Number

HRT (d)

Influent mode

Time interval for biomass harvesting (d)

HRT

A1 A2 A3

1 2 3

MM MM MM

No harvesting No harvesting No harvesting

Influent mode

B1 B2 B3

2 2 2

MM SM UM

No harvesting No harvesting No harvesting

Time interval for biomass harvesting

C1 C2 C3

2 2 2

MM MM MM

2 4 6

2.5. Analytical method Liquid samples were collected every day and used for dissolved oxygen (DO) and pH determination. Then they were filtered through a 0.45 μm pore-size acetate cellulose membrane (Shanghai Xinya, China) for analysis of other wastewater chemical property. DO was determined by an HQ30D oxygen-meter (HACH, USA). The pH was determined using a PHS-3B pH meter. NO2-N, NO3-N, NH4-N, total nitrogen (TN) and TP contents were determined according to the standard methods of American Public Health Association (Rice et al., 2012). Biomass concentration was estimated from the dry weight (DW) measurement; triplicate aliquots were filtered through pre-weighted cellulose acetate membrane filters (0.45 μm), rinsed with dH2O and dried overnight at 75 °C. Carbon (C) content was measured using a VARIO ELIII (Elementar, Germany) element analyzer. N and P contents in the microalgae or microalgae-bacteria mixture were determined according to Bao (2007). Protein was analyzed using bovine serum albumin as the standard according to Lowry et al. (1951). Carbohydrates were determined using a phenol–acid method using D-glucose as the standard. Before protein and carbohydrate assay, the samples were treated ultrasonically, as follows. 20 mg aliquots of freeze-dried biomass were suspended for 20 min in 10 mL of lysis buffer in a Falcon tube. Then, the ultrasound pretreatment (high intensity ultrasonic processor, 500 W model) was applied at an amplitude of 30% for 10 min and On/Off pulses of 30/10 to avoid overheating the samples. Chlorophyll was measured as follows: the microalgae solutions were first filtered with vacuum filtration (0.45 μm) and all reside was extracted with absolute alcohol (4 °C, 24 h), then centrifuged (1200 rpm, 5 min). Chlorophyll content in the pooled extract was spectrophotometrically measured at 645 nm and 663 nm. Carotenoid content was measured at 470 nm. Chlorophyll and carotenoid contents were calculated from the equation:

and 24 h/0 h, respectively. The influences of HRT and time interval for biomass harvesting were carried out in the form of normal continuous flow, the wastewater was discharged from the reactor after a period of time. The influence of influent mode was investigated in the form of batch circulation mode, with 2 L wastewater being pumped into the reactor by the peristaltic pump, and the effluent was used as influent and pumped into the reactor again until most of the N and P in the effluent were removed. The influent rates of the three kinds of influent modes were all 5.54 L h−1. 2.4. Microbial diversity analysis Wastewater samples were collected at the start-up stage (stage A, the first day) and the stable operation stage (stage B, the seventh day), and then stored at −20 °C until the genomic DNA was extracted. Microbial DNA was extracted using the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to manufacturer’s protocols. The V4–V5 region of the bacteria 16S ribosomal RNA gene were amplified by Polymerase Chain Reaction (PCR) (95 °C for 2 min, followed by 25 cycles at 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s and a final extension at 72 °C for 5 min) using primers 338F 5′-barcode-ACTCCTA CGGGAGGCAGCA)-3′ and 806R 5′-GGACTACHVGGGTWTCTAAT-3′, where barcode is an eight-base sequence unique to each sample. PCR reactions were performed in triplicate 20 μL mixture containing 4 μL of 5× FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu Polymerase, and 10 ng of template DNA. Amplicons were extracted from 2% agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, 423

Cchlorophyll = 20.2A 645 nm + 8.02A 663 nm

(1)

Ccarotenoid = (1000A 470 nm − 2084.33 A 645 nm + 483.19 A 663 nm )/229

(2)

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Table 3 Specifications and market prices of desirable products. Product

Specification

Price ($ kg−1)

Reference

Lipid Chlorophyllin Carotene Protein Carbohydrate

60% 99% 10% 60% 50%

26.01 39.02 65.03 5.49 27.46

Guangzhou Huijian Biological Technology Co. Ltd Shanghai Luan Biological Technology Co. Ltd Shanxi Sciphar Natural Products Co. Ltd Shandong Binzhou Tianjian Biotechnology Co. Ltd Xi’an Lvteng Biological Technology Co. Ltd

where W is the dry weight of biomass harvested from FPCO-PBR (g), V is the working volume of the reactor, t is the interval time of biomass harvesting (d), C is the nutrient concentration of biomass (%), Ct is the concentration of nutrient in effluent, C0 is the concentration of nutrient influent. The economic value of biomass was estimated according to De Francisci et al. (2018). Revenue of biomass (Revenueb) was calculated as follows:

2.6. Data analyses 2.6.1. Statistical analysis Differences in related parameters among three treatments were tested by one-way ANOVA using IBM SPSS software V20 (SPSS, Chicago, IL). The significance threshold was set at a probability level of p = 0.05. Taxonomic alpha diversity was calculated as estimated community diversity by the Shannon index using Mothur software (v.1.30.1).

Revenueb = Ci × Pricei

Revenue of wastewater treatment (Revenuew) was calculated as follows:

2.6.2. Sequencing data analysis The sequence data were processed using QIIME (Version 1.17). All sequence reads were trimmed and assigned to each sample based on their barcodes. The sequences with high quality (length > 300 bp, without ambiguous base ‘N’, and average base quality score > 30) were used for downstream analysis. Sequences were clustered into operational taxonomic units (OTUs) at a 97% identity threshold. The aligned 16S rRNA gene sequences were used for chimera check using the Uchime algorithm (Edgar et al., 2011). Alpha and Beta diversity analyses were calculated, for which the rarefaction curves were generated from the observed species. Taxonomy was assigned using the Ribosomal Database Project classifier (Wang et al., 2007).

Revenue w = Q p × Pricej

RR =

C0 − Ct × 100 C0

Costlight = Elight × Pricek /(Ci × lpath)

(4) −1

where C0 is the initial concentration of N or P (mg L ), Ct is the concentration of N or P at t time (mg L−1). The ammonia volatilization was calculated according to Hansen et al (1998) as follows:

Free NH3 10−pH = ⎛1 + −(0.09018 + 2729.92/T(K)) ⎞ Total NH 4−N 10 ⎝ ⎠ ⎜

3. Results and discussion 3.1. Effects of operation parameters on nutrient removal of FPCO-PBR 3.1.1. Influence of influent mode on nutrient removal of FPCO-PBR Fig. 2 showed the nutrient RE of FPCO-PBR under different influent modes. It indicated that NH4-N RE of MM and SM were significantly higher than UM (p < 0.05, Fig. 2a). On the sixth day, NH4-N was almost completely removed for MM and SM, with RE of 99.13% and 96.28% respectively, while RE of NH4-N was only 85.99% for UM. REs of NH4-N were not significantly different between MM and SM (p > 0.05). NO2-N of MM and SM showed increasing trends on the first 6 days and decreased from the seventh day, while NO2-N of UM was found to increase all the time due to the residue of NH4-N (Fig. 3a). Variations of NO3-N showed no significant differences among the three treatments on the first 4 days, with NO3-N concentration declining slightly at first and then remaining stable (Fig. 4a). NO3-N of MM and SM increased rapidly from the fifth day, while NO3-N of UM showed obviously increasing trends until the eighth day. The increase of NO2-N might be attributed to ammoxidation, and when NH4-N was almost depleted, NO2-N was converted into NO3-N and thus increasing the concentration of NO3-N (Fig. 4a). Variations of TN were consistent with NH4-N. RE of TN showed no significant differences between MM and SM (p > 0.05), but they were significantly higher than UM (p < 0.05,

⎟

(5)

Nitrification rate of NH4-N was calculated as follows:

Nitrification rate = (NO2 − N+ NO3−N)/Total NH 4 − N

(6)

N uptake was determined by the biomass production and TN contained in this biomass, which was calculated as follows:

N uptake rate =

Biomass × N content Total NH 4−N

(7)

The biomass production rate (BPR) of microalgae were calculated according to the equation:

BRR(g L−1 d−1) = W/V/t

(8)

Nutrient recovery efficiency (Yao et al., 2013) and rate (NRR) was calculated using the following equation:

NRR(g L−1 d−1) = BRR × C NRE (%) = NRR/(Ct − C0)/t /V

(13)

where Ci was the production rate of biomass product, Pricei was the price of biomass product, which were obtained from an e-commerce website: www.1688.com (Table 3). Qp was quantity of each pollutant removed by unit biomass produced (=RR/BPR), Pricej was the price of pollutant. According to Molinos-Senante et al. (2010), shadow prices of undesirable outputs, N and P were 9.32 $ kg−1 (8.06 € kg−1) and 35.77 $ kg−1 (30.94 € kg−1), respectively, these prices represent the environmental benefit for avoiding damage by discharging these pollutants in an uncontrolled manner. Elight was the energy consumption for artificial light, it was estimated to be 209 W m−2 according to De Francisci et al. (2018). Pricek was the price of electricity, which were obtained from www.zjzwfw.gov.cn. lpath was the light path (0.025 m) of FPCO-PBR.

(3)

C0 − Ct t − t0

(12)

The cost of artificial light (Costlight) was calculated according De Francisci et al. (2018):

2.6.3. Relative calculation The removal efficiency (RE, %) and removal rate (RR, mg L−1 d−1) of N and P were defined as:

RE(%) =

(11)

(9) (10) 424

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Removal efficiency (%)

100

100

(a)

80

80

60

60

40

40

20

20

0

0

100

80

(c)

Multi-point mode

(b)

Sprinkling mode

Underflow mode

60 40 20 0

Time (d) Fig. 2. Nutrient removal efficiency of FPCO-PBR under different influent modes. Ammonia nitrogen, total nitrogen and total phosphorus were shown in (a), (b) and (c) respectively.

MM (RE of 88.84%), 2.38 mg L−1 for SM (RE of 86.26%) and 5.38 mg L−1 for UM (RE of 67.56%). An appropriate influent mode may improve the hydraulic condition and C source distribution (Wang et al., 2013). The results described above indicated that MM and SM appeared more efficient in nutrient removal. MM and SM could not only increase the utilization rate of C source, but also make the distribution of DO more reasonable in the bioreactor and promote nitrification (Yang et al., 2015), and thus improve the efficiency of nutrient removal. MM and SM also showed good performance in other wastewater treatment processes. For example, MM can improve the N RE of urban sewage treatment plant (Yang et al., 2015), SM can enhance the removal of NH4-N (Wang, 2017).

Fig. 2b). TN concentrations of MM and SM decreased to the minimum on the fifth day (260 mg L−1, RE of 70% approximately), while the minimum of TN was observed on the sixth day for UM (280 mg L−1, RE of 65%). REs of TP under different influent modes were listed in Fig. 2c. Results showed that there were no significant differences between TP RE of MM and SM (p > 0.05), which were higher than that of UM (p < 0.05, Fig. 2c). TP of the three treatments decreased rapidly in the first 3 days, with TP concentrations of 6.50 mg L−1 for MM, 7.01 mg L−1 for SM and 8.76 mg L−1 for UM on the third day. TP remained stable from the third day to the sixth day but decreased again from the seventh day, with the final concentrations of 1.88 mg L−1 for 300 240

NO2-N (mg L-1)

180

(a) Multi-point mode Sprinkling mode Underflow mode

300

(b)

240

Influent HRT=1 d HRT=2 d

180

120

120

60

60

0

0

300

300

(c)

240

240

180

180

120 60

Influent 4d

(d) Inffluent Effluent

120

2d 6d

60

0

0

Time (d) Fig. 3. Variations of NO2-N under different operation parameters. Influent mode, HRT, time interval for biomass harvesting and optimal conditions were shown in (a), (b), (c) and (d) respectively. 425

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150

50

(a) Multi-point mode Sprinkling mode Underflow mode

100

(b) Influent HRT=1 d

40

30

50 NO3-N (mg L-1)

20

0

10

150

(c)

Influent 4d

100

150 2d 6d

(d) Influent

Effluent

100

50

50

0

0

Time (d) Fig. 4. Variations of NO3-N under different operation parameters. Influent mode, HRT, biomass harvesting frequency and optimal conditions were shown in (a), (b), (c) and (d) respectively.

(p < 0.01, Fig. 5a), while NH4-N RE did not significantly increase when HRT was added from 2 d to 3 d (p > 0.05). The NH4-N concentration of effluent remained stable after 4 days’ operation, with RE of 85% for HRT at 2 d and 3 d and 50% for HRT at 1 d. RE of TN was

3.1.2. Influence of HRT on nutrient removal of FPCO-PBR The nutrient RE of FPCO-PBR under different HRT were investigated. The results were showed in Fig. 5. It indicated that NH4-N RE at HRT of 1 d was significantly lower than that at HRT of 2 d and 3 d

Removal efficiency (%)

100

100

(a)

80

80

60

60

40

40

20

20

0

(b)

0 1 3 5 7 9 11 13 15 17 19 21

100

(c)

HRT=1 d

1 3 5 7 9 11 13 15 17 19 21 HRT=2 d

HRT=3 d

80 60

40 20 0

1

3

5

7

9

11 13 Time (d)

15

17

19

21

Fig. 5. Nutrient removal efficiency of FPCO-PBR under different hydraulic retention times. Ammonia nitrogen, total nitrogen and total phosphorus were shown in a, b and c respectively. 426

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lower than that of NH4-N, with the final TN RE of 65%, 65% and 40% for HRT at 2 d, 3 d and 1 d respectively (Fig. 5b). The reason for lower RE of TN was that substantial numbers of NH4-N was converted into NO2-N and NO3-N (Figs. 3b and 4b). TP removal under different HRT was also explored, it was found that RE of TP increased with HRT, but this increasing trends was not significant when HRT was higher than 2 d (Fig. 5c). TP RE of the three treatments began to remain stable from the second day, with RE of approximately 80% for HRT of 2 d and 3 d and around 60% for HRT of 1 d. RE of nutrient removal was observed to increase with HRT when it was not higher than 2 d, so the optimal HRT for FPCO-PBR was considered to 2 d. This value was lower than that of the ordinary PBR and high rate algal pond (4 d) (Chu et al., 2015a; Matamoros et al., 2015). In general, the increase of HRT can improve nutrient RE, but this increase is not unlimited, as the growth of microalgae will be inhibited due to lack of nutrient, which is caused by the increase of nutrient RE (Medina and Neis, 2007). Moreover, higher HRT will increase processing costs. For example, the best microalgae production was observed at a moderate HRT (4 d) according to Chu et al. (2015a). Ferreira et al. (2017) also tested the performances of PBR at a range of HRT (2.1–10.4 d) and found that an HRT of 3.5 d assured the highest biomass productivity and efficient treatment. Therefore, HRT is not the higher the better.

with the highest TN RE in the treatment with time interval for biomass harvesting of 2 d (Fig. 6b). RE of TP under different biomass harvesting frequencies were depicted in Fig. 6c. Results showed that RE of TP remained stable from the ninth day, with TP RE of approximately 80% when time interval for biomass harvesting was controlled at 2 d. RE of TP showed no significant differences between treatments with time interval for biomass harvesting of 4 d and 6 d (p > 0.05, around 65%), but they were significantly lower than that in treatment with time interval for biomass harvesting of 2 d (p < 0.01). Biomass harvesting interval is one of the most significant factors affecting the performance of PBR. If the harvesting interval is too long, a very thick algae biofilm would result, which dramatically decreases light penetration and CO2 transfer to the interior layer of biofilm. On the contrary, too frequent harvesting leads to poor production because this keeps cell growth at the “lag phase”, during which minimal cell growth occurs (Gross et al., 2013). Moreover, frequent harvesting also introduced more mixing into the PBR via pumping of liquid into and out of the PBR with greater frequency, and thus increased wastewater treatment throughput (Novoveská et al., 2016). In this study, effects of biomass harvesting interval on performance of FPCO-PBR were investigated. In indicated that FPCO-PBR performed best in nutrient removal when time interval for biomass harvesting was controlled at 2 d. The specific growth rate increased with the harvesting frequency (Silva and Silva, 2013), so microalgae had high activity and kept high cell density after 2 days’ operation, thus showed good performance in nutrient removal. Therefore, controlling time interval for biomass harvesting at 2 d was reasonable.

3.1.3. Influence of time interval for biomass harvesting on nutrient removal of FPCO-PBR Nutrient removal under different biomass harvesting frequencies were discussed. Results showed that RE of NH4-N remained stable from the fifth day when biomass was harvested every 2 days, with RE of 85% (Fig. 6a). When time interval for biomass harvesting was controlled at 4 d and 6 d, RE of NH4-N remained stable from the ninth day, with RE of 77% and 75% respectively, which were significantly lower than that in the treatment with time interval for biomass harvesting of 2 d (p < 0.05). Concentrations of NO2-N and NO3-N were found to decrease with increase of time interval for biomass harvesting (Figs. 3c and 4c). It meant that increasing time interval for biomass harvesting could reduce the accumulation of NO3-N and NO2-N and thus enhance the removal of TN. This could be confirmed by variations of TN RE,

Removal efficiency (%)

100

3.1.4. Nutrient removal of FPCO-PBR under optimal conditions Nutrient removal of FPCO-PBR was investigated under optimal conditions (with influent mode of MM, HRT of 2 d and time interval for biomass harvesting of 2 d). Results showed that NH4-N concentrations in effluent remained stable (around 20 mg L−1) from the fourth day, with RE of approximately 95% and RR of 200 mg L−1 d−1 (Fig. 7a). Concentrations of TN in effluent also remained stable (221.84–253.75 mg L−1) from the fourth day, with RE of 46.79–59.35% and RR of 125–190 mg L−1 d−1 (Fig. 7b). The high concentration of TN in effluent might be caused by nitrification, which made substantial 100

(a)

80

80

60

60

40

40

20

20

100

(c)

2d

(b)

4d

6d

80

60 40 20 Time (d) Fig. 6. Nutrient removal efficiency of FPCO-PBR under different time interval for biomass harvesting. Ammonia nitrogen, total nitrogen and total phosphorus were shown in a, b and c respectively. 427

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Removal efficiency

100

600

80 60

400

40 200

20 0

0

800 Total nitrogen (mg L-1)

120

(a)

80

(b)

600

60

400

40

200

20

0

Total phosphorus (mg L-1)

40

Removal efficiency (%)

Effluent

Removal efficiency (%)

Ammonia nitrogen

mg L-1

Influent

0 100

(c)

80

30

60 20

40 10

20

Removal efficiency (%)

800

0

0 Fig. 7. Nutrient removal from piggery biogas slurry by FPCO-PBR under optimal conditions.

numbers of NH4-N convert into NO2-N (149.01–158.14 mg L−1) and NO3-N (60.24–66.43 mg L−1) (Figs. 3d and 4d). Concentrations of TP in effluent remained stable (4.88–7.04 mg L−1) from the first day, with RE of 68.34–81.65% and RR of 6–11 mg L−1 d−1 (Fig. 7c). By comparison, RE of TP was higher than that of TN. This might be caused by a high molar ratio of N to P (47:1), which was much higher than the Redfield ratio (16:1) for microalgae growth (Falkowski and Davis, 2004), so the phosphorus was assimilated by microalgae preferentially and thus resulting in the higher RE.

Table 4 Biodiversity analysis of the microbial community in the FPCO-PBR. Stage

OUT number

Shannon index

Simpson index

A B

1058 405

2.52 ± 0.09a 2.33 ± 0.01a

0.15 ± 0.01a 0.18 ± 0.01a

Note: Values were means ± SE. Different letters indicate statistically significant differences (p < 0.05)

Proteobacteria (27.33%), Firmicutes (15.06%), Planctomycetes (2.44%) and Bacteroidetes (2.39%). Comparing with stage A, Cyanobacteria, Proteobacteria and Firmicutes were still the most three dominant phyla at stage B. RA of Cyanobacteria and Proteobacteria declined by 4% and 5% respectively, but RA of Firmicutes rose by 9.6%. The taxonomic classifications of bacterial reads at the genus level were depicted in Fig. 8b. The function and RA of the dominant genera were listed in Table 5. Results showed that most of the genus were related to nutrient removal at stage A, such as Cyanobacteria, Salinarimonas, Bacillus, Rhodobacter, Cyclobacteriaceae and Arthrobacter, with RA of these genera reaching up to 91.12%. Great variations of microbial structure were observed at stage B. For example, RA of Cyanobacteria_norank, Salinarimonas, Bacillus, Rhodobacter, Cyclobacteriaceae_unclassified, Arthrobacter decreased by 34.01%, 19.69%, 5.35%,

3.2. Variations of microbial community Microbial diversity can be estimated by Shannon index and Simpson index. High Shannon index or low Simpson index indicates a stable microbial system (Garcia-Villaraco Velasco et al., 2009). Results showed that no significant differences of microbial diversity index between stage A and B were observed (p > 0.05, Table 4). This indicated that the microbial diversity in FPCO-PBR remained relatively stable. The composition of the major phyla with relative abundances (RA) greater than 1% was illustrated in Fig. 8a. There were 23 phyla at stage A. The five dominant phyla were Cyanobacteria, Proteobacteria, Firmicutes, Bacteroidetes and Actinobacteria, with RA of 4.92%, 32.33%, 5.41%, 3.63 and 2.86% respectively. The number of phyla was 24 at stage B, with the five dominant phyla of Cyanobacteria (50.88%), 428

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Fig. 8. Relative abundance and dynamics of bacterial taxonomic groups in FPCO-PBR at different stages (A and B). The taxonomic classification of bacterial reads at phylum and genus levels are shown in (a) and (b) respectively. Bacterial groups accounting for less than 1% of all classified sequences are summarized to the group ‘‘others”.

Table 5 Function and abundance of the main genus in FPCO-PBR at different stages. Genus

Cyanobacteria_norank Salinarimonas Skermanella Bacillus Clostridium sensu stricto Cyclobacteriaceae_unclassified Arthrobacter Pseudomonas SM1A02

Function

Carbon fixation (Lam and Lee, 2015), nutrient removal (Lynch et al., 2015) Nitrate reduction (Liu et al., 2010) Organic pollutant degradation (Subhash et al., 2017) Heterotrophic nitrification (Mével and Prieur, 2000), Nitrification-denitrification (Kim et al., 2005) hydrolysis and acidification (Rui et al., 2014) Nitrate and nitrite reduction (Srinivas et al., 2014) Nitrification-denitrification (He et al., 2017) heterotrophic nitrification-aerobic denitrification (Sun et al., 2016),Denitrifying polyphosphate-accumulating (Xiao et al., 2009) Nitrification (Chu et al., 2015b)

429

RA (%) Stage A

Stage B

54.89 19.71 0 5.38 0 2.30 2.16 0.002

50.88 0.02 17.14 0.03 7.93 0.20 0 2.45

0

1.95

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Table 6 Biochemical composition of microalgae-bacteria biomass (%). C

N

P

Chlorophyll

Carotenoid

Protein

Carbohydrate

45.37 ± 2.53

6.85 ± 0.97

1.48 ± 0.24

0.92 ± 0.16

0.31 ± 0.12

41.35 ± 3.17

15.76 ± 2.31

Note: Values were means ± SE.

common microalgae (with C content of 16.95–68.8%, N content of 3.55–11.22% and P content of approximately 1%) (Cao et al., 1997). The chlorophyll content was 0.92%, which was equal to that of Chlorella sorokiniana cultured in mixed influent of industrial and municipal wastewater (De Francisci et al., 2018). The carotenoid content was 0.31%, which was higher that of Desmodesmus intermedius cultured in municipal secondary-treated wastewater (< 0.1%) (Osundeko et al., 2014).

Table 7 Estimation of biomass value. Biomass production rate (mg L−1 d−1)

Biomass Lipid Chlorophyll Carotenoid Protein Carbohydrate Sum

470 74.31 4.32 1.46 194.35 74.07 −1

Nutrient removal

Amount (kg kg

Nitrogen Phosphorus Sum

0.34 0.02

)

Total revenue

Revenue ($ kg−1)

Percentage (%)

4.11 0.36 0.20 2.27 4.33 11.27

27.29 2.38 1.34 15.07 28.72 74.80

Revenue ($ kg−1)

Percentage (%)

3.11 0.67 3.79

20.67 4.53 25.20

15.06

100

3.3.2. Nutrient recovery from piggery biogas slurry NRR and NRE were calculated according to Eqs. (9) and (10). Results showed that NRR of TN and TP were approximately 30 mg L−1 d−1 and 7 mg L−1 d−1 respectively, NRE of TN and TP were approximately 20% and 80% respectively (Fig. 9). NRE of TN was much lower than that of TP. This might be caused by ammonia volatilization, as pH in the wastewater remained at 8.5, which was beneficial to the occurrence of ammonia volatilization, thus substantial numbers of NH4N was removed by means of volatilization and NRE of TN was low. NRR obtained in this study was much higher than other studies. For example, Park reported that N NRR from the effluent of an anaerobic was 5–6 mg L−1 d−1 by Scenedesmus sp. (Park et al., 2010). NRR of N and P from biogas slurry supernatant were 10.1 mg L−1 d−1 and 2.0 mg L−1 d−1 by Scenedesmus sp. in batch experiments at bench-scale with artificial illumination (in 12 h dark/12 h light cycle) (Marcilhac et al., 2015).

3.44%, 2.1% and 2.16% respectively at stage B, while RA of some genera related to organic pollutant degradation and nutrient removal increased, such as organic pollutant degradation bacteria Skermanella (Subhash et al., 2017), hydrolysis and acidification bacteria Clostridium sensu stricto (Rui et al., 2014), nitrifying bacteria SM1A02 (Chu et al., 2015b), heterotrophic nitrification-aerobic denitrification and denitrifying polyphosphate-accumulating bacteria Pseudomonas (Sun et al., 2016; Xiao et al., 2009)

3.3.3. Estimation of biomass value and economic potential The economic value of biomass was estimated according to Eq. (11), results showed that the revenue generated from cultivation Desmodesmus sp. CHX1 and organic pollutant degradation bacteria in piggery biogas slurry was estimated to be 15.06 $ kg−1 (Table 7), which was much higher than that of Chlorella sorokiniana (3.61 $ kg−1) cultured in mixed influent industrial/municipal wastewater (De Francisci et al., 2018). The revenue of biomass was estimated to be 11.27 $ kg−1 (74.80%), which included 4.33 $ kg−1 (28.72%) of carbohydrate, 4.11 $ kg−1 (27.29%) of lipid, 2.27$ kg−1 (15.07%) of protein, 0.36 $ kg−1 (2.38%) of chlorophyll and 0.20 $ kg−1 (1.34%) of carotenoid respectively (Table 7). The revenue of nutrient removal from

3.3. Resource recovery potential from piggery biogas slurry by FPCO-PBR 3.3.1. Biomass production rate and biochemical composition Biomass was harvested from bioreactor every two days, with the BPR of 0.47 g L−1 d−1 (Table 7), which was much higher than that of MPBR for treating the effluent of an anaerobic membrane bioreactor system (Viruela et al., 2018). This might be attributed to richer nutrient in this study. The contents of C, N and P in biomass were 45.37%, 6.85% and 1.48% respectively (Table 6), which were consistent with that of the

Nutrient recovery efficiency (%)

100

Nitrogen recovery efficiency

Phosphorus recovery efficiency

Nitrogen recovery rate

Phosphorus recovery rate

80

40

30

60 20 40 10

20

0

Nutrient recovery rate (mg L-1 d-1)

Biomass product

0 2

4

Time (d)

6

8

10

Fig. 9. Nutrient recovery from piggery biogas slurry by FPCO-PBR, Line charts (black) represent nutrient recovery rate, bar charts (blue) represent nutrient recovery efficiency. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 430

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wastewater as an environmental service was estimated to be 3.79 $ kg−1, which included 3.11 $ kg−1 (20.67%) of N removal and 0.67 $ kg−1 (4.53%) of P removal. The cost (illumination) for producing a kilo of microalgae was estimated to be 28.09 $ kg−1, which was higher than the total revenue of biomass and nutrient removal. De Francisci et al. (2018) also found that illumination was the main production cost (accounted for 94.5%) for microalgae production coupled with wastewater treatment, and the production cost can be reduced by 96.0% when the artificial light was replaced by sunlight. Therefore, the substitution of artificial light with sunlight should be carried out in the further study.

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