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Hydrogen recovery from waste activated sludge: Role of free nitrous acid (FNA) in a pre-fermentation-microbial electrolysis cells system Zhihong Liu, Aijuan Zhou, Jiaguang Zhang, Sufang Wang, Yunbo Luan, Wenzong Liu, Aijie Wang, and Xiuping Yue ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b04201 • Publication Date (Web): 30 Jan 2018 Downloaded from http://pubs.acs.org on February 2, 2018

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ACS Sustainable Chemistry & Engineering

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Hydrogen recovery from waste activated sludge: Role of free nitrous

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acid (FNA) in a pre-fermentation-microbial electrolysis cells system

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Zhihong Liu 1, Aijuan Zhou 1, 2, *, Jiaguang Zhang 3, Sufang Wang 1, Yunbo Luan 4,

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Wenzong Liu 5, Aijie Wang 5, 6 and Xiuping Yue 1, 2, *

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Technology, 79 Yingzexi Road, Taiyuan 030024, P.R. China

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Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Haidian District, Beijing 10085, P.R. China

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College of Mechanics, Taiyuan University of Technology, 79 Yingzexi Road, Taiyuan 030024, P.R. China

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College of Architecture and Civil Engineering, Taiyuan University of Technology, 79 Yingzexi Road, Taiyuan 030024, P.R. China

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Shanxi Engineer Research Institute of Sludge Disposition and Resources, Taiyuan University of Technology, 79 Yingzexi Road, Taiyuan 030024, P.R. China

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College of Environmental Science and Engineering, Taiyuan University of

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State Key Laboratory of Urban Water Resource and Environment, Harbin Institute

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of Technology (SKLUWRE, HIT), 73 Huanghe Road, Nangang District, Harbin

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150090, P.R. China

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*Corresponding author:

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College of Environmental Science and Engineering, Taiyuan University of

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Technology, 79 Yingzexi Road, Taiyuan 030024, Shanxi Province, P.R. China

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E-mail: [email protected] (A. Zhou); [email protected] (X. Yue);

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Tel./fax: +86 0351-3176581.

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ACS Paragon Plus Environment

ACS Sustainable Chemistry & Engineering 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Abstract

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Due to the limited hydrolysis rate of particulate organics and suitable substrates for

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hydrogen-producing bacteria in raw waste activated sludge (WAS), traditional

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fermentative hydrogen production has low hydrogen yield and energy recovery

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efficiency. The role of free nitrous acid (FNA) pretreatment on WAS and hydrogen

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recovery was investigated in a pre-fermentation-microbial electrolysis cells (MECs)

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system. The results demonstrated that WAS hydrolysis and acidification were

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enhanced by FNA pretreatment. Notably, the accumulation of acetic acid and

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propionic acid eventually reached to 55% and 22% during pre-fermentation. During

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MECs cascade utilization, volatile fatty acids (VFAs) were exhausted and the

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utilization efficiencies of soluble carbohydrates and proteins reached 62% and 41.5%,

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respectively. The hydrogen yield from FNA-pretreated sludge was 1.44 mL/g VSS,

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which was approximately 3 times than that of the control. High-throughput

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sequencing and canonical correspondence analysis revealed that FNA pretreatment

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promoted the hydrolysis and acidification of particulate organics through

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accumulating anaerobic fermentation bacteria (AFB) in pre-fermentation, furthermore,

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stimulated the increase of electrochemically active bacteria (EAB) and therefore

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enhanced the current and hydrogen production. This study may provide a sound basis

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for the potential implementation of FNA pretreatment to accomplish organics

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cascading utilization and the synchronous recovery of energy from WAS.

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Keywords

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Waste activated sludge (WAS); Free nitrous acid (FNA); Anaerobic pre-fermentation;

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Microbial electrolysis cells (MECs); Hydrogen production

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Introduction Waste activated sludge (WAS) is the most voluminous by-product of biological

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wastewater treatment, which requires proper handling, treatment and disposal.1-4 In

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China, 30~40 million tons of WAS (80% moisture content) are produced annually, and

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the amount is expected to reach 60~90 million tons by 2020. WAS is considered to be

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a potential source of organic compounds, containing large amounts of carbohydrates

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and proteins, which account for 35-61% and 7-11% of the total chemical oxygen

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demand (TCOD) of WAS.5 Relevant research has concentrated on transforming these

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considerable organics into biogas, fertilizer, or easily-biodegradable carbon sources.6

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Recently, greater attention has been paid to bio-hydrogen production from WAS due

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to its high energy yield of 142.9 KJ/g and zero pollution in utilization.7

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Unfortunately, most biodegradable organics in WAS is either enclosed inside the

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microbial cell wall or enmeshed in extracellular polymeric matrix, which further

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contributes to limit the biodegradability of these organics into hydrogen. In order to

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accelerate the hydrolysis of organic matters, WAS is often pretreated by various

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physical and chemical methods prior to test.8 Lately, free nitrous acid (FNA),

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protonated form of nitrite, was demonstrated to be an effective and promising method

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for WAS pretreatment.9-11 It was found that FNA increased biodegradability by

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generating multiple inhibitory effects on microbial metabolism of extensive

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microorganisms,12 being extraordinarily biocidal to microorganisms at parts per

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million (ppm) or even sub-ppm levels.13-14 FNA pretreatment was reported to be

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effective on enhancing hydrolysis rate, volatile fatty acids (VFAs) and methane

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production of WAS. The biodegradability of FNA-pretreated sludge could be

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promoted by 20%-50% based on biochemical methane potential (BMP) testing.10, 15-16

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Methane production could be improved by 10-30% at FNA concentrations in the 3



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range of 0.4-2.1 mg N/L for 24 h.10 Additionally, Li et al. (2016) proved the shorter

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fermentation time and highest VFAs production could achieve at 1.8 mg N/L of

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FNA.17 However, an improvement of the biogas production rate is further considered

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by upgrading conventional AD systems.18-19

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MECs is a prospective technology for hydrogen production via the utilization of

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substrates from WAS. Recently, a number of studies have suggested that combine

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MECs with anaerobic digestion (AD), could not only increase the positive synergisms

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established in the digesters, but also provide an utilization route for the existing

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digestate, which further accomplish the bio-hydrogen recovery from WAS.7 Wang et

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al. (2014) obtained the yield of hydrogen at 8.5 mg H2 g-1 VSS feeding with sodium

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dodecylsulphate (SDS)-treated WAS fermentation liquid (SFL).5 Zhou et al. (2016)

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harvest 12.90 mg H2 g-1 VSS from Rhamnolipid bio-surfactant (RL)-treated SFL.20

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In this study, the feasibility of hydrogen recovery and cascade utilization of

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organics from WAS were investigated in a pre-fermentation-microbial electrolysis

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system. The performance of FNA pretreatment on WAS solubilization and

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acidification during the pre-fermentation process was studied. The hydrogen

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production and the degradation rules of the substrates of the fermented

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FNA-pretreated WAS were analyzed during the subsequent MECs process.

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Furthermore, high-throughput sequencing analysis was used to evaluate the

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mechanism of FNA pretreatment for enhancing hydrogen recovery from WAS on the

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microorganism level. In addition, we used canonical correspondence analysis (CCA)

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to assess the correlations between major environmental variables and functional

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microbial populations.

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Material and methods

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FNA pretreatment and pre-fermentation of WAS

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WAS was collected from a Zhengyang municipal wastewater treatment plant

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(WWTP) in Jinzhong City, China. Prior to use, WAS was concentrated by settling for

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24 h at 4 °C, and the supernatant was then removed. To prevent clogging problems,

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concentrated WAS was screened with a 40-mesh sieve. The main characteristics

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(average value, plus standard deviation of triplicates) were as follows: total suspended

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solids (TSS): 26.4 ± 0.4 g/L; volatile suspended solids (VSS): 12.1 ± 0.2 g/L; total

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chemical oxygen demand (TCOD): 26.2 ± 0.2 g/L; soluble chemical oxygen demand

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(SCOD): 0.41 ± 0.05 g/L; VFAs: 255 ± 12 mg COD/L; soluble carbohydrates: 44.56 ±

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8 mg COD/L; and soluble proteins: 37.5 ± 2 mg/L, pH 7.03 ± 0.02.

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Three serum bottles with a working volume of 400 mL each were set-up for

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WAS pretreatment by FNA. This process was termed “Phase 1”. A nitrite stock

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solution was first added to reactors to achieve a NO2--N concentration of 300 mg N/L.

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The pH was adjusted to 5.5 ± 0.1 by adding 3.0 M HCl. Twenty milliliters of

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phosphate buffer solution (PBS) was added in each reactor to maintain moderately

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acidic conditions. The pretreatment lasted for 24 h, during which the temperature was

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maintained at 22 ± 1 °C. Under these conditions, the FNA concentration was titrated

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to 2.13 mg N/L based on a calculation applying the formulae SNO2--N/(Ka×10pH) and

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Ka=e-2300/(273+T) 21, which proved to be an optimal FNA dosage for WAS

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solubilization.10 To evaluate the effect of FNA pretreatment, NO2--N, NO3--N, NH4+-N,

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soluble proteins and soluble carbohydrates were measured every 4 hours.

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Anaerobic pre-fermentation experiments were carried out in six batch reactors, a

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process that was termed “Phase 2”. These reactors, with a 400-mL working volume

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each, were divided into two groups (every three reactors were replicates for each 5



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group). One group was fed concentrated WAS (hereafter referred to as the

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AD_Control test). The feedstock for the other group was FNA-pretreated WAS

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(hereafter referred to as the AD_FNA test). Some of the fresh sludge was maintained

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under aeration at room temperature overnight and then used as inoculum with an

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addition ratio of 1:9 (v/v). Nitrogen gas was pumped into reactors for 10~15 min in

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order to remove oxygen; then, all reactors were capped, sealed, and stirred in an

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air-bath shaker (120 rpm) at 35 ± 1 °C.

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MECs reactors setup and operation

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Ten single-chamber MECs reactors were constructed simultaneously and

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inoculated using aeration tank effluent from the same WWTP in Jinzhong. Details of

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volume, anode and cathode were provided previously.7, 22 The reactors were operated

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at room temperature (22 ± 1 °C), a voltage of 0.80 ± 0.01 V was applied (Switching

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Power Supply, FDPS-150, Fudantianxin Inc. China), and 100 mM PBS (pH 7.0) and

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1,500 mg/L acetate were prepared for the feedstock of the MECs set-up. The setup

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time lasted for 10 days in approximately 24-h batches until the currents reached 2 mA,

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and the coulombic efficiencies were greater than 90%. Then, six parallel reactors were

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selected for the subsequent experiment. Reactors were divided into two groups in a

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process termed “Phase 3”. The pre-fermented WAS was added to each group, mixed

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with 100 mM PBS (VWAS:VPBS=1:1) and incubated for 3 days as a cycle to make full

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use of the organics.

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DNA extraction and Illumina MiSeq sequencing

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Illumina MiSeq sequencing was used to analyze the diversity of the microbial

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community. Before DNA extraction, sludge samples of pre-fermentation experiments

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and carbon brushes (bio-anode) were centrifuged at 8000 × g to remove the

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supernatant. DNA was extracted in triplicate from sludge sediments using an EZNA® 6


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Soil DNA kit (Omega Bio-Tek, Inc., Norcross, GA, USA) and subsequently pooled

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together. Based on the amplification of V3-V4 region of the 16S rRNA gene, the

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commonly used PCR primers 341F (5′-CCCTACACGACGCTCTTCCGATCTG-3′)

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and 805R (5′-GACTGGAGTTCCTTGGCACCCGAGA ATTCCA-3′) were selected.

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During sequencing, sample multiplexing was achieved by means of the incorporation

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of barcodes between the forward primer and 454 adaptor. Polymerase chain reactions

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(PCRs) were performed as described previously.23-24 Then, the PCR amplicon was

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purified and quantified and was further applied to sequencing on an Illumina MiSeq.

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For taxonomic analysis, the 16S rRNA gene sequences read was performed

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individually using Ribosomal Database Project (RDP) classifier. Raw sequence data

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of the study were deposited to the NCBI Short Read Archive database with the

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accession no. SRR5482157. Notably, we trimmed the adapters, barcodes, and primers

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in all raw sequences to avoid the effects of random sequencing errors. The sequence

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was removed if it was shorter than 350 bp or contained any ambiguous base cells.

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The remaining sequences were clustered into operational taxonomic units

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(OTUs) using a 97% identity threshold (3% dissimilarity level). Rarefaction curves

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were generated and alpha diversity measurements, including the Shannon index

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(http://www.mothur.org/wiki/Shannon), Chao1 index

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(http://www.mothur.org/wiki/Chao) and ACE index

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(http://www.mothur.org/wiki/Ace), were calculated for each sample. BLAST of

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taxonomic classification was conducted using MOTHUR program via SILVA database

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with a set confidence threshold of 80%. Hierarchical clustering analysis (HCA) was

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used to display the interrelationship among the four samples intuitively on the basis of

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the depths of colors, which represented the abundance of the bacteria communities. In

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this study, we performed HCA analysis using GraPhlAn 0.9.7 and iTOL 3.2.1. 7



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Cytoscape v3.2.1 was used to generate OTUs networks, which could further delineate

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the similarity and diversity between the different sludge samples. The central portion

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was shared OTU, while the species and numbers of the other bacterium showed the

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diversity among the four samples.25 Principal coordinates analysis (PCoA) visually

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reflected beta diversity, which was calculated using the distance matrices based on the

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UniFrac phylogenetic method. Canonical correspondence analyses (CCA) were

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generated by Canoco 4.5 to evaluate the specific correlations between characteristic

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genera and environmental factors measured in the study, including methane, VFAs,

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NO2--N, current, hydrogen, soluble proteins and carbohydrate concentrations. The

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relative abundance of 16 characteristic bacteria was used in the CCA analysis.

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Analysis and calculation methods

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WAS samples were centrifuged at 10,000 × g for 10 min, filtered through a

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0.45-µm cellulose nitrate membrane filter and stored at 4 °C prior to analysis. TCOD,

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SCOD, TSS, VSS, NH4-N, NO3--N and NO2--N were measured according to standard

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methods (APHA, 1998). Soluble proteins were measured using a bicinchoninic acid

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(BCA) protein quantitation kit, whereas soluble carbohydrates was determined using

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the phenol–sulfuric acid method with glucose as the standard.26 VFAs were analyzed

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using gas chromatography, and the total concentration comprised acetic acid (HAc),

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propionic acid (HPr), n-/iso- butyric acid (n-HBu/iso-HBu) and n-/iso- valeric acid

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(n-HVa/iso-HVa), expressed as the COD concentration (mg COD/L). The equivalent

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relationships between COD and various VFAs were as follows: 1.07 g-COD/g-HAc,

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1.51 g-COD/g-HPr, 1.82 g-COD/g- (n-/iso-) HBu, and 2.04 g-COD/g- (n-/iso-) HVa.

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The gases produced by anaerobic reactors and MECs were collected in gas bags, and

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the total volumes were measured by glass syringe. Gas composition was analyzed by

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gas chromatography. The voltage and current were recorded via a data recorder 8


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(model 2700, Keithley Instrument, USA) every 10 minutes. Crucial indices to judge

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the performance of MEC reactors included coulombic efficiency (CE), current density

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(A/m2, normalized to the projected cathode area), hydrogen production rate Q

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(m3-H2/m3/day), hydrogen yield YH2 (mL H2/g VSS) and energy recovery efficiency.27

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Energy efficiency (ηE, %) was calculated on the basis of electricity input as ηE = WH2 /

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Win, where WH2 is the energy content of hydrogen based on the heat of combustion

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(upper heating value of 285.83 kJ per mole of H2) and Win is the electricity input

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determined as ܹ௜௡ = ‫ܧܫ(׬‬௔௣ − ‫ܫ‬ଶ ܴ) ∙ ݀‫ݐ‬, where Eap is the applied voltage (0.8 V in

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this study) and R is the external resistance (10 Ω) 28.

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Results and discussion

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Effect of FNA pretreatment on WAS solubilization and acidification

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The degree of WAS solubilization can be expressed by the change of soluble

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protein and carbohydrate levels.29-30 As shown in Fig. 2A and B, soluble carbohydrate

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and protein levels increased with FNA pretreatment and peaked at 298.7 ± 6.5 mg

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COD/L and 665.3 ± 30.1 mg COD/L (12 h), which was 6.7 and 18.4 times higher than

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that of the control, respectively. The eventual values were still much higher than that

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of un-pretreated WAS. Remarkably, NO2--N concentration increased with treatment

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time and peaked at 226.9 ± 7.5 mg/L during the initial 12 h. NO3--N content also

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achieved its maximum (40.4 ± 3.9 mg/L), which demonstrated that the nitration

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process occurred during Phase 1 (Fig. 1A). No methane was produced but 140 mL of

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gas was collected at the end of the pretreatment, consisted with N2 and N2O. The

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results could in accordance with Wang et al. (2013), who detected only N2 on the first

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day and proved that denitrification occurred during FNA pretreatment.10 In short, the

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results indicated that FNA pretreatment was effective in enhancing WAS

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solubilization. 9



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In 96-h pre-fermentation, the effect of FNA pretreatment on soluble

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carbohydrates and proteins is shown in Fig. 2A and B. Both soluble carbohydrates and

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proteins were clearly elevated from 48 h onward and decreased slightly with the time

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extension. By comparison, soluble proteins fluctuated slightly. The final

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concentrations of soluble carbohydrates and proteins in the AD_FNA test (120 h)

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were 2.46 and 4.14 times higher than that of the AD_Control test. Analogously, as a

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by-product of protein degradation, the trend of ammonia was similar with that of

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proteins (Fig. 1B). The concentration of VFAs in the AD_FNA test reached its

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maximum (1761 mg COD/L) on the fourth day, which was 5.0-fold of that obtained in

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the AD_Control test (Fig. 2C, Phase 2). Obviously, the components of VFAs reflected

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significant difference. HAc and HPr, the two top individual VFAs, reached 55% and

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22% in the AD_FNA test, respectively (Fig. 2D). However, the levels in the

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AD_Control test were 10% and 61%, respectively (Fig. 2D). The n-HVa was the

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lowest VFAs in any of the measured reactors, which was less than 3%. Thus, FNA

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pretreatment could expedite the production of VFAs, particularly HAc.

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Hydrogen recovery and organics utilization during MECs cascade utilization

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Fig. S1A depicted the current variation during the MECs startup. The average

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current tended to be stable and eventually decreased from ~7 mA to 2 mA (> 2 mA).

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Moreover, the coulombic efficiency and hydrogen yield rate were elevated from 85 ±

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6% to 101 ± 5% and 79 ± 6% to 154 ± 12%, respectively. On basis of the energy of

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electricity input, the average energy efficiency of hydrogen yield was up to 140 ± 11%

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at 0.8 V applied voltage. These phenomena implied that the MECs were successfully

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constructed.

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During MECs operation, the variation of electron transport (current) was

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connected with soluble substrates, especially VFAs degradation.5 As shown in Fig. 10


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S1B, FNA pretreatment contributed to abundant soluble substrates for exoelectrogens,

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accordingly, the maximal current density was 11 A/m2; while the limited releases of

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soluble substrates in the control group resulted in the lower current density (only 4

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A/m2). The currents both stepped to decreases along with the consumption of soluble

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substrates (Fig. 2C). The hydrogen production yield was 1.44 mL/g VSS in the FNA

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test, which was 2.7-fold higher than that of the control test (Fig. 2E). FNA

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pretreatment promoted accumulation of HAc and HPr in pre-fermentation (account

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for 77% of VFAs), which benefited hydrogen production and further shortened

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operational time.5

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As seen from Fig. 2A, the soluble carbohydrates began to decline sharply at 126

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h and the final removal efficiency increased to 62% in the FNA test, whereas in the

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control test, it was only 43%. The change of ammonia was consistent with soluble

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proteins (Fig. 1A), which markedly increased at 126 h and then appeared to integrally

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decrease. However, the eventual removal efficiencies (34.7% and 41.5% in control

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test and FNA test, respectively) were still less than that of soluble carbohydrates (Fig.

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2B). These results implied that soluble carbohydrates were much easier to utilize by

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exoelectrogens than large-molecule proteins.

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VFAs, one of the main compositions of fermented WAS, could be easily

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consumed by exoelectrogens,28 as shown in Fig. 2C, reduced acutely in both FNA and

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control tests; the respective utilization efficiencies were 99% and 83%. It is reported

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that HAc and HPr are preferred as main electron donors, which provide significant

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current generation, while the other VFAs (HBu and HVa) mainly contribute to

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exoelectrogens metabolism.31 Accordingly, all components of VFAs were also

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consumed maximally (Fig. 2E). The results demonstrated that on the basis of FNA

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pretreatment, the soluble substrates released in pre-fermentation accomplished the 11



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cascade utilization in MECs and further achieved higher hydrogen production.

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Microbial community analysis

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The bacterial communities of AD_Control and AD_FNA, as well as the biofilm

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developed on the anodes of MECs, fed with fermented AD_Control sludge

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(AD_MEC_Control) and AD_FNA sludge (AD_MEC_FNA) were analyzed using

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Illumina MiSeq sequencing. In total, as shown in Table S1, the number of OTUs in

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the four bacteria samples was 13,697, of which 542 OTUs (4% of the total OTUs)

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were shared by all samples. The shared OTUs were mainly grouped into three phyla:

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Proteobacteria (41.9%), Bacteroidetes (12.7%), and Firmicutes (10.9%) (Fig. 4).

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Rarefaction curves for all libraries not only illustrated that the sequences of all

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samples were reasonable but also displayed shapes indicative of eﬀective sampling of

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the community diversity (Fig. S2A). The microbial diversities of the involved

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communities in all samples were assessed based on α-diversity. Under FNA

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pretreatment, the Shannon index of samples decreased from 6.08 (AD_Control) to

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5.47 (AD_FNA), and further declined to 5.45 (AD_MEC_FNA) in the MECs system

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(Table S1). Based on Chao1 and ACE indices, which indicate richness, the

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AD_MEC_FNA sample also had relatively lower diversity (13,493 and 23,228,

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respectively). β-diversity was used to calculate and examine the similarity of the

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microbiome. PCoA exhibited the dissimilarity of bacterial community composition

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among the samples, based on unweighted UniFrac (Fig. S2B). The principal

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components 1 and 2 were 36.53% and 34.19%, respectively. In addition, the four

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samples were clearly separated from each other, which illustrated the significant

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differences in the bacterial community composition (Fig. S2B). This could be further

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demonstrated by the hierarchical clustering analysis results (Fig. 3D).

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Phylogenetic differences in 16S rRNA gene sequences were characterized at 12


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three different level classifications, including that of phylum, class and genus levels,

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to further investigate the diversity of the microbial community. The three dominant

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phyla, Proteobacteria, Bacteroidetes and Firmicutes, which have been identified in

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anaerobic32-34 or bioelectrochemical systems,35-37 were identical but with different

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relative abundance for all samples (Fig. 3A). At the class level, the majority of

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sequences belonged to 10 classes, among which Bacteroidia, Clostridia,

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Betaproteobacteria and Gammaproteobacteria were the shared predominant groups

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(Fig. 3B). Bacteroidia, Gammaproteobacteria and Clostiridia are capable of

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producing VFAs by utilizing soluble organics,38 which were all enhanced by FNA

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pretreatment no matter what subsequent treatments were. Bacteroidia and Clostiridia

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clearly increased in the AD-MEC cascading system, which may improve the

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generation of amino acids during protein hydrolysis39 and hydrogen production,40

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respectively.

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To obtain more detailed information on microbial communities, the genus level

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of different samples was examined (Fig. 3C). Macellibacteroides (class Bacteroidia),

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which can produce lactate, HAc, HBu and iso-HBu from glucose metabolism and

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utilize many kinds of carbohydrates,41 enriched to 6.6% in AD_FNA and further

319

accumulated to 15.6% in AD_MEC_FNA, whereas it was only 0.4% and 1.7% in

320

AD_Control and AD_MEC_Control, respectively. Petrimonas (class Bacteroidia),

321

which are closely related to the utilization of various sugars and produce HPr and

322

HAc as their primary products,42 peaked at 4.8% in AD_MEC_FNA.

323

Proteiniclasticum (class Clostridia), which cannot utilize carbohydrate but can utilize

324

soya peptone, tryptone and amino acids,43 accumulated to 4.2% in AD_FNA versus

325

0.4% in AD_Control. Acetoanaerobium (class Bacilli), which can produce HAc from

326

H2 and CO2,44 was enriched to 3.2% in the AD_MEC system. The detailed 13



327

distribution information of these four predominant anaerobic fermentation bacteria

328

(AFB) is illustrated in Fig. 4. The total relative abundance of AFB,41-49 as shown in

329

Fig. 3C, illustrated significant differences among the four samples. AFB were

330

enriched from 4.2% (AD_Control) to 13.6% (AD_FNA) and were further enhanced to

331

19.5% (AD_MEC_Control) and 34.4% (AD_MEC_FNA) after MECs cascading

332

treatment. The results demonstrated the intense solubilization and acidification of

333

FNA-pretreated WAS occurred during AD_MEC cascading system. Electrochemically

334

active bacteria (EAB), classified in a previous study,40 and which mainly attach to

335

anodes, improved the decomposition of organic matter by consuming VFAs produced

336

during pre-fermentation and converting e- and H+ (produced in the process) to H2 (Fig.

337

3C). Their distributions intuitively demonstrated that EAB emerged only in MECs

338

rather than in pre-fermentation (Fig. 4). The total relative abundances increased to 5.5%

339

of AD_MEC_Control versus 8.7% of AD_MEC_FNA, which explained the

340

phenomenon of the higher hydrogen and current generated in the latter than that in the

341

former. In addition, Ottowia, Thermomonas (class Proteobacteria) and

342

Phaeodactylibacter (class Sphingobacteria), classified as nitrate-reducing bacteria

343

(NRB), peaked at 13.7% in AD_FNA. Notably, Ottowia, which could contribute to

344

denitrification and produce N2O in the end,50 was enriched to 7.0% in AD_FNA

345

versus 3.4% in AD_Control. Its distribution was displayed in Fig. 4, which explained

346

the denitrification that occurred in the AD_FNA test.

347

Correlation between environmental variables and microbial populations

348

To further explore the plausible relationship among characteristic genera, various

349

environmental and performance measurements in WAS pre-fermentation and MECs

350

systems, including soluble carbohydrates, VFAs, NO2--N, soluble proteins, hydrogen

351

and current, we performed CCA using 16 characteristic bacteria (Fig. 5). The soluble 14


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352

protein and hydrogen contents as well as the current were found to be positively

353

associated with the first canonical axis (accounting for 61.9% of the variance of

354

genera distribution), whereas the other endpoints showed a negative correlation. For

355

axis 2 (explaining 31.9% of the variance), only VFAs, soluble carbohydrates and

356

NO2--N showed good positive correlations. Detailed information was shown in Table

357

S2.

358

The strength of the relationship between environmental variables and the

359

microbial community could be indicated by the length of a vector that exists in an

360

arrow-line form. As implied, soluble carbohydrates, current and hydrogen were

361

significantly relevant to the microbial community based on the length of the vector,

362

followed by VFAs and soluble proteins. Moreover, the intersection angle between

363

VFAs and soluble carbohydrates implied VFAs and soluble carbohydrates were

364

prominently correlated, and VFAs would be influenced when the soluble carbohydrate

365

content changed. Additionally, all factors were related to NO2--N. In other words, free

366

nitrite increased as VFAs and soluble carbohydrates accumulated at the beginning of

367

FNA pretreatment, whereas it decreased because of subsequent denitrification. During

368

this process, characteristic bacterial genera, such as Ottowia, Thermomonas,

369

Comamonas and Phaeodactylibacter, were enriched. As stated previously, we also

370

found that current and hydrogen had a very high positive interaction with partial

371

bacterial genera, including Olsenella, Cloacibacillus, Geobacter, Acetoanaerobium

372

and Pseudomonas, which were the common genera in AD_MEC_Control and

373

AD_MEC_FNA. Additionally, Macellibacteroides, Proteiniphilum and Petrimonas

374

were the dominant genera in AD_MEC_FNA, which further contributed to hydrogen

375

production. This is the reason why the hydrogen yield of AD_MEC_FNA was higher

376

than that of AD_MEC_Control. In this sense, the correlation between environmental 15



377

variables and characteristic genera further specified the function of the genera and

378

was consistent with the conclusion of process assessment. The CCA results confirmed

379

that the relationship between community structure and the measured variables may

380

reveal precious insight into the WAS pre-fermentation and MECs system.

381

Significance and potential implementation for practice

382

Most sludge pretreatment technologies require substantial energy input, the

383

external addition of chemical reagents and high costs, which hinder its large-scale

384

application. In this study, FNA pretreatment has been proven to be a low-cost and

385

eco-friendly technology for WAS, which can serve as an ideal approach for energy

386

recovery from a WAS pre-fermentation-MECs system. Moreover, the MECs system

387

would serve as the essential energy recovery processing unit for WWTPs due to its

388

short operation time, high energy efficiency and excellent performance.

389

Fig. 6 illustrates the enhanced energy recovery concept with the combined

390

system applied in a WWTP. In practice, FNA could be obtained in situ as a byproduct

391

from anaerobic digestion liquor during the nitritation process of wastewater treatment

392

in a WWTP.10, 51-52 The produced FNA could evaluate WAS hydrolysis and

393

acidification in anaerobic pre-fermentation and release adequate soluble substrates for

394

MECs. MECs system would accomplish complete cascade utilization of the

395

aforementioned organics by converting them to hydrogen and electric energy, which

396

is beneficial for energy recovery and cost savings. It was reported that the net benefit

397

($132600 per annum) can be achieved at 2.13 mg HNO2−N/L condition during

398

20-days digestion from the heat and energy converted by methane.10 The benefit from

399

hydrogen production was expected to be much higher than the ones from methane

400

because of its higher calorific value. Additionally, the degradation of WAS lead to

401

lower disposal and transport costs. Certainly, the potential challenges should be 16


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402

further assessed in WWTPs practical implementation and related analysis should be

403

focused on the life cycle, economic and ecological evaluations in whole processes for

404

ensuring sustainability.

405

Conclusion

406

This study examined the feasibility of FNA pretreatment on hydrogen recovery

407

from WAS in a pre-fermentation-MEC system by process assessment associated with

408

microbial community network analysis. FNA pretreatment was effective in enhancing

409

WAS solubilization and acidification, promoting soluble substrates cascade utilization

410

in MECs and accelerating hydrogen and energy recovery. The highest hydrogen yield

411

from FNA pretreated WAS was 1.44 mL/g VSS, which was 2.7-fold higher than that

412

obtained from un-pretreated WAS, meanwhile, the current density peaked to 11 A/m2

413

of the former while it was only 4 A/m2 of the latter. It is worth noting that FNA

414

pretreatment stimulated accumulation of functional microorganism (AFB in

415

pre-fermentation and EAB in MECs), produced abundant soluble substrates and

416

further enhanced hydrogen production. In other words, FNA pretreatment played a

417

crucial role in the construction of the WAS innate microbial community by

418

sequencing, CCA and microbial network analysis. Zero-pollution and the in situ

419

synthesis during wastewater treatment would promote the crucial implementation of

420

FNA for WAS treatment in WWTPs.

421

Supporting Information

422

Alpha diversity of four WAS samples; The eigenvalues of first two canonical

423

axes and their relationships with each environmental factor; Current generation of

424

MECs; Rarefaction curves of the four bacterial communities based on sequencing of

425

16S rRNA gene; PCoA analysis based on the unweighted UniFrac analyses.

426

Acknowledgements 17



427

This research was supported by the National Natural Science Foundation of

428

China (NSFC, Nos. 51608345, 51708386 and 21501129), by the China Postdoctoral

429

Science Foundation (Nos. 2015M570241, 2016M591416 and 2017T100170), by the

430

Open Project of Key Laboratory of Environmental Biotechnology, CAS (No.

431

kf2016004), by the Key Research and Development (R&D) Project of Shanxi

432

Province (No. 201603D321012) and the Scientific and Technological Project of

433

Shanxi Province (No. 201701D221230 and 201601D021130).

434

References

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21



Figure captures

605 606

Figure 1 Changes of NO3--N and NO2--N concentrations under FNA pretreatment

607

during 24 h (A). Time-course profiles of NH4+-N concentration in the

608

whole process (B).

609

Figure 2 The cascade utilization of soluble carbohydrate (A) and protein (B) during

610

the FNA pretreatment, pre-fermentation and MEC operation, respectively.

611

Accumulation and consumption of VFAs in pre-fermentation and MEC

612

operation (C), each component content of VFAs and corresponding

613

production of methane and hydrogen yield at the end of pre-fermentation

614

and MEC operation, respectively (D and E).

615

Figure 3 Relative abundance was defined as the number of sequences per sample.

616

Taxonomic classification of sequences from the four WAS bacterial

617

communities at the phylum (A), class (B) and genus (C) levels.

618

Hierarchical clustering analysis of bacterial communities of four samples

619

(D).

620

Figure 4 Microbial network analysis of the four WAS bacterial communities; overlap

621

of the four bacterial communities based on OTU (3% distance).

622

Figure 5 Canonical correspondence analysis (CCA) between enriched genera and

623

environmental variables [soluble carbohydrates, VFAs, NO2--N, soluble

624

proteins, hydrogen and current].

625 626

Figure 6 Implement of FNA-based pre-fermentation-microbial electrolysis cells system in practice.

627

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Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Abstract Graphic

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Synopsis

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This study displayed the crucial role of FNA pretreatment on hydrogen and energy recovery from WAS in pre-fermentation-microbial electrolysis cells system and its potential implement in WWTP.

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Hydrogen Recovery from Waste Activated Sludge - ACS Publications

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