Research advances in deriving renewable energy from biomass in wastewater treatment plants

Yuan-kai Zhang, Xiu-hong Liu, Xiao-wei Liu, Yi-fei Zha, Xiang-long Xu, Zheng-guang Ren, Hang-cheng Jiang and Hong-chen Wang*
School of Environment & Natural Resource, Renmin University of China, No. 59 Zhongguancun Street, Haidian District, Beijing 100872, China. E-mail: whc@ruc.edu.cn; Fax: +861062510853; Tel: +861062510853

Received 15th March 2016 , Accepted 19th May 2016

First published on 19th May 2016


Abstract

Anaerobic digestion (AD) can be used to derive renewable energy from biomass in wastewater treatment plants, and the produced biogas represents a valuable end-product that can greatly offset operation costs. However, AD is a complex biological process involving many different kinds of microbial communities and biotransformation processes, and it is easily affected by start-up and operation conditions. In order to facilitate improvements in the AD process and enable predictions of biogas production quantities, this review discusses the microbial dynamics, scientific models, and reinforcement strategies of AD biogas production (i.e., sludge pre-treatment, bio-electrochemical and Fe/Fe3O4 technologies). During sludge pre-treatment, the focused-pulsed method can be introduced to promote cell lysis, and this requires specific treatment chamber designs. The bio-electrochemical method, especially in regard to the use of microbial electrolysis cells (MECs), can obviously improve biogas production while only consuming small amounts of energy. Mechanisms of AD-MEC systems and associated influencing factors are described in detail in this review. Engineered nanomaterials, which are increasingly being used in commercial products, may have detrimental or even beneficial effects on the sludge AD process. The use of low doses of Fe/Fe3O4 nanomaterials (≤0.5% (w/w)) as an economically viable and environmentally sustainable method for improving biogas production is discussed. In addition, removal methods for dissolved methane, which can have a negative environmental impact if not handled properly, in the anaerobic digester effluents are discussed in this review.


1 Introduction

Wastewater treatment processes consume large amounts of energy and produce unwanted by-products such as sludge,1 and large amounts of sludge are generated in wastewater treatment plants (WWTPs) every year. In 2014, total sludge production (total solids of 20% or more) amounted to over 3.6 × 107 t in China, and sludge production within each province is shown in Fig. 1.2,3 Most of the sludge was generated in the eastern region of China, and Guangdong province had the maximum amount of 5.2 × 106 t (Fig. 1). Sludge is a complex material that typically contains about 50% protein, 10% carbohydrates, 10% lipids, and 30% other components such as RNA and fiber,4 and treatment and disposal costs for sludge are typically high, i.e., these costs generally amount to 20–45% of the total WWTP capital costs in China.5 Common sludge disposal techniques include land applications, sanitary landfill disposals, and incineration. However, these methods are neither economical nor sustainable, and they generate vast amounts of so-called greenhouse gases. Given the energy crisis and increasingly strict sludge disposal laws, cost-effective and sustainable technology for sludge management is urgently needed. Anaerobic digestion (AD) occurs in oxygen-free systems, and this process can utilize organic matter to produce biogas that is an efficient and renewable energy source. Biomass treatment by AD technology is associated with many co-benefits in that this technology can achieve significant reductions in organic matter, pathogens, and odors while imparting the obvious benefits that can be realized by producing energy from biogas to discount WWTP operation costs. Because of this promising potential, AD technology has been attracting significant attention from researchers. As illustrated in Fig. 2(a), AD publications have experienced a dramatic increase over the years 2000–2015 (from 95 in 2000 to 636 in 2015), and nearly all of the annual increments have been positive (the maximum number of 144 was found in 2013) (Fig. 2(a)). These data were based on literature searches in the Thomson Reuters “Web of Science” database with the key words “anaerobic digestion” and “sludge”. Furthermore, through the analysis of all the AD research articles from the year 2000 to 2015, we found that China had the highest counts (773, 17.9%), followed by the USA (489, 11.3%) and Spain (338, 9.0%) (Fig. 2(b)). Obviously, China attaches great importance to the use of AD for sludge treatment.
image file: c6ra06868e-f1.tif
Fig. 1 Sludge production across provinces of China in 2014.

image file: c6ra06868e-f2.tif
Fig. 2 (a) Time course for publications on sludge anaerobic digestion from the year 2000 to 2015, and (b) overview of the top 10 most productive countries for sludge anaerobic digestion research from the year 2000 to 2015.

Biogas, which has a caloric value of 21–25 MJ m−3, can be used as vehicle fuel or as a fuel to generate heat and electricity; thus, biogas use can lead to reductions in the use of fossil fuels and lower CO2 emissions to the atmosphere.6 On the basis of chemical properties (H/C ratio) and other intrinsic properties of biogas fuel, Bordelanne et al.7 estimated that reductions of over 80% in greenhouse gas emissions could be achieved by using the biomethane from AD systems instead of gasoline. However, during the AD process, changes in the pH, temperature, sludge concentration and composition, liquid dynamics, and ionic strength, as well as tank configurations and methods of operation can cause fluctuations in the amount of biogas generated from excess sludge.8–17 Chen et al.8 found that the initial pH had a strong influence on biogas production through its effects on the hydrolysis and acidification stages during the sludge AD process. The maximum methane yield (224 mL CH4/g VS) was achieved at pH = 9. Moreover, the findings showed that the sludge AD process could be accelerated under proper temperature conditions; soluble chemical oxygen demand (SCOD) yield was usually used to evaluate the efficiency of AD process. Yang et al.9 found that the highest SCOD yield (4407 ± 80 mg L−1) was obtained at 60 °C, which was 20-fold higher than that at 75 °C. Recently, sludge concentration was the focus of an international study, especially high sludge concentration AD. Prabhu and Mutnuri10 compared the performance of high sludge concentration AD (10% < total solid (TS)) with conventional low sludge concentration AD. In their study, high sludge concentration AD achieved highly efficient volatile solid reduction and high CH4 production because of more numerous total archaea (dominated by Methanosarcina) in a high sludge anaerobic reactor, which enhanced the sludge AD process. However, when the sludge concentration was increased further, the efficiency of sludge AD never increased; it actually decreased because of increasingly bad liquid dynamics.11 Solon et al.12 reported that the ionic strength could affected AD performance because of significant physico-chemical effects including ion pairing of inorganic and organic substrates with different cations (such as K+ and Na+) and anions (such as Cl). Wang et al.13 reviewed tank configurations (such as continuous stirred tank reactor (CSTR), anaerobic sequencing batch reactor (ASBR), up-flow anaerobic sludge blanket (UASB), expanded granular sludge bed (EGSB), anaerobic packed bed (APB), anaerobic fluidized-bed (AFBR) and hybrid bio-reactor (HBR) configurations) and found that the configuration could influence the AD process performance for different compositions of sludge and operating conditions. Based on specific AD processes, a traditional single-stage process and two-stage process were both developed. The two-stage system was found to both improve the stability and performance of the AD process, and it had a 21% higher specific methane yield than the single-stage system.14

In order to better understand the mechanisms involved in sludge AD processes, and to provide information that can help to optimize and predict biogas production, this review discusses AD processes in terms of microbial dynamics, scientific models, and reinforcement strategies (i.e., sludge pre-treatment, bio-electrochemical and Fe/Fe3O4 technologies). In sludge pre-treatment technologies, the high-efficiency and available focused-pulsed method can promote cell lysis; its mechanisms of cell lysis and specific treatment chamber designs are discussed in detail. State-of-the-art sludge pre-treatment technology, the microbial electrolysis cell (MEC) method, and the mechanisms involved in AD-MEC systems are also introduced in the reinforcement strategies section. Furthermore, because increasing amounts of engineered nanomaterials are being released into wastewater treatment plants, the potential impact of nanomaterials, especially low doses of Fe/Fe3O4 nanomaterials (≤0.5% (w/w)), on sludge AD processes and the associated mechanisms are also described in this review. In addition, we also review new technology, namely, anaerobic methane oxidation technology, which can be used to treat dissolved methane in the AD digestate and prevent it from becoming a threat to the environment.

2 Microbial dynamics and scientific models

The energy derived from sludge AD technology in WWTPs has been used to discount up to 33–100% of the electricity costs in some developed countries.18 To provide information that can be used to improve the operational efficiency, the microbial dynamics and scientific models of sludge AD processes are reviewed here.

2.1 Microbial dynamics

The overall sludge AD process involves several co-dependent biological stages (e.g., hydrolysis, acidogenesis, acetogenesis, and methanogenesis) where biodegradable organics are broken down by different kinds of microbes in an air-tight digester vessel; the final products include biogas (about 40–70% CH4 and 30–60% CO2) and residual compounds (AD digestate), which are rich in nutrients and may contain some dissolved CH4 if the recommended sludge retention time (SRT) values of 10–20 days and temperatures of 0–80 °C are applied (Fig. 3).19–21 In the hydrolysis stage, complex polymeric organics are broken down to simple organic monomers such as sugars, amino acids, and fatty acids with extracellular enzymes from hydrolytic bacteria. Subsequently, in the acidogenesis stage, these hydrolytic products are fermented by acid-forming bacteria into products such as propionic acid, butyric acid, acetic acid, ethanol, H2, and CO2. The products acetic acid, H2, and CO2 are then directly in utilized in the fourth stage of the process, whereas the other products from the second stage undergo acetogenesis, whereby more acetic acid, H2, and CO2 are produced. Finally, in the methanogenesis stage, methane-forming bacteria such as acetotrophic and hydrogenotrophic methanogens use the acetate, H2, or CO2 to produce methane.22,23
image file: c6ra06868e-f3.tif
Fig. 3 Sludge anaerobic digestion biochemical pathways for biogas (SRT: sludge residence time).

Methanogens are strictly anaerobic prokaryotes.24–27 There are three metabolic pathways for methanogenesis, and these include the production of methane from carbon dioxide and hydrogen, methanol and hydrogen, and acetate and water, as shown below (eqn (1)–(3)):28,29

Methanogenesis from CO2 + H2

 
4H2 + H2CO3 → CH4↑ + 3H2O (1)

Methanol to CH4

 
CH3OH + H2 → CH4↑ + 3H2O (2)

Acetate to CH4

 
CH3COOH + H2O → CH4↑ + 3H2CO3 (3)

The pathway involving methanol to CH4 is rare and only occurs under specific environments.29 Theoretically, about two-thirds of the total methane production is generated by the pathway involving acetate to CH4 and the remainder is produced via the CO2 + H2 to CH4 pathway in typical AD systems.30 Temperature plays a key role in AD processes, and these processes are usually classified into psychrophilic (<25 °C), mesophilic (25–40 °C), thermophilic (40–60 °C), and hyperthermophilic (60–80 °C) conditions,31–33 which can be used to regulate the pathways of methanogenesis. Under psychrophilic conditions, the pathway involving acetate to CH4 is dominant; the acetate to CH4 and CO2 + H2 to CH4 pathways both occur at a certain proportion during the AD process under mesophilic conditions, while methane is exclusively generated by methanogenesis from CO2 + H2 under thermophilic and hyperthermophilic conditions.34–36

2.2 Scientific models

Modeling is a useful tool to assess AD process performance, and it can be further used to determine the optimal operating conditions.37 In an attempt to account for the various types of microorganisms involved, the formation of complex intermediate products, differences in the sludge's intrinsic biodegradability, and other types of process influencing factors during the sludge AD process, the ADM1 (Anaerobic Digestion Model No. 1) was formally introduced in March 2002.38 Since that time, great progress has been made in terms of ADM1 modifications. The ADM1 consists of two main reaction types including biochemical reactions (disintegration of complex organics, extracellular hydrolysis, and biomass growth and decay) and physico-chemical reactions (ion association/dissociation and gas–liquid transfers),39 and this work is summarized in Table 1. However, because operating conditions such as the temperature (mesophilic, thermophilic,40 and psychrophilic41) and the presence of high-solid contents (total solid > 10%),42 inhibitors (e.g., Na+,43 NH3,44 PO43+,45 and ionic strength46), and promotion materials (e.g., Fe47) can critically impact biochemical reactions, the related parameter values should be revised to improve the ADM1; furthermore, some additional relevant kinetic equations should be added into ADM1. Presently, the lack of a comprehensive understanding of some processes such as anion reduction (NO3 and SO42−) and the formation and emission of odorous compounds limits the ability of the ADM1 to predict the outcomes accurately, but new research is being conducted to elucidate the related process mechanisms and evaluate their effect on fermentation and methanogenesis.48–50 Moreover, operational modes such as the use of co-digestion and two-stage systems, which can also affect the AD process, are being investigated. Sludge co-digestion with carbon-rich wastes, which has been shown to be economically viable and feasible with AD systems, can realize “1 + 1 > 2” effects in regard to biogas production.56 However, disintegration and conversion of more complex feed materials into carbohydrates, proteins, and lipids can occur by mechanisms that are different from the conventional digestion pathways, and these related reaction pathways need to be re-modeled with proper kinetics.51 Two-stage AD systems, which are stable, reliable, and effective systems, consists of two independent phases (i.e., acetogenesis and methanogenesis), and work has been done to optimize the operational conditions in such systems;57 thus, a two-stage model would be useful for making improvements in these systems.52 While activated sludge models (ASMs) such as the ASM1, ASM2d, and ASM3 can be used to evaluate the efficiency of WWTPs without considering details of the AD process,58 overall, performance assessments of WWTPs, especially in regard to effluent quality and biogas production, would benefit greatly from application of coupled ASMs including the ADM1; defining and modeling the interfaces is the difficult part.53–55
Table 1 Related contributions and advantages for modified ADM1 proceduresa
Modified ADM1 Related contributions Advantages Reference
a ADM1: Anaerobic Digestion Model No. 1, HT/MT: mesophilic/thermophilic, PS: psychrophilic, HTS: high total solid, Na: sodium, NH3: ammonia, P: orthophosphate, IS: ionic strength, Fe/ZVI: zero-valent iron, NR: nitrate reduction, SR: sulfate reduction, OC: odorous compounds, CD: co-digestion, TS: two-stage digestion, ASM1: Activated Sludge Model No. 1, ASM2d: Activated Sludge Model No. 2d, ASM3: Activated Sludge Model No. 3, VFAs: volatile fatty acids, SBR: sequencing batch reactor, LBPs: less biodegradable pollutants, CBIM: continuity-based interfacing method, MCN: maximizing the total COD and nitrogen contents, COD: chemical oxygen demand.
HT/MT-ADM1 Studied the dynamic behavior in mesophilic and thermophilic digestion Provided the accurate kinetic values for ADM1 under mesophilic and thermophilic conditions 40
PS-ADM1 Omitted the hydrolysis step Simplified ADM1 41
Studied the dynamic behavior in psychrophilic digestion Provided the accurate kinetic values for ADM1 under psychrophilic conditions
HTS-ADM1 Studied acetate degradation kinetics during digestion of high-solid contents Calibrated the kinetic parameters related to acetate uptake 42
Na-ADM1 Added a sodium inhibition function into ADM1 Considered the effect of sodium on acetoclastic methanogens and biogas production and composition 43
NH3-ADM1 Implemented the alternate acetate oxidizing mechanism instead of the standard acetoclastic pathway Led to more accurate pH predictions 44
Described metabolic activity of un-ionized species, undissociated acetic acid and un-ionized NH3 used as inhibitors
P-ADM1 Comparison of three modifications of ADM1 with the orthophosphate influence Introduced an orthophosphate inhibition kinetics equation 45
Provided insight into the orthophosphate influence on fatty acid dynamics and methane production
IS-ADM1 Davies approach applied instead of molar concentrations for consideration of ionic strength chemical activities Described impact of the ionic strength on physico-chemical reactions, pH, and biogas 46
Fe-ADM1 Integrated three new processes (electron release and H2 formation from ZVI corrosion, and transformation of LBPs) into ADM1 Described performance of the ZVI-based anaerobic system 47
NR-ADM1 Added nitrate reduction processes into ADM1 Elucidated the effect of nitrate reduction processes on fermentation and methanogenesis 48
SR-ADM1 Added sulfate reducing processes into ADM1 Provided new understanding of substrate competition in anaerobic systems 49
OC-ADM1 Developed a mathematical model based upon ADM1 for the formation and emission of odorous compounds Gave the general mechanisms for the formation of common odorous sulfur compounds such as CH4S, C2H6S, and H2S, as well as VFAs and NH3 50
CD-ADM1 Added surface-based kinetics (modeling of complex organics disintegration and conversion to carbohydrates, proteins, and lipids) into ADM1 Found that the kinetic constant of the disintegration process just depends on the nature and composition of biomass in co-digestion reactors 51
TS-ADM1 Presented a process model for an anaerobic two-stage digestion system Predicted the dynamic behavior of a two-stage digestion process 52
ASM1-ADM1 Connected ASM1 and ADM1 with the CBIM and MCN methods, respectively Evaluated plant-wide control systems and operating strategies 53
ASM2d-ADM1 Combined ASM2d with ADM1 with an interface definition (ADM1 was used to calculate the anaerobic treatment phases and ASM2d was used to calculate the aerobic phases) Optimized performance of WWTPs 54
Predicted the SBR potential for detoxification and COD removal
ASM3-ADM1 Coupled ASM3 with ADM1 Optimized performance of WWTPs so that they could meet stricter effluent discharge criteria, especially in regard to N removal 55


3 Strategies for improving anaerobic digestion

Sludge AD aimed at the production of methane-rich biogas is in widespread use at WWTPs nowadays. However, one major limitation of the AD process is that it cannot efficiently extract the energy from sludge. Productivity of sludge AD systems can be improved with efficient and available strengthening strategies such as pre-treatment technology (especially focused-pulsed technology), bio-electrochemical techniques, and Fe/Fe3O4 nanomaterial methods.

3.1 Focused-pulsed technology

During the AD process, microbial cells can account for 70% of the sludge.59 Therefore, extracellular polymeric substances and microbial cell walls in the sludge can seriously hinder the release and hydrolysis of intracellular organic matter. Poor sludge AD performance with long SRT values (20–50 days) and low organic degradation efficiencies (20–50%)21 can be dramatically improved by pre-treatment technologies, which mainly involve the application of physical, chemical, and biological methods. Physical methods that have been used include stirred ball mills,60 high-pressure homogenization techniques,61 Venturi tubes,62 ultrasonic systems,63 freeze/thaw cycles,64 and focused-pulsed methods.65 Chemical methods that have been used include ozonation,66 Fenton reactions,67 and biological methods such as enzyme68 and enhanced biological techniques;69 these pre-treatment methods can increase sludge pyrolysis, which is a limiting step for methanogenesis, through cell lysis. Cell lysis can be initiated by mechanical forces with physical methods (Fig. 4(a)) and by chemical molecules or enzymes with chemical and biological methods, respectively (Fig. 4(b)). Out of all of these pre-treatment methods, the optimal technology is focused-pulsed pre-treatment because of its outstanding performance, low cost, simple operation procedures, use of technology that is easy to retrofit, and application of a physical/non-chemical process.70,71
image file: c6ra06868e-f4.tif
Fig. 4 (a) Mechanism of cell lysis initiated by mechanical force during application of the physical method and (b) mechanism of cell lysis initiated by chemical molecules or enzymes during application of the chemical and biological methods, respectively.

In both bench- and pilot-scale studies, focused-pulsed technology has been shown to effectively disrupt and break up sludge flocs and cells, which leads to an obvious increase in SCOD (Table 2). Sludge biodegradation is also greatly improved (larger khyd values and fermentation efficiencies) with this technology, and greater biogas production can be achieved. Banaszak et al.72 investigated the effect of focused-pulsed technology on improvements in excess sludge biodegradability in a full-scale WWTP (capacity 7.57 × 104 m3 d−1), and a 60% increase in biogas production and a 40% reduction in sludge disposal amounts were obtained, which resulted in a net profit of 5.4 × 105 dollars. Zhang et al.75 reported that focused-pulsed pre-treatment not only made the sludge more biodegradable, but it also increased bacterial diversity and the relative abundance of acetoclastic methanogens in the digestate, and this in turn led to very low acetate concentrations in the AD system effluent. Salerno et al.78 studied the effect of some factors such as the applied voltage, pulse duration, pulse frequency, sludge conductivity, length of the treatment chamber, and sludge residence time in the treatment chamber on sludge biodegradability and defined the treatment intensity as follows:

 
image file: c6ra06868e-t1.tif(4)
where η is the treatment intensity, V is the applied voltage, D is the pulse duration, f is the pulse frequency, σ is the sludge conductivity, T is the sludge residence time in the treatment chamber, L is the length of the treatment chamber, and k is a constant.

Table 2 Effect of focused-pulsed pre-treatment on sludge in bench- and pilot-scale studiesa
Research size Organic sources Operation results Reference
a SCOD: dissolved chemical oxygen demand, khyd: first-order rate constant for hydrolysis, TCOD: total chemical oxygen demand.
Pilot-scale Excess sludge 158% increase in SCOD 72
60% increase in biogas production
40% reduction in disposal sludge
Pilot-scale Primary + excess sludge 160% increase in SCOD 73
60% increase in biogas production
40% reduction in disposal sludge
Pilot-scale Primary + excess sludge 32% increase in biogas production 74
17% reduction in disposal sludge
Pilot-scale Primary + excess sludge 30–40% increase in biogas production 75
Bench-scale Excess sludge 220% increase in SCODa 76
40% reduction in reactor the size
33% increase in biogas production
21% increase in khyd
18% increase in TCOD removal
Bench-scale Excess sludge 4.5-fold increase in SCOD/TCOD 77
2.5-fold increase in biogas production
Bench-scale Excess sludge 1-fold increase in biogas production 78
Bench-scale Excess sludge 2.6-fold increase in SCOD 79
Increase in fermentation efficiency by about 23%


Recently, a new study showed that focused-pulsed pre-treatment can also remove siloxane in sludge prior to its entry into biogas production.80 Siloxane is a problematic material because of its abrasive, non-thermal, and non-electrical properties, and it can seriously damage the heat exchangers and gas engines during the use of biogas.81

In addition, focused-pulsed technology has the potential to make sludge into a viable electron donor that can drive denitrification processes82 or MECs.79 Lee et al.82 found a 26-fold increase in the sludge's semi-soluble chemical oxygen demand (SSCOD) after focused-pulsed treatment, and the maximum denitrification rate was improved by 5-fold. Ki et al.79 reported that the accumulation of desirable volatile fatty acids (VFAs) and acetate could be enhanced by 2.6-fold when focused-pulsed treatment was applied. Correspondingly, there was a 2.4-fold increase in the maximum current density in the MECs compared to MECs prepared without the use of focused-pulsed treated sludge.

3.1.1 Mechanisms of cell lysis. Focused-pulsed technology improves sludge biodegradability mainly by disrupting the cell membranes of cells within the sludge and releasing intracellular organic matter. Generally, two main theories (i.e., electroporation and shockwave) can describe this process.

The sludge cell lysis that occurs when cells are exposed to an electric field is mainly caused by the electromechanical instability of cell membranes. Cell membranes can maintain an effective osmotic boundary and protect microorganisms from surrounding environmental conditions; however, when exposed to an electric field, lipid membranes become charged up and are broken down if the transmembrane potential (TMP) developed across the cell membrane reaches about 1 V.83 For exposure to an external electric field, Sale and Hamilton84 established the following equation to describe the TMP:

 
V(t) = 1.5rE (5)
where V(t) is the transmembrane potential with the same direction of applied electric field strength, r is the sludge cell radius, and E is the applied electric field strength. The normal TMP is about 10 mV because of the dielectric constant difference between dielectric materials in the cell membrane and the environment. When cells are exposed to an electric field, an increase in the TMP can be achieved (eqn (5)). This increase of TMP can lead to a reduction in the cell membrane thickness because the electro-compressive force increases. When moderate electric fields are applied, the viscoelastic restoring force formed by the decreases in cell membrane thickness can oppose the electro-compressive force, but when E increases to a sufficient extent, large pores will form in the cell membrane and an irreversible breakdown occurs. This ultimately results in cell lysis.

After a rapid discharge of high-voltage electricity from an electrode submerged in the liquid medium, tremendously high transient pressure pulses can develop, which produce shockwaves (∼1 × 103 bars).85 These strong shockwaves can cause mechanical oscillations in the cell membranes, which act in concert with the process of electroporation to more effectively breakdown sludge cells.

3.1.2 Treatment chamber design. Treatment chamber design can seriously affect electric fields and the different levels of field uniformity.86 In recent years, optimization of the geometry of focused-pulsed treatment chambers has been studied, and designs such as parallel plates and co-axial and co-linear shapes have been modeled with computational fluid dynamic (CFD) equations.87 A uniform electric field distribution can be obtained by parallel plate electrode chambers, but unfortunately, the electric field was not found to be strong at the section of the edges of the electrodes because of the limited sizes. Zang et al.88 improved the geometry of the parallel plate electrode chamber design by adding baffled flow channels. The co-axial cylindrical configuration can generate a well-defined electric field distribution, but undesirable dielectric breakdown often occurs at strong electric field enhancement points. Qin et al.89 optimized the shape of the co-axial cylinder arrangement with numerical electric field computations and by adding a cooling system to overcome the above mentioned disadvantage. By using a similar numerical approach, Meneses et al.90 developed a better co-linear chamber structure through considering the treatment parameter electric field strength distribution.

In addition, electrochemical reactions often occur in the treatment chamber, and this can result in corrosion of the electrode.91 To avoid electrode corrosion, Góngora-Nieto et al.92 introduced a new electrode material with a much higher resistance value against corrosion. Morren et al.91 reported that the use of short pulses could also help to prevent the corrosion.

3.2 Bio-electrochemical technology

Many studies of bio-electrochemical technologies have focused on the interactions between microbial cells and electrodes. Of these technologies, MECs driven by exoelectrogenic bacteria with a small applied voltage (0.2–0.8 V) are commonly used in AD biogas production systems; this voltage is far less than that typically required for water electrolysis (1.8–2.0 V). Chemical production rather than power generation maybe a much more appealing way of utilizing MEC technology because of the current technological implementation limitations and challenges.93 Studies have shown that a pair of electrodes placed into an AD system to form a larger AD-MEC system can accelerate sludge hydrolysis and increase methane production.94–96 Generally, system designers can choose between the use of two different reactor structures for MEC operations (i.e., single-chamber and two-chamber structures). In two-chamber MECs, the membrane is added between the anode and cathode, thus forming two separate chambers. The membrane materials applied in MECs included proton-exchange membranes (PEMs), cation-exchange membranes (CEMs), and anion-exchange membranes (AEMs).97 Although the two-chamber structure better protects the cathode from hazardous substances and reduces the anode consumption during hydrogen diffusion, use of this structure can cause a pH gradient in the two separate chambers, which will not only result in bacterial inactivation, but also increase the reactor resistance and thus produce low current densities, reduced biogas production efficiencies, and high energy consumption rates.98 Therefore, many studies of MECs have been carried out with a single-chamber structure. Chen et al.99 found that methane production was increased by 76.2% with a 26.6% reduction in volatile suspended solids (VSSs) in an AD-MEC system compared to a single AD system. Guo et al.100 reported that a significant increase (11.4–13.6-fold) in methane production could be obtained if MEC systems were incorporated into the sludge AD process. An MEC system can accelerate the hydrolysis of sludge flocs, enhance the transformation of SCOD, improve the conversion of VFAs, and maintain an optimal pH range for methane-producing activity. Asztalos and Kim101 found that VSS and chemical oxygen demand (COD) removal in an AD-MEC system was continuously higher by 5–10% than a similar AD system that did not employ MECs under ambient temperatures; this is equivalent to what can be achieved with common AD under mesophilic temperatures. This study indicated that MECs could represent a promising technology for boosting the efficiencies of AD processes under ambient temperatures. Generally, higher methane contents in biogas can be generated by using AD-MEC systems. Song et al.102 reported that an AD-MEC system that applied 0.3 V between the anode and cathode could generate biogas with a methane content of 76.9%, which is obviously higher than the contents produced by common AD systems. They also found that some operating parameters such as the pH and alkalinity were quite stable and suitable for methane growth, which suggests that MECs can enhance the stability of the AD process. Cusick et al.103 reported that the applied voltage could be supported by renewable energy sources, such as solar energy and salinity-gradient energy, which would improve the productivity of AD-MEC systems further.

In addition, MECs have shown great future potential to recover residual energy in AD digesters, and they can minimize environmental impacts because organic matter in the effluent is being changed to less harmful forms. Kondaveeti and Min104 demonstrated the feasibility of producing (i) biofuels such as butanol, ethanol, and propanol and (ii) biogases such as hydrogen and methane from anaerobic digestion effluents via MECs. Clauwaert et al.105 discussed how MECs were promising technologies for generating methane from AD digesters based on their analysis of experimental data.

3.2.1 Mechanisms of AD-MEC systems. Sludge consists mostly of proteins and carbohydrates, and sludge hydrolysis during the AD process is commonly considered as the limiting step for methane production. On the basis of many studies, mechanisms of MECs that can accelerate sludge hydrolysis and enhance methane production during the AD process have been elucidated (Fig. 5).101,106–109 In AD-MEC systems, organic matter such as VFAs, alcohols, glucose, phenols, and benzene110 are first oxidized by anodic exoelectrogenic bacteria like Shewanella and Geobacter species that play a crucial role in the electron transportation between the anode and cathode; these species breakdown the sludge flocs and cell membranes into simple organics by decomposing proteins and carbohydrates, and then, the sludge hydrolysis efficiency is accelerated.109 Exoelectrogenic bacteria and anodes with bacteria growing on them are referred to as bio-catalysts and bio-anodes, respectively (Fig. 5).
image file: c6ra06868e-f5.tif
Fig. 5 Mechanisms of microbial electrolysis cell-based accelerated sludge hydrolysis and increased methane production during anaerobic digestion (AM: acetoclastic methanogenesis, HM: hydrogenotrophic methanogens, MN: Methanosarcina, MT: Methanosaeta, GB: Geobacter, DIET: direct interspecies electron transfer, VFAs: volatile fatty acids).

The pathways used by MECs for increasing biogas production mainly include four different styles (Fig. 5). Of these four styles, pathway I and pathway II are common mechanisms. When MECs are added to the AD process, acetate and CO2 production efficiency is notably increased by the bio-catalysts. At the same time, H2 production can be enhanced by the electrons from the processes involving exoelectrogenic bacteria such as Shewanella and Geobacter spp., the decomposition of organic matter, and H+ reactions in the mixed solution. Therefore, acetate to CH4 and methanogenesis from CO2 + H2 are both enhanced as a result of the presence of adequate substrate. Sun et al.107 reported that pathway II involving methanogenesis from CO2 + H2 is prominent in AD-MEC systems, and this pathway accounted for 96.01% of the methane generation in their analysis. They also found that exoelectrogenic Geobacter and hydrogen-producing Petrimonas bacteria were the dominant species involved, according to pyrosequencing data. Pathways III and IV are both new mechanisms for increasing methane production in AD-MEC systems. Pathway III involves the situation where the methane is formed by direct cathodic reduction of H2 (eqn (6)),95 which is also referred to as electromethanogenesis:

 
CO2 + 8H+ = CH4↑ + 2H2O E0 ≈ −0.44 [vs. the standard hydrogen electrode (SHE) potential] (6)

Geobacter metallireducens can build a direct electron connection with Methanosaeta and Methanosarcina (i.e., through direct interspecies electron transfer, hereafter referred to as DIET) during the production of methane, and this is referred to as pathway IV.28,111 Zhao et al.108 found that DIET (pathway IV) played a more important role in the AD process compared to electromethanogenesis (pathway III), which just accounted for a small part of the methane production. In their study, a 12.9% increase in methane production and a 17.2% reduction in sludge production were obtained in AD-MECs.

3.2.2 Influencing factors of AD-MEC systems. In order to further increase AD biogas production with MEC technology, some factors such as the electrode material and structure,112,113 additions of ferric iron (Fe3+),114 the hydraulic retention time (HRT),102 and applied voltage115 have been explored in depth.

Electrode material and its structure are important factors for the electron exchange between the cathode and microbial cells. Villano et al.112 used anode materials with potentials between +200 mV and −200 mV (versus the SHE potential) to study the effect of anode materials on AD-MEC system performance. Anodes associated with high acetate removal rates and cathodes associated with high methane generation rates both had potentials under +200 mV. However, the sum of the internal potential losses decreased by 52.7% when the anode potential was decreased from +200 mV to −200 mV, which indicates that cathode overpotentials can reduce methane production. The results also clearly show that anode potentials need to be optimized to satisfy both rapid substrate oxidation rates and high energy efficiencies. The optimal electrode potentials for AD-MECs are still under investigation. Zhen et al.113 improved a bio-anode by using a layer of graphite felt (GF) on a plain carbon stick. The modified bio-cathode was found to enhance the microbial electrocatalysis activity by affording abundant space for exoelectrogenic bacteria growth, and thus, increases in methane production were mainly due to obvious reductions in the cathode overpotential. This study revealed that GF has the substantial potential to upgrade the electromethanogenesis efficiency, which can be attributed to its open structure and high conductivity. Addition of Fe3+ has been found to significantly enhance electron exchange through the effect of microbial Fe3+ reduction, which further accelerates sludge fermentation.114 Zhang et al.115 found that COD removal could be stably maintained at 88.3% by adding Fe3+ into an AD-MEC system, and this value decreased by 5.9% after the applied voltage (0.8 V) was cut off; the study results also indicated that the addition of Fe(III) could enhance anodic oxidation, which was confirmed by pyrosequencing and denaturing gradient gel electrophoresis (DGGE) analyses that showed that richer bacteria and archaea communities were attached on the anode compared to those in other common AD systems. Slow sludge hydrolysis rates resulted in long HRTs for the AD process. Song et al.102 studied AD-MEC system performance at different HRTs (5 to 20 days), and the VSS removal rate and methane content in the biogas slightly changed as the HRT decreased from 20 to 5 days. The methane recovery efficiency was 69.1–98.7%, and the maximum energy efficiency appeared in under 10 days, which suggests that short HRTs may be profitable for AD-MECs. Feng et al.116 reported that 22.4% and 11% increases in methane production and VSS removal rates, respectively, could be obtained under 0.3 V. However, the methane production decreased and hydrogen appeared in the cathode as the applied voltage increased to 0.6 V, apparently because the excessive utilization of H+ in the cathode resulted in a highly alkaline pH that seriously inhibited the methanogens. Moreover, DGGE analysis revealed that archaea and bacteria were both bio-augmented under a low applied voltage (0.3 V), which might be useful information for future improvements in the sludge hydrolysis and methane production processes.

In addition, because of the remaining lack of knowledge in this field, the effects of the design parameters such as electrode size and electrode arrangement, the operational parameters such as temperature, organic loading rate, and pH, and toxicants including organic toxicants and inorganic toxicants on the AD-MECs efficiency and the associated mechanisms should be widely investigated in future research.

3.3 Fe/Fe3O4 nanomaterials

Over the past several years, many kinds of nanomaterials have been introduced in commercial consumer products, and these materials have a high probability of being released to WWTPs. The impact of nanomaterials on the AD process is presently a hot topic. Studies that have been conducted to date have found that nanomaterials such as Ag, ZnO, TiO2, CuO, and CeO2 can seriously inhibit the AD process because they are toxic to AD microorganisms.117–119 Nanomaterials often inactivate AD microorganisms through multidimensional effects, which may include direct contact effects (i.e., breaking down of cellular membranes, thereby resulting in losses of membrane-bound enzyme activity) and indirect damage caused by reactive oxygen species (ROS) that are generated by nanomaterials in aqueous solutions; ROS are highly reactive and can damage the double bonds on membrane phospholipids, which will lead to adverse increases in membrane fluidity and membrane permeability.120–122 However, some studies have shown that relatively low doses of nanoscale Fe/Fe3O4 (≤0.5% (w/w)) material could accelerate sludge hydrolysis, thus improving biogas production (30.4–56.0%) and increasing the methane content (5.1–13.2%); this specific type of nanomaterial was also associated with the production of more stabilized sludge and reduced odors during the AD process.123–126 While Fe/Fe3O4 nanomaterials can generate toxic substances such as ROS, this may actually help to improve the sludge biodegradability by breaking down sludge cells.127 In addition, Fe/Fe3O4 nanomaterials are unstable and can generate Fe2+, Fe3+, and especially H2, which may have a beneficial effect on the sludge AD process.128,129 Specifically, H2 generated from Fe nanomaterials can ensure that adequate substrate concentrations are present for methanogenesis from CO2 + H2 (ref. 128) and this may increase the utilization of CO2;130 furthermore, electrons from Fe nanomaterials can form effective DIET relationships between fermentative bacteria and methanogens, which may enhance the methane production rate.129 Viggi et al.131 found that Fe3O4 nanomaterials could indeed enhance DIET between fermentative bacteria and methanogens by acting as electron conduits. Therefore, a metallic iron core could potentially be used as slow-release source for active ions, electron donors, and electron conduits to accelerate methanogenic activity and boost biogas production. Moreover, in the AD process, H2S is formed during the dissimilatory reduction of sulfate by sulfate reducing bacteria (SRB), and this is the main odor source in AD systems. Since active compounds such as Fe2+ and Fe3+ can convert H2S to precipitates such as FeS, FeS2, and S, the addition of Fe/Fe3O4 nanomaterials may be able to greatly reduce the odors associated with the AD process.132 Meanwhile, Fe2+/Fe3+ can also remove the phosphorus that is formed during the degradation of organic matter, as it will form precipitates such as Fe3+-hydroxy-P and Fe3(PO4)2.131 All of the potential behaviors of Fe/Fe3O4 nanomaterials in sludge during the AD process are shown clearly in Fig. 6. Because of the above mentioned potential advantages, use of Fe/Fe3O4 nanomaterials should be considered in the design of an economically and environmentally sustainable method for improving biogas production.
image file: c6ra06868e-f6.tif
Fig. 6 All of the behaviors of Fe/Fe3O4 nanomaterials during the sludge anaerobic digestion process (ROS: reactive oxygen species, SRB: sulfate reducing bacteria, Fe/Fe3O4 NPs: Fe/Fe3O4 nano-particles).

4 Removal of dissolved methane

Dissolved methane in anaerobic digestion effluents has great potential to cause environmental harm because it is 25-fold more effective in trapping heat than CO2 in the atmosphere.133 Therefore, dissolved methane must be removed before the anaerobic digestate is discharged. Some alternatives such as biogas micro-aeration,134 de-gassing membranes,135,136 and increased liquid turbulence137 technologies have been proposed for the purpose of removing dissolved methane from effluents. However, none of these technologies have yet been proven to be readily available and economical. Recently, some researchers studied the removal of dissolved methane from anaerobic digestate by coupling the technique with anaerobic methane oxidation (AMO), and the results showed great potential for application of the technology as an AD post-treatment process.

4.1 AMO

Anaerobic methane oxidation, which was first discovered in 1976, efficiently controls atmospheric CH4 efflux by consuming more than an estimated 90% of all the CH4 generated on Earth;138–140 this process and the responsible microorganisms, which are anaerobic methanotrophic archaea (ANME), were identified after about 20 years of research.141 The ANME consist of three distinct clusters with subgroups including ANME-1 (two subgroups: ANME-1a and ANME-1b), ANME-2 (four distinct subgroups: ANME-2a, ANME-2b, ANME-2c, and ANME-2d), and ANME-3.142–146 So far, depending on the differences in the electron acceptors, AMO models can be categorized into the following four different types: cooperative/un-cooperative SO42− dependent anaerobic methane oxidation (CS/US-DAMO) (Fig. 7(I) and (II)), NO2/NO3 dependent anaerobic methane oxidation (Nx-DAMO) (Fig. 7(III)), metal ion (Fe3+ and Mn4+) dependent anaerobic methane oxidation (MIs-DAMO) (Fig. 7(IV)), and organic (humic acids) dependent anaerobic methane oxidation (O-DAMO) (Fig. 7(V)). The ANME-1, ANME-2, and ANME-3 archaea can all work with SRB to catalyze CS-DAMO (Fig. 7(I)); the SRB involved are Desulfosarcina, Desulfococcus, and Desulfobulbus.147 The partner bacteria Desulfosarcina and Desulfococcus often cooperate with ANME-1 and ANME-2 to oxidize CH4, while Desulfobulbus works with ANME-3 (eqn (7)).148,149 Furthermore, some studies show that ANME-2 and ANME-3 can also work without SRB present (Fig. 7(II))150,151 as follows (eqn (7) and (8)):
 
CH4(aq) + SO42− → HCO3 + HS + H2O ΔG = −34 kJ mol−1 CH4 (7)
 
7CH4(aq) + 8SO42− + 5H+ → 7HCO3 + 4H2S + 11H2O ΔG = −26.5 kJ mol−1 CH4 (8)

image file: c6ra06868e-f7.tif
Fig. 7 Modes of microbial anaerobic methane oxidation (AMO) depending on the different electron acceptors: (I) and (II) cooperative/un-cooperative SO42− dependent anaerobic methane oxidation (CS/US-DAMO), (III) NO2/NO3 dependent anaerobic methane oxidation (Nx-DAMO), (IV) metal ions (such as Fe3+ and Mn4+) dependent anaerobic methane oxidation (MIs-DAMO), and (V) organics dependent anaerobic methane oxidation (O-DAMO). ANME: anaerobic methanotrophic archaea (including three distinct clusters, i.e., ANME-1, ANME-2, and ANME-3), ANME-2d: a subgroup of ANME-2, SRB: sulfate reducing bacteria as partner bacteria for ANME, HAs: humic acids.

In the Nx-DAMO model, besides archaea (such as ANME-2d using NO3)152 (eqn (9)), the NC10 bacteria may be also capable of independent methane oxidation by using NO2 (eqn (10)); the relevant reactions are as follows:153

 
CH4(aq) + 4NO3 → CO2 + 4NO2 + 2H2O ΔG = −503 kJ mol−1 CH4 (9)
 
3CH4(aq) + 8NO2 → 3CO2 + 4N2 + 10H2O ΔG = −928 kJ mol−1 CH4 (10)

In 2009, Beal et al.154 reported that some metal ions such as Fe3+ and Mn4+ can also be used as electron acceptors by ANME-1 and ANME-3 (eqn (11) and (12)); however, the partner bacteria remain unknown (Fig. 7(IV)):

 
3CH4(aq) + 8Fe(OH)3 + 15H+ → HCO3 + 8Fe2+ + 21H2O ΔG = −270.3 kJ mol−1 CH (11)
 
3CH4(aq) + 4MnO2 + 7H+ → HCO3 + 4Mn2+ + 5H2O ΔG = −556 kJ mol−1 CH4 (12)

In addition to the use of the above inorganics as electron acceptors, Gupta et al.155 found that some organics (such as humic acids) can be used as electron acceptors. Unfortunately, the details of this mechanism require further study (i.e., to confirm the involvement of ANME) (Fig. 7(V)).

4.2 Coupled AMO

The research related to AD post-treatment technology coupled to AMO is just at the starting stage, but it has a bright future for application in systems that can remove dissolved methane because of its environmentally friendly and cost-saving advantages. In addition to dissolved methane, high NH4+ concentrations also are contained within the anaerobic digestion effluents. Recently, some studies have shown that Nx-DAMO represents a promising alternative that is potentially capable of achieving the simultaneous removal of dissolved methane and NH4+ efficiently. In 2011, Zhu et al.156 was the first to achieve a co-culture of (i) anaerobic ammonium oxidation microorganisms (anammox) that could directly convert NO2 and NH4+ into N2 in an oxygen-free environment and (ii) ANME that could simultaneously remove dissolved methane and NH4+ in synthetic wastewater containing CH4, NH4+, and NO2. The consumption rate of CH4 was 0.7 mmol L−1 d−1 and the enrichment of methane-oxidizing microorganisms was done with NC10 phylum bacteria. Subsequently, Chen et al.157 reported that ANME-2d, anammox, and NC10 phylum bacteria could jointly remove dissolved methane. The ANME-2d converted CH4 into CO2 by using fed in and/or generated NO3 by anammox bacteria, and DAMO bacteria also oxidized CH4 to CO2 by using the NO2 produced by anammox bacteria. In order to provide electron acceptors (NO2/NO3) for Nx-DAMO, and at the same time limit NO2 concentrations because high NO2 concentrations can be toxic or inhibitory to ANME,158 ammonia-oxidizing bacteria (AOB) were cultured with ANME and anammox species in the anaerobic digestion effluents. Chen et al.159 simultaneously removed dissolved methane and ammonium from the anaerobic digestion effluents by integrating Nx-DAMO and partial nitritation–anammox processes in a single-stage membrane biofilm reactor (Fig. 8). First, AOB provided NO2 for anammox by oxidizing NH4+ from bulk liquid and by using O2 from gas-permeable membranes. Then, ANME-2d converted the NO3 generated by anammox to NO2, which was provided to anammox in return or to NC10 phylum bacteria that used CH4 from bulk liquid as the electron donor; NC10 phylum bacteria converted CH4 and NO3 into CO2 and N2, respectively. Finally, dissolved methane and NH4+ were simultaneously removed from the system in the form of CO2 and N2, respectively. The factors such as oxygen surface loading (LO2) and HRT that could affect the dissolved methane removal efficiency were studied. The optimal dissolved removal efficiency was over 90% with a HRT > 5.75 days. The suitable LO2 range for >90% dissolved methane removal efficiency increased with an increase in the HRT.
image file: c6ra06868e-f8.tif
Fig. 8 Mechanisms present in co-cultures of anaerobic methanotrophic archaea (ANME), anaerobic ammonium oxidation (anammox) bacteria, and ammonia-oxidizing bacteria (AOB). GPMs: gas-permeable membranes, BL: bulk liquid.

Dissolved methane removal by the coupled AMO process in anaerobic digestion effluents has not been studied very much. Therefore, in addition to coupled processes involving Nx-DAMO, other models such as CS/US-DAMO, MIs-DAMO, and O-DAMO should be studied in relation to dissolved methane removal efficiencies in future work.

5 Conclusions and outlook

In this review, in order to sufficiently extract energy from biomass in wastewater treatment plants, microbial dynamics and scientific models involved in the AD process were first presented, which could contribute to future advances in AD systems and optimized performance of the design and operation parameters.

The sludge AD process involves three co-dependent biological stages, and sludge hydrolysis is the limiting step for biogas production. Sludge focused-pulsed pre-treatment technology had been applied in full-scale WWTPs, and this technology can conveniently and effectively breakdown sludge flocs and cells in a way that accelerates sludge hydrolysis. However, treatment chambers used in focused-pulsed technology are easily corroded; thus, they should be designed well with proper materials.

Bioelectrochemical systems such as MECs and Fe/Fe3O4 nanomaterial methods have become hot research topics in recent years. While these two methods have only been studied at the bench-scale, they have shown great potential for future applications because of their various advantages. In general, MECs and Fe/Fe3O4 nanomaterial methods cannot only improve the AD process, but they can also increase the methane content of the biogas. In the future, a more detailed examination into MECs and Fe/Fe3O4 nanomaterial methods for their application potential is urgently needed.

To improve MECs technology for biogas production, the effects of the design parameters such as the electrode size and electrode arrangement, the operational parameters such as temperature, organic loading rate, and pH, and toxicants including organic toxicants and inorganic toxicants on the efficiency of AD-MECs and the associated mechanisms should be further investigated in future studies.

Fe/Fe3O4 nanomaterial methods may be capable of simultaneously accelerating sludge hydrolysis, improving biogas production, increasing the methane content, producing more stabilized sludge, and reducing odors during the AD process. However, the dose of Fe/Fe3O4 nanomaterials used is an important factor to consider for applications in the sludge AD process. If the dose of Fe/Fe3O4 nanomaterials is too large, this could greatly inhibit the AD process.160,161 Therefore, the optimal dose of Fe/Fe3O4 nanomaterials and its effects should be further investigated.

When energy is extracted from biomass in wastewater treatment plants, dissolved methane in the anaerobic digestion effluents can be harmful to the environment if it is released to the atmosphere; therefore, technologies that can effectively remove methane by coupled AMO processes are urgently needed. This area of study is just in the starting stage, but it has a bright future for application because of its environmentally friendly and cost-saving advantages. In addition to coupled processes with Nx-DAMO, other models such as CS/US-DAMO, MIs-DAMO, and O-DAMO should also be studied for dissolved methane removal in future work.

Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (16XNH040).

References

  1. Y. K. Zhang, X. F. Yin, Z. J. He, X. J. Zhang, Y. Wen and H. C. Wang, Int. J. Environ. Res. Public Health, 2015, 12, 15449–15458 Search PubMed .
  2. Z. G. Deng, National sludge disposal technology and equipment industry development peak BBS, Wuhan, November 2015 Search PubMed .
  3. National Bureau of Statistics of China, http://www.stats.gov.cn/tjsj/ndsj/, accessed March 2016.
  4. Y. Y. Li and T. Noike, Jpn. J. Water Pollut. Res., 1987, 10, 729–740 CrossRef CAS .
  5. J. Yu, N. N. Tian, K. J. Wang and Y. Ren, Chin. J. Environ. Eng., 2007, 1, 82–86 Search PubMed .
  6. L. Levén, Anaerobic digestion at mesophilic and thermophilic temperature, Doctoral thesis, Swedish University of Agricultural Sciences Uppsala, 2006 .
  7. O. Bordelanne, M. Montero, F. Bravin, A. Prieur-Vernat, O. Oliveti-Selmi, H. Pierre, M. Papadopoulo and T. Muller, J. Nat. Gas Sci. Eng., 2011, 3, 617–624 CrossRef CAS .
  8. C. K. Yin, N. W. Zhu, H. P. Yuan and Z. Y. Lou, China Environ. Sci., 2016, 36, 833–839 Search PubMed .
  9. C. Yang, Z. He, Z. Guo, A. Zhou, A. Wang and W. Liu, Desalin. Water Treat., 2016, 57, 13183–13189 CrossRef CAS .
  10. M. S. Prabhu and S. Mutnuri, Waste Manage. Res., 2016, 1–9 Search PubMed .
  11. X. H. Dai, X. Gai and B. Dong, Chin. J. Environ. Eng., 2014, 8, 3912–3918 CAS .
  12. K. Solon, X. Flores-Alsina, C. K. Mbamba, E. I. Volcke, S. Tait, D. Batstone, K. V. Gernaey and U. Jeppsson, Water Res., 2015, 70, 235–245 CrossRef CAS PubMed .
  13. L. Wang, L. Xie, G. Luo and Q. Zhou, J. Agric. Sci. Technol., 2012, 14, 134–144 CAS .
  14. A. K. Jha, J. Li, L. Nies and L. Zhang, Afr. J. Biotechnol., 2013, 10, 14242–14253 Search PubMed .
  15. W. W. Li and H. Q. Yu, Biotechnol. Adv., 2011, 29, 972–982 CrossRef CAS PubMed .
  16. D. Brown, J. Shi and Y. Li, Bioresour. Technol., 2012, 124, 379–386 CrossRef CAS PubMed .
  17. O. P. Karthikeyan and C. Visvanathan, Rev. Environ. Sci. Biotechnol., 2013, 12, 257–284 CrossRef CAS .
  18. X. Q. Cao, A. N. Chen, Y. P. Gan, J. Chang and J. Zhou, Environ. Eng., 2008, 26, 215–219 Search PubMed .
  19. P. C. Suryawanshi, A. B. Chaudhari and R. M. Kothari, Crit. Rev. Biotechnol., 2010, 30, 31–40 CrossRef CAS PubMed .
  20. J. C. Frigon and S. R. Guiot, Biofuels, Bioprod. Biorefin., 2010, 4, 447–458 CrossRef CAS .
  21. Y. Cao and A. Pawłowski, Renewable Sustainable Energy Rev., 2012, 16, 1657–1665 CrossRef CAS .
  22. L. Appels, J. Baeyens, J. Degrève and R. Dewil, Prog. Energy Combust. Sci., 2008, 34, 755–781 CrossRef CAS .
  23. J. G. Ferry, Curr. Opin. Biotechnol., 2011, 22, 351–357 CrossRef CAS PubMed .
  24. S. Connaughton, G. Collins and V. O'Flaherty, Water Res., 2006, 40, 2503–2510 CrossRef CAS PubMed .
  25. J. L. Garcia, B. K. C. Patel and B. Ollivier, Anaerobe, 2000, 6, 205–226 CrossRef CAS PubMed .
  26. S. Sakai, H. Imachi, S. Hanada, A. Ohashi, H. Harada and Y. Kamagata, Int. J. Syst. Evol. Microbiol., 2008, 58, 929–936 CrossRef PubMed .
  27. K. Paul, J. O. Nonoh, L. Mikulski and A. Brune, Appl. Environ. Microbiol., 2012, 78, 8245–8253 CrossRef CAS PubMed .
  28. P. MallaáShrestha, Energy Environ. Sci., 2014, 7, 408–415 Search PubMed .
  29. R. S. Oremland, L. Marsh and D. J. Des Marais, Appl. Environ. Microbiol., 1982, 43, 462–468 CAS .
  30. R. Conrad, FEMS Microbiol. Ecol., 1999, 28, 193–202 CrossRef CAS .
  31. J. O'Reilly, C. Lee, G. Collins, F. Chinalia, T. Mahony and V. O'Flaherty, Water Res., 2009, 43, 3365–3374 CrossRef PubMed .
  32. B. K. Ahring, A. A. Ibrahim and Z. Mladenovska, Water Res., 2001, 35, 2446–2452 CrossRef CAS PubMed .
  33. R. Lepistö and J. Rintala, Bioresour. Technol., 1996, 56, 221–227 CrossRef .
  34. O. R. Kotsyurbenko, FEMS Microbiol. Ecol., 2005, 53, 3–13 CrossRef CAS PubMed .
  35. O. R. Kotsyurbenko, M. V. Glagolev, A. N. Nozhevnikova and R. Conrad, FEMS Microbiol. Ecol., 2001, 38, 153–159 CrossRef CAS .
  36. M. R. Wu, R. Zhang, J. Zhou, X. X. Xie, X. Y. Yong, Z. Y. Yan, M. M. Ge and T. Zhang, CIESC J., 2014, 65, 1602–1606 CAS .
  37. A. Donoso-Bravo, J. Mailier, C. Martin, J. Rodríguez, C. A. Aceves-Lara and A. V. Wouwer, Water Res., 2011, 45, 5347–5364 CrossRef CAS PubMed .
  38. D. J. Batstone, J. Keller, I. Angelidaki, S. V. Kalyuzhnyi, S. G. Pavlostathis, A. Rozzi, W. T. Sanders, H. Siegrist and V. A. Vavilin, Water Sci. Technol., 2002, 45, 65–73 CAS .
  39. M. Y. Lee, C. W. Suh, Y. T. Ahn and H. S. Skin, Bioresour. Technol., 2009, 100, 2816–2822 CrossRef CAS PubMed .
  40. H. Siegrist, D. Vogt, J. L. Garcia-Heras and G. Willi, Environ. Sci. Technol., 2002, 36, 1113–1123 CrossRef CAS PubMed .
  41. M. M. Hosseini, C. N. Mulligan and S. Barrington, Environ. Manag. Sustain. Dev., 2015, 4, 165–192 CrossRef .
  42. J. Bollon, R. Le-Hyaric, H. Benbelkacem and P. Buffière, Biochem. Eng. J., 2011, 56, 212–218 CrossRef CAS .
  43. A. Hierholtzer and J. C. Akunna, Water Sci. Technol., 2012, 66, 1565–1573 CrossRef CAS PubMed .
  44. B. Wett, I. Takács, D. Batstone, C. Wilson and S. Murthy, Water Sci. Technol., 2014, 69, 1634–1640 CrossRef CAS PubMed .
  45. R. Wang, Y. Li, W. Wang, Y. Chen and P. A. Vanrolleghem, Chem. Eng. J., 2015, 260, 791–800 CrossRef CAS .
  46. K. Solon, X. Flores-Alsina, C. K. Mbamba, E. I. Volcke, S. Tait, D. Batstone, K. V. Gernaey and U. Jeppsson, Water Res., 2015, 70, 235–245 CrossRef CAS PubMed .
  47. X. Xiao, G. P. Sheng, Y. Mu and H. Q. Yu, Water Res., 2013, 47, 6007–6013 CrossRef CAS PubMed .
  48. A. E. Tugtas, U. Tezel and S. G. Pavlostathis, Water Sci. Technol., 2006, 54, 41–50 CrossRef CAS PubMed .
  49. V. Fedorovich, P. Lens and S. Kalyuzhnyi, Appl. Biochem. Biotechnol., 2003, 109, 33–45 CrossRef CAS PubMed .
  50. W. J. Parker and G. H. Wu, Water Sci. Technol., 2006, 54, 111–117 CrossRef CAS PubMed .
  51. G. Esposito, L. Frunzo, A. Panico and F. Pirozzi, Waste Manage., 2011, 31, 2527–2535 CrossRef CAS PubMed .
  52. F. Blumensaat and J. Keller, Water Res., 2005, 39, 171–183 CrossRef CAS PubMed .
  53. U. Zaher, P. Grau, L. Benedetti, E. Ayesa and P. A. Vanrolleghem, Environ. Model. Software, 2007, 22, 40–58 CrossRef .
  54. J. Kauder, N. Boes, C. Pasel and J. D. Herbell, Chem. Eng. Technol., 2007, 30, 1100–1112 CrossRef CAS .
  55. D. Brdjanovic, M. Mithaiwala, M. S. Moussa, G. Amy and M. C. M. Loosdrecht, Water Sci. Technol., 2007, 56, 21–31 CrossRef CAS PubMed .
  56. X. Xin, J. He, J. Feng, L. Li, Z. Wen, Q. Hu, Q. Wei and J. Zhang, Chem. Eng. J., 2016, 284, 979–988 CrossRef CAS .
  57. L. Yu, Q. Zhao, J. Ma, C. Frear and S. Chen, Bioresour. Technol., 2012, 124, 8–17 CrossRef CAS PubMed .
  58. C. Martin and P. A. Vanrolleghem, Environ. Model. Software, 2014, 60, 188–201 CrossRef .
  59. G. Lehne, A. Müller and J. Schwedes, Water Sci. Technol., 2001, 43, 19–26 CAS .
  60. J. A. Müller, A. Winter and G. Strünkmann, Water Sci. Technol., 2004, 49, 97–104 Search PubMed .
  61. A. K. Wahidunnabi and C. Eskicioglu, Water Res., 2014, 66, 430–446 CrossRef CAS PubMed .
  62. A. Machnicka, K. Grübel and K. Mirota, Ecol. Chem. Eng. S, 2015, 22, 645–658 CAS .
  63. E. J. Martínez, J. G. Rosas, A. Morán, A. Morán and X. Gómez, Water, 2015, 7, 6483–6495 CrossRef .
  64. W. Gao, Desalination, 2011, 268, 170–173 CrossRef CAS .
  65. D. Ki, P. Parameswaran, B. E. Rittmann and C. I. Torres, Environ. Eng. Sci., 2015, 32, 831–837 CrossRef CAS .
  66. G. Silvestre, B. Ruiz, M. Fiter, C. Ferrer, J. G. Berlanga, S. Alonso and A. Canut, Ozone: Sci. Eng., 2015, 37, 316–322 CrossRef CAS .
  67. S. Pilli, T. T. More, S. Yan, R. D. Tyagi and R. Y. Surampalli, Chem. Eng. J., 2016, 283, 285–292 CrossRef CAS .
  68. S. Yu, G. Zhang, J. Li, Z. Zhao and X. Kang, Bioresour. Technol., 2013, 146, 758–761 CrossRef CAS PubMed .
  69. H. Liu, H. Xiao, B. Yin, Y. P. Zhu, H. He, B. Fu and H. J. Ma, Chem. Eng. J., 2016, 284, 194–201 CrossRef CAS .
  70. J. E. Banaszak, P. Burrowes and R. Lopez, 5th Canadian Residuals and Biosolids Conference Proceedings, Niagara Falls, September 2009 Search PubMed .
  71. OpenCEL, http://www.trojantechnologies.com/our-businesses/opencel/, accessed March 2016.
  72. J. E. Banaszak, P. Burrowes, G. Daigger, B. E. Mark, M. K. Angela, E. R. Bruce, S. Michael and R. S. Paul, Proc. Water Environ. Fed., 2008, 2008, 104–115 CrossRef .
  73. B. E. Rittmann, H. Lee, H. Zhang, J. Alder, J. E. Banaszak and R. Lopez, Water Sci. Technol., 2008, 58, 1895–1901 CrossRef CAS PubMed .
  74. J. E. Banaszak, B. E. Rittmann, K. Pagilla, I. Lukicheva and R. Lopez, Proc. Water Environ. Fed., 2010, 2010, 1033–1047 CrossRef .
  75. H. Zhang, J. E. Banaszak, P. Parameswaran, J. Alder, R. Krajmalnik-Brown and B. E. Rittmann, Water Res., 2009, 43, 4517–4526 CrossRef CAS PubMed .
  76. I. S. Lee and B. E. Rittmann, Bioresour. Technol., 2011, 102, 2542–2548 CrossRef CAS PubMed .
  77. H. Choi, S. W. Jeong and Y. Chung, Bioresour. Technol., 2006, 97, 198–203 CrossRef CAS PubMed .
  78. M. B. Salerno, H. S. Lee, P. Parameswaran and B. E. Rittmann, Water Environ. Res., 2009, 81, 831–839 CrossRef CAS PubMed .
  79. D. Ki, P. Parameswaran, S. C. Popat, B. E. Rittmann and C. I. Torres, Bioresour. Technol., 2015, 195, 83–88 CrossRef CAS PubMed .
  80. I. Lee and B. E. Rittmann, J. Environ. Eng., 2015, 142, 04015056 CrossRef .
  81. M. R. llen, A. Braithwaite and C. C. Hills, Environ. Sci. Technol., 1997, 31, 1054–1061 CrossRef .
  82. I. S. Lee, P. Parameswaran, J. M. Alder and B. E. Rittmann, Water Environ. Res., 2010, 82, 2316–2324 CrossRef CAS PubMed .
  83. U. Zimmermann, Rev. Physiol., Biochem. Pharmacol., 1986, 105, 196–256 Search PubMed .
  84. A. J. H. Sale and W. A. Hamilton, Biochim. Biophys. Acta, 1968, 163, 37–43 CrossRef CAS .
  85. V. Sitzmann, High voltage pulse techniques for food preservation, in New methods for food preservation, Springer, 1995, pp. 236–252 Search PubMed .
  86. M. Lindgren, K. Aronsson, S. Galt and T. Ohlsson, Innovative Food Sci. Emerging Technol., 2002, 3, 233–245 CrossRef .
  87. D. Gerlach, N. Alleborn, A. Baars, A. Delgado, J. Moritz and D. Knorr, Innovative Food Sci. Emerging Technol., 2008, 9, 408–417 CrossRef .
  88. Q. Zang, G. V. Barbosa-Cánovas and B. G. Swanson, J. Food Eng., 1995, 25, 261–281 CrossRef .
  89. B. Qin, Q. Zhang, G. V. Barbosa-Cánovas, B. G. Swanson and P. D. Pedrow, Trans. ASAE, 1995, 38, 557–565 CrossRef .
  90. N. Meneses, H. Jaeger, J. Moritz and D. Knorr, Innovative Food Sci. Emerging Technol., 2011, 12, 6–12 CrossRef .
  91. J. Morren, B. Roodenburg and S. W. H. de Haan, Innovative Food Sci. Emerging Technol., 2003, 4, 285–295 CrossRef CAS .
  92. M. M. Góngora-Nieto, D. R. Sepúlveda, P. Pedrow, G. V. Barbosa-Cánovas and B. G. Swanson, LWT–Food Sci. Technol., 2002, 35, 375–388 CrossRef .
  93. W. W. Li and H. Q. Yu, Environ. Sci. Technol., 2013, 48, 17–18 CrossRef PubMed .
  94. H. Matsushima, Y. Fukunaka and K. Kuribayashi, Electrochim. Acta, 2006, 51, 4190–4198 CrossRef CAS .
  95. S. Cheng, D. Xing, D. F. Call and B. E. Logan, Environ. Sci. Technol., 2009, 43, 3953–3958 CrossRef CAS PubMed .
  96. B. E. Logan, D. Call, S. Cheng, H. V. Hamelers, T. H. Sleutels, A. W. Jeremiasse and R. A. Rozendal, Environ. Sci. Technol., 2008, 42, 8630–8640 CrossRef CAS PubMed .
  97. J. R. Varcoe, P. Atanassov, D. R. Dekel, A. M. Herring, M. A. Hickner, P. A. Kohl, T. Xu and L. Zhuang, Energy Environ. Sci., 2014, 7, 3135–3191 CAS .
  98. S. W. Bai, Master thesis, Harbin Institute of Technology, 2014 .
  99. Y. Chen, B. Yu, C. Yin, C. Zhang, X. Dai, H. Yuan and N. Zhu, RSC Adv., 2016, 6, 1581–1588 RSC .
  100. X. Guo, J. Liu and B. Xiao, Int. J. Hydrogen Energy, 2013, 38, 1342–1347 CrossRef CAS .
  101. J. R. Asztalos and Y. Kim, Water Res., 2015, 87, 503–512 CrossRef CAS PubMed .
  102. Y. Song, Q. Feng and Y. Ahn, Energ. Fuel., 2016, 30, 352–259 CrossRef CAS .
  103. R. D. Cusick, Y. Kim and B. E. Logan, Science, 2012, 335, 1474–1477 CrossRef CAS PubMed .
  104. S. Kondaveeti and B. Min, Water Res., 2015, 87, 137–144 CrossRef CAS PubMed .
  105. P. Clauwaert and W. Verstraete, Appl. Microbiol. Biotechnol., 2009, 82, 829–836 CrossRef CAS PubMed .
  106. J. De Vrieze, S. Gildemyn, J. B. A. Arends, I. Vanwonterghem, K. Verbeken, N. Boon, W. Verstraete, G. W. Tyson, T. Hennebel and K. Rabaey, Water Res., 2014, 54, 211–221 CrossRef CAS PubMed .
  107. R. Sun, A. Zhou, J. Jia, Q. Liang, Q. Liu, D. Xing and N. Ren, Bioresour. Technol., 2015, 175, 68–74 CrossRef CAS PubMed .
  108. Z. Zhao, Y. Zhang, X. Quan and H. Zhao, Bioresour. Technol., 2016, 200, 235–244 CrossRef CAS PubMed .
  109. Z. Zhao, Y. Zhang, Q. Yu, W. Ma, J. Sun and X. Quan, Int. Biodeterior. Biodegrad., 2016, 106, 161–169 CrossRef CAS .
  110. D. E. H. Rotaru, A. E. Franks, R. Orellana, C. Risso and K. P. Nevin, Adv. Microb. Physiol., 2011, 59, 1–100 CrossRef PubMed .
  111. A. E. Rotaru, P. M. Shrestha, F. Liu, B. Markovaite, S. Chen, K. P. Nevin and D. R. Lovley, Appl. Environ. Microbiol., 2014, 80, 4599–4605 CrossRef PubMed .
  112. M. Villano, C. Ralo, M. Zeppilli, F. Aulenta and M. Majone, Bioelectrochemistry, 2016, 107, 1–6 CrossRef CAS PubMed .
  113. G. Zhen, X. Lu, T. Kobayashi, G. Kumar and K. Xu, Chem. Eng. J., 2016, 284, 1146–1155 CrossRef CAS .
  114. J. D. Coates, K. A. Cole, U. Michaelidou, J. Patrick, M. J. McInerney and L. A. Achenbach, Appl. Environ. Microbiol., 2005, 71, 4728–4735 CrossRef CAS PubMed .
  115. J. Zhang, Y. Zhang, X. Quan and S. Chen, Water Res., 2013, 47, 5719–5728 CrossRef CAS PubMed .
  116. Y. Feng, Y. Zhang, S. Chen and X. Quan, Chem. Eng. J., 2015, 259, 787–794 CrossRef CAS .
  117. J. Hedberg, C. Baresel and I. Odnevall Wallinder, J. Environ. Sci. Health, Part A: Toxic/Hazard. Subst. Environ. Eng., 2014, 49, 1416–1424 CrossRef CAS PubMed .
  118. Y. Yang, C. Zhang and Z. Hu, Environ. Sci.: Processes Impacts, 2013, 15, 39–48 CAS .
  119. E. K. Ünşar, A. S. Çığgın, A. Erdem and N. A. Perendeci, Environ. Sci.: Processes Impacts, 2016, 18, 277–288 Search PubMed .
  120. S. J. Klaine, P. J. J. Alvarez, G. E. Batley, T. F. Fernandes, R. D. Handy, D. Y. Lyon, S. Mahendra, M. J. McLaughlin and J. R. Lead, Environ. Toxicol. Chem., 2008, 27, 1825–1851 CrossRef CAS PubMed .
  121. E. Cabiscol, J. Tamarit and J. Ros, Int. Microbiol., 2010, 3, 3–8 Search PubMed .
  122. Y. K. Zhang, Z. J. He, H. C. Wang, L. Qi, G. H. Liu and X. J. Zhang, Front. Environ. Sci. Eng., 2015, 9, 770–783 CrossRef .
  123. Z. Yang, X. Xu, R. Guo, X. Fan and X. Zhao, Bioresour. Technol., 2015, 190, 132–139 CrossRef CAS PubMed .
  124. Y. Feng, Y. Zhang, X. Quan and S. Chen, Water Res., 2014, 52, 242–250 CrossRef CAS PubMed .
  125. X. Li, D. G. Brown and W. Zhang, J. Nanopart. Res., 2007, 9, 233–243 CrossRef CAS .
  126. L. Su, X. Shi, G. Guo, A. Zhao and Y. Zhao, J. Mater. Cycles Waste Manage., 2013, 15, 461–468 CrossRef CAS .
  127. B. Marsalek, D. Jancula, E. Marsalkova, M. Mashlan, K. Safarova, J. Tucek and R. Zboril, Environ. Sci. Technol., 2012, 46, 2316–2323 CrossRef CAS PubMed .
  128. Y. Liu and G. V. Lowry, Environ. Sci. Technol., 2006, 40, 6085–6090 CrossRef CAS PubMed .
  129. J. R. Sieber, M. J. McInerney and R. P. Gunsalus, Annu. Rev. Microbiol., 2012, 66, 429–452 CrossRef CAS PubMed .
  130. A. W. Carpenter, S. N. Laughton and M. R. Wiesner, Environ. Eng. Sci., 2015, 32, 647–655 CrossRef CAS PubMed .
  131. C. Cruz Viggi, S. Rossetti, S. Fazi, P. Paiano, M. Majone and F. Aulenta, Environ. Sci. Technol., 2014, 48, 7536–7543 CrossRef CAS PubMed .
  132. L. H. Su, G. Y. Zhen, L. J. Zhang, Y. Zhao, D. Niu and X. Chai, Environ. Sci.: Processes Impacts, 2015, 17, 2013–2021 CAS .
  133. X. Gao, C. A. Schlosser, A. Sokolov, K. W. Anthony, Q. Zhuang and D. Kicklighter, Environ. Res. Lett., 2013, 8, 035014 CrossRef .
  134. K. Hartley and P. Lant, Biotechnol. Bioeng., 2006, 95, 384–398 CrossRef CAS PubMed .
  135. J. Cookney, E. Cartmell, B. Jefferson and E. J. McAdam, Water Sci. Technol., 2012, 65, 604–610 CrossRef CAS PubMed .
  136. W. M. Bandara, T. Kindaichi, H. Satoh, M. Sasakawa, Y. Nakahara, M. Takahashi and S. Okabe, Water Res., 2012, 46, 5756–5764 CrossRef CAS PubMed .
  137. C. L. Souza, C. A. L. Chernicharo and G. C. B. Melo, Water Sci. Technol., 2012, 65, 1229–1237 CrossRef CAS PubMed .
  138. W. S. Reeburgh, Earth Planet. Sci. Lett., 1976, 28, 337–344 CrossRef CAS .
  139. K. Knittel and A. Boetius, Annu. Rev. Microbiol., 2009, 63, 311–334 CrossRef CAS PubMed .
  140. L. Fu, S. W. Li, Z. W. Ding, J. Ding, Y. Z. Lu and R. J. Zeng, Water Res., 2016, 88, 808–815 CrossRef CAS PubMed .
  141. K. U. Hinrichs, J. M. Hayes, S. P. Sylva, P. G. Brewer and E. F. Delong, Nature, 1999, 398, 802–805 CrossRef CAS PubMed .
  142. A. Meyerdierks, M. Kube, I. Kostadinov, H. Teeling, F. O. Glöckner, R. Reinhardt and R. Amann, Environ. Microbiol., 2010, 12, 422–439 CrossRef CAS PubMed .
  143. K. Knittel, T. Lösekann, A. Boetius, K. Renate and A. Rudolf, Appl. Environ. Microbiol., 2005, 71, 467–479 CrossRef CAS PubMed .
  144. V. J. Orphan, C. H. House, K. U. Hinrichs, K. D. Mckeegan and E. F. Delong, Science, 2001, 293, 484–487 CrossRef CAS PubMed .
  145. V. J. Orphan, K. U. Hinrichs, W. Ussler, C. K. Paull, L. T. Taylor, S. P. Sylva, J. M. Hayes and E. F. Delong, Appl. Environ. Microbiol., 2001, 67, 1922–1934 CrossRef CAS PubMed .
  146. H. J. Mills, C. Hodges, K. Wilson, I. R. Macdonald and P. A. Sobecky, FEMS Microbiol. Ecol., 2003, 46, 39–52 CrossRef CAS PubMed .
  147. A. J. M. Stams and C. M. Plugge, Nat. Rev. Microbiol., 2009, 7, 568–577 CrossRef CAS PubMed .
  148. V. J. Orphan, C. H. House, K. U. Hinrichs, K. D. Mckeegan and E. F. DeLong, Proc. Natl. Acad. Sci. U. S. A., 2002, 99, 7663–7668 CrossRef CAS PubMed .
  149. A. Pernthaler, A. E. Dekas, C. T. Brown, S. K. Goffredi, T. Embaye and V. J. Orphan, Proc. Natl. Acad. Sci. U. S. A., 2008, 105, 7052–7057 CrossRef CAS PubMed .
  150. J. Milucka, T. G. Ferdelman, L. Polerecky, D. Franzke, G. Wegener, M. Schmid, I. Lieberwirth, M. Wagner, F. Widdel and M. M. M. Kuypers, Nature, 2012, 491, 541–546 CrossRef CAS PubMed .
  151. T. Lösekann, K. Knittel, T. Nadalig, B. Fuchs, H. Niemann, A. Boetius and R. Amann, Appl. Environ. Microbiol., 2007, 73, 3348–3362 CrossRef PubMed .
  152. M. F. Haroon, S. Hu, Y. Shi, M. Imelfort, J. Kelle, P. Hugenholtz, Z. G. Yuan and G. W. Tyson, Nature, 2013, 500, 567–570 CrossRef CAS PubMed .
  153. K. F. Ettwig, S. Shima, V. De Pas-Schoonen, K. Jörg, H. M. Marnix, J. M. O. D. C. Huub, S. M. J. Mike and S. Marc, Environ. Microbiol., 2008, 10, 3164–3173 CrossRef CAS PubMed .
  154. E. J. Beal, C. H. House and V. J. Orphan, Science, 2009, 325, 184–187 CrossRef CAS PubMed .
  155. V. Gupta, K. A. Smemo, J. B. Yavitt, F. David, B. Brian and B. Nathan, Environ. Sci. Technol., 2013, 47, 8273–8279 CAS .
  156. B. Zhu, J. Sánchez, T. A. van Alen, J. Sanabria, M. S. M. Jetten, K. F. Ettwig and B. Kartal, Biochem. Soc. Trans., 2011, 39, 1822–1825 CrossRef CAS PubMed .
  157. X. Chen, J. Guo, Y. Shi, S. Hu, Z. Yuan and B. J. Ni, Environ. Sci. Technol., 2014, 48, 9540–9547 CrossRef CAS PubMed .
  158. Z. He, C. Cai, S. Geng, L. P. Lou, X. Y. Xu, P. Zheng and B. L. Hu, Bioresour. Technol., 2013, 147, 315–320 CrossRef CAS PubMed .
  159. X. Chen, J. Guo, G. J. Xie, Y. W. Liu, Z. G. Yuan and B. J. Ni, Water Res., 2015, 85, 295–303 CrossRef CAS PubMed .
  160. Y. Yang, J. Guo and Z. Hu, Water Res., 2013, 47, 6790–6800 CrossRef CAS PubMed .
  161. Y. X. Huang, J. L. Guo, C. Y. Zhang and Z. Q. Hu, Water Res., 2016, 88, 475–480 CrossRef CAS PubMed .

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