RSC Advances

REVIEW

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View Article OnlineView Journal | View Issue

Investigation of t

aSchool of Biological and Chemical Engine

Nanyang 473004, ChinabSchool of Environmental Science and E

250100, China. E-mail: niuqg96@163.comcJiangxi Bocent Advanced Ceramic Environ

337000, ChinadDepartment of Civil and Environmental Eng

Tohoku University, Miyagi 980-8579, Japan

† These authors contributed equally to th

Cite this: RSC Adv., 2020, 10, 39171

Received 7th August 2020Accepted 5th October 2020

DOI: 10.1039/d0ra06813f

rsc.li/rsc-advances

This journal is © The Royal Society o

he process stability of differentanammox configurations and assessment of thesimulation validity of various anammox-basedkinetic models

Chunyan Wang,ab Hanyang Wu,†c Bin Zhu,c Jianyang Song,a Tingjie Lu,c Yu-You Lid

and Qigui Niu *b

Over the last 30 years, the successful implementation of the anammox process has attracted research

interest from all over the world. Various reactor configurations were investigated for the anammox

process. However, the construction of the anammox process is a delicate topic in regards to the high

sensitivity of the biological reaction. To better understand the effects of configurations on the anammox

performance, process-kinetic models and activity kinetic models were critically overviewed, respectively.

A significant difference in the denitrification capabilities was observed even with similar dominated

functional species of anammox with different configurations. Although the kinetic analysis gained insight

into the feasibility of both batch and continuous processes, most models were often applied to match

the kinetic data in an unsuitable manner. The validity assessment illustrated that the Grau second-order

model and Stover–Kincannon model were the most appropriate and shareable reactor-kinetic models

for different anammox configurations. This review plays an important role in the anammox process

performance assessment and augmentation of the process control.

1. Introduction

With the development of the economy of the world, eutrophi-cation has spread fast and led to serious aquatic environmentalpollution, especially in the developing countries. The exces-sively discharged nitrogen can cause many negative impacts,such as eutrophication. Therefore, nitrogen removal shall beprocessed in a sustainable way, especially for wastewater treat-ment with a low C/N ratio. With the lower capital investment,less operational maintenance, no additional organic carbonsource, 90% reduction of sludge and the limited emission ofN2O, the anaerobic ammonia oxidation (anammox) process isa new nitrogen removal technology that has gained widespreadattention.1,2 However, despite the increasing interest in theanammox application, there were still many difficulties, such asthe low bacterial growth rate and the sensitivity to the envi-ronmental factors.3

ering, Nanyang Institute of Technology,

ngineering, Shandong University, Jinan

mental Technology Co., Ltd, Pingxiang

ineering, Graduate School of Engineering

is work.

f Chemistry 2020

Anammox processes can be conducted in many differentcongurations. Each process has its own optimized operationsand characteristics based on the design of the reactor. However,it is generally known that common reactors have certainshortcomings, and that appropriate operation conditionsshould be fully investigated to prevent the process from failurewithout optimized operation.4 Different reactors have differentcapabilities of nitrogen removal. Among the applied reactors,the Sequencing Batch Reactor (SBR), Up Flow Anaerobic SludgeBlanket (UASB) and Membrane Bio-Reactor (MBR) were themost popular congurations with different congurationstructures that benet the biomass enrichment in the form ofthe biolm or for avoiding sludge wash out.5–7 The aim ofoptimizing the operational conditions is to enhance the effi-ciency of nitrogen removal.8 The effects of environmentalfactors at various operational options on the anammox micro-bial community were also reported.9 Moreover, the microbialresidence time plays a critical role in the taxonomic composi-tion and functional description of the microbial communities.10

A large number of previous studies focused on experimentaland mathematical methods.11–13 The process-kinetic models arepowerful tools for investigating the performances of differentcongurations during the anammox process. Appropriatekinetic mathematical models are enabled to assess, control, andmitigate the process inhibition and process optimization.

RSC Adv., 2020, 10, 39171–39186 | 39171

RSC Advances Review

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These models can be employed to achieve many key objectivesinvolving the optimization of the chemical processes, assessmentof the reaction hazards, and the design of emergency reliefsystems.14,15 Process-kinetic models enhance the comprehensionof the constructed system, and are conducive to the formulationand validation of a hypothesis. The prediction of the system'sbehaviour at a variety of conditions was developed to reduce theoperation risks. However, certain selection criteria and conditionsmust be obeyed when the proper evaluation and application of theprocess models were performed for understanding the process orpredicting the performance. In order to identify a suitable model,a correct model type shall be selected, and the parameters of themodel shall be estimated with the available data. To a great extent,the validity of the studies relies on the model, which is dened bythe proper preference of a mathematical model rst, and then thevalidity of the kinetics evaluation. Generally, an evaluation on theprocess performance is still characterized by the application of thesimple kineticsmodels andmethods for evaluating their reactions.

Kinetic models are proved to be a conventional way to estimateand prospect the performances and operations of differentprocesses. It is very important to choose a suitable model relatingto the kinetics evaluation of the anammox process assessment.Quantitative process-kinetics can gain insight into the underlyingoperational strategy with feasibility investigations in both batchand continuous processes. However, it is a pity that mostfrequently used models cannot t the kinetic data well. It isnecessary for us to know which model is better, and which isperfect in the simulated conditions. Moreover, the optimizedoperation conditions of the anammox process are also importantfor the lab scale and full-scale process design.

A majority of previous papers of different congurations(EGSB, UASB, SBR, MBR, and CSTR) were related to discussionsof the treatment performance or engineering application.However, only a limited number of articles are available forsolving the specic problems of the kinetics evaluation. So far,studies in investigating the anammox process-kinetics withdifferent congurations are relatively limited, and the assess-ment of anammox performance in different reactors is rare.Thus, the performance of the anammox system in differentreactors is reviewed in this paper with regards to validation ofthe commonly used process kinetic models.

2. Process-kinetics approaches

Different reactors used in the anammox process were summa-rized, and the performance comparison and kinetic simulationwere also evaluated. Currently, we are mainly focusing on theapplication of four kinds of substrate removal models,including the rst-order substrate removal model, the Grausecond-order substrate removal model, the modied Stover–Kincannon model and the Monod equation, to investigate theanammox process kinetics.

2.1 First-order substrate removal model

With regard to the application of the rst-order substrateremoval model to the bioreactor, the change rate of the

39172 | RSC Adv., 2020, 10, 39171–39186

substrate concentration in the completely mixed system may beexpressed as follows:12

� dS

dt¼ QSi

V� QSe

V� k1Se (1)

Because the change rate (�dS/dt) can be neglected undera pseudo-steady-state situation, the equation is derived as:

Si � Se

HRT¼ k1Se (2)

where Si and Se are the total nitrogen concentrations (g L�1) inthe inuent and effluent, HRT is the hydraulic retention time(day), k1 is the constant of the rst-order substrate removal rateconstant (1/d), Q is the inow rate (L/d) and V is the volume ofthe reactor (L).

2.2 Grau second-order substrate removal model

The general equation of a second-order model is described asshown below.16 By integration and linearization, the aboveequation can be expressed as eqn (3):

�dS

dt¼ k2

�Se

Si

�2

(3)

By integration and linearization, the above equation isexpressed as eqn (4):

SiHRT

Si � Se

¼ HRTþ Si

k2X(4)

If Si/(k2X) is considered as a constant (a) and (Si � Se)/Si isreplaced by the substrate removal efficiency (E), b is a constantfor the Grau second-order model, the above equation can berepresented as a simple one:

HRT

E¼ aþ bHRT:

2.3 Modied Stover–Kincannon model

As for the Stover–Kincannonmodel,16 initially, the equation wasapplied to predict the development of the attached-growthbiomass in a rotating biological contactor. Later, the modelwas further modied, and widely used to describe and predictthe performances of the bioreactor.17,18 The original model isexpressed, as shown below:

dS

dt¼

Umax

�QSi

A

�

KB þ�QSi

A

� (5)

where dS/dt is the substrate removal rate (kg m�3 d�1), Umax isthe constant of the maximum utilization rate (kg m�3 d�1), A isthe surface area of the disc (L), and KB is the constant of thesaturation value (kg m�3 d�1). If the volume of the reactor (V)takes the place of the surface area (A), the Stover–Kincannonmodel can be modied as below:18

This journal is © The Royal Society of Chemistry 2020

Fig. 1 Published papers in each year: (a) kinetic models used in theanammox process and (b) enrichment reactors. (Web of Scopus,access data: 2020.04.06).

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dS

dt¼

Umax

�QSi

V

�

KB þ�QSi

V

� (6)

where the Stover–Kincannon model takes dS/dt as a function ofthe loading rate of substrate at a steady state:dSdt

¼ QV� ðSi � SeÞ, so it can be further expressed as shown

below in eqn (7):

V

QðSi � SeÞ ¼KbV

Umax

QSi

þ 1

Umax

(7)

2.4 Monod model

The Monod model was extensively employed.19 The modiedversions of the Monod equation were developed concerning themodel anammox process in terms of the oxygen concentration,ammonia concentration and missing alkalinity:20

ds

dt¼ KSe

Ks þ Se

(8)

where K is the maximum substrate utilization rate (kg m�3 d�1),and Ks is the half saturation concentration (g L�1).

2.5 Other models used for the anammox-based process

Generally, the anammox bacteria cooperated with other func-tional bacteria to degrade the wastewater in the applied elds,such as SHARON and anammox process based on the continuitymodel.21 The ASM2, ASM2d and ASM3 models were also used tosimulate the anammox process, which is enriched frommunicipal activated sludge.22 The more complex microbialcommunities with AOB, NOB DHB and anammox were simu-lated with complex models, as reported.23

3. Anammox processes conducted indifferent reactors

In recent years, the interest in anammox has been increasedsignicantly with more and more articles being published astime has passed, as showed in Fig. 1a. Since 2005, an average of78 more papers has been published every year. In 2014, 300papers were published. Fig. 1b shows the number of anammoxprocesses conducted in different reactors, as previously re-ported. The sequencing batch reactor (SBR) was recognized asthe most dominantly employed bioreactor, which was the topicin one third of published papers, followed by the upowanaerobic sludge blanket (UASB), membrane bio-reactor (MBR),expanded granular sludge bed (EGSB), continuous stirred-tankreactor (CSTR), sequencing biolm batch reactor (SBBR) andupow anaerobic xed bed (UAFB), subsequently. While thenumber of applied UASBs was two-thirds of that of appliedSBRs, both SBRs and UASBs were the most popular reactorsconducted for the anammox process. Many advantages of thereactor are summarized in Table 1. The high NLR (nitrogen

This journal is © The Royal Society of Chemistry 2020

loading rate) and NRR (nitrogen removal ratio), enrichmentgranular sludge, fast start up and high settleability can be ob-tained when the UASB or EGSB is applied. However, the MBR(with a long sludge retention time (SRT)) brought a fast startupof the process and a high accumulation rate for anammoxenrichment, which was commonly evaluated with specialpurposes (Table 1). The general comparison of the anammoxprocess conducted in different processes is shown in Table 2.The contents regarding the general process descriptions,applications, advantages and disadvantages, and design criteriaare given for each technology based on previous researchstudies as the referenced applications. The advantages anddisadvantages of each reactor constructed for anammox arelisted in Table 2. Biolm, granule and occulent were the threemain kinds of anammox conducted in different reactors, amongwhich the biolm and granule were the most popular ones. Aprevious investigation showed that UASB and SBR were themost popular bioreactors that can make granules or keep highanammox biomass. The long retention time of the active sludgeis the key factor to inuence the successful development ofa high-rate anaerobic wastewater treatment. The long retentiontime of sludge can be realized due to the specic reactorconguration design and xed biomass in the form of staticbiolms, particle-supported biolms or granule sludge. It wasobserved that the UASB with upow can easily change thesludge into granules. The high loading rate of UASB with highremoval efficiency was oen reported.7 In contrast, the engi-neering applications of anammox included more than 100 full-scale partial nitridation/anammox installations worldwide by

RSC Adv., 2020, 10, 39171–39186 | 39173

Tab

le1

Anam

moxproce

ssesco

nductedin

differentco

nfigurations(m

ost

popularreac

tors)a

UASB

EGSB

CST

RSG

BR/SASB

R(G

/F)MBR

Nov-BFR

UAFB

MBBR/SBR

Adv

antage

HighNLR

andNRR;

enrich

men

tgran

ular

slud

ge;

highsettleab

ility

HighNLR

andNRR;

enrich

men

tgran

ular

sludg

e;fast

startup

;high

settleab

ility

Intensive

mixing;

idealmod

elfor

simulation

Highstab

ilityof

processen

rich

men

tgran

ular

slud

ge;fast

startup

;high

subs

tratecon.;

adjust

process

timingviaPL

C,

conventop

eration

Highstab

ilityof

process;

fast

start

up/enrich

men

t;highslud

geconcentration;h

igh

NRRwithfree

cell

or/granular

slud

ge;

higheffl

uent

quality;

highde

gree

ofau

tomation

High

stab

ility;

fast

enrich

men

t;bio

lmcu

lturing;

highde

gree

of automation

Highstab

ilityof

process;

fast

startup

/en

rich

men

t;highslud

geconcentration;

highNRRwith

free

cellor/

gran

ular

slud

ge

High

stab

ility;

fast

enrich

men

t;bio

lmcu

lturing

Disad

vantage

Deadzoneareasan

dsu

bstrateinhibition;

inue

nthierarchy;

slud

gewashou

t;sludg

eoa

tation

withlayer

distribution

Costlyto

operate;

slud

gewashou

t;slud

geoa

tation

consu

meen

ergy

LowSR

T;c

onsu

me

energy

Costlyto

operate;

slud

gewashou

t;negativeeff

ecton

downstream

processes;highpe

akow

may

disrup

tpe

rforman

ce

Costlyto

operate;

mem

bran

ecleaning

andreplacem

ent;

processsensitive

tosu

stained

peak

hou

row

;sop

histicated

operation

Costlyto

operate

Costlyto

operate;

mem

bran

ecleaning;

ligh

ter,

uffier

slud

geocs

Costlyto

operate;

bio

lmfall

off

aSG

BR:s

taticgran

ular

bedreactor;SA

SBR:s

tatican

aerobicslud

gebe

dreactor;Nov-BFR

:Noven

bio

lmreactor;MBBR:m

ovingbe

dbio

lmreactor.

39174 | RSC Adv., 2020, 10, 39171–39186 This journal is © The Royal Society of Chemistry 2020

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Tab

le2

Perform

ance

ofan

ammoxproce

ssco

nductedin

differentreac

tors

a

Reactor

type

Inoculum

slud

geOpe

ration

day(d)

T(�C)

pHAmmon

ium

removal

(%)

NLR

(kgN

m�3

d�1)

NH

4+–N

inue

nt(m

gL�

1)

NO2�–N

inue

nt(m

gL�

1)

Slud

geconcentration

(VSL�

1)or

anam

mox

purity

(%)

Referen

ce

UASB

Anaerobic

gran

ules

400

35�

17.0

86.5–92.3

6.4

1027

1409

—46

UASB

Aerob

icgran

ules

300

307.0

900.5

200

220

—26

UASB

Nitrifying+

anam

mox

>140

0Normal

6.3–7.3

<90

9.5

400–55

057

5–17

550

–60

47

UASB

Nitrifying

220

30–33

7.5–8.0

970.16

115

130

—48

UASB

Anam

mox

gran

ules

235

357.8–8.0

99.29(TN)

1.03

458

575

91.2–92.4

49

UASB

AN-GSA-AS

320

�35

7.3–7.9

951.25

——

—50

UASB

Anaerobic

gran

ular

slud

gean

ammox

slud

ge

214/45

035

�1

6.8–7

70–99.9

74.3–76.7

�200

240

42–57gVSL�

17

1LUASB

Synthetic

med

ium

7035

�1

6.8–7.0

93.2

�7.1

30–70

30–7

0—

51

SBR

ASbiom

ass

200–60

025

8.0

——

——

—52

SBR

Anam

mox

320

337.0–8.0

99–100

0.43

180

250

—53

SBR

Activated

slud

ge36

536

�0.3

7.5–8.2

99.9

1.6

1268

166.14

85.0

6SB

RAnam

mox

400

35�

16.7–7.0

60(TN)

1.0

700

0—

54SB

RAnam

mox

400

35�

17.5

78(TN)

0.36

200–25

00

<35

25SB

RAnam

mox

gran

ules

218

30�

17.5–8.0

980.3

150

150

—55

2LSB

RNitritation

–an

ammox

synthetic

wastewater

180

33�

185

–94

0.18

gN

perg

VSS

perd

500

—1.8gVSS

perL

18–19%

Ca.

Jettenia

(16.8%

)an

dNitrosomon

as(20.1%

)

56

18LSB

RPa

+AN

Raw

optoelectron

icindu

strial

wastewater

9437

7.8–8.0

10–33gm

�3d�

118

3–20

0—

—57

3LSB

R0.6kg

Nm

�3d�1

pigg

erywaste

prod

uction/no

dilution

50dstartup

308.1�

191

�10

970.5–0.6gNpe

rL

perd

—NO2–N

removed

/NH

4–N

removed

molar

ratio

was

1.28

�14

%

274�

45%

2.3g

TSS

perLan

d79

%VSS

/TSS

58

3LSB

RCANON

Trace

N2H

4

(4mgL�

1)was

adde

dinto

the

inuen

t

8031

�1

—70

�7(TN)

0.33

�0.06

kgN

perLpe

rd

150mgN

per

L—

Ca.

Scalindu

a,3.14

�10

9to

5.86

�10

10

copies

perg(dry

slud

ge)

59

This journal is © The Royal Society of Chemistry 2020 RSC Adv., 2020, 10, 39171–39186 | 39175

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Tab

le2

(Contd.)

Reactor

type

Inoculum

sludg

eOpe

ration

day(d)

T(�C)

pHAmmon

ium

removal

(%)

NLR

(kgN

m�3

d�1)

NH

4+–N

inue

nt(m

gL�

1)

NO2�–N

inue

nt(m

gL�

1)

Slud

geconcentration

(VSL�

1)or

anam

mox

purity

(%)

Referen

ce

MBBR

Synthetic

wastewater

2Lof

virgin

carrier

med

ia

86–121

33.4–35

7.5�

0.1

0.57

–0.64�

0.17

0.03

–0.22

348.36

–507

0.48

–0.77

Ca.

Brocadia

fulgidash

ito

Ca.

Kuenenia

stuttgartiensis

AOB1.83

–1.4

NOB

60

10LMBR

Synthetic

wastewater

306.8–7.5

85%

(TN)

1gN

perLpe

rd

12.9

mM

3.4mM

(1.21)

Growth

rate

of0.21

d�1

5

15L/8L

MBR

Synthetic

wastewater

250

377.1–7.5

1680

1680

0.23

dayas

free

cells

61

1.6LMBR

Synthetic

med

ium

6330

�1

7.0�

0.2

82.14

293/16

8084

084

0Ca.Brocadia60

%62

1.8L

GSB

FEffluen

tof

theA-

stag

eof

the

WWTP

20� C

/1–5

215

� C/60–12

710

� C/134

–285

20an

d10

7–7.5

—50

�7mgN

pergVSS

perd

0.4gN

perL

perd

50�

7mgN

pergVSS

per

d

Ca.

Brocadia

0.35

gTSS

/12g

TSS

63

6LSB

BR

Synthetic

ammon

ium-rich

wastewater

3035

�1

74—

470

——

64

SBBRs

Synthetic

med

ium

9035

�1

7.0–7.8

881.62

kgN

m�3

d�1

—Ca.

Anam

moxoglobu

s(D

Q31

7601

.1)

40.1%

65

2.6LFB

RSynthetic

wastewater

6535

�0.2

8�

0.2

0.05

–0.06kg

Nm

�3d�

10.25

kgN

m�3d�1

50mgL�

150

mgL�

1Ca.

Kuenenia

stuttgartiensis

66

EMBRs

—50

33�

18.0�

0.3

0.8

——

—67

5LAnR

Synthetic

med

ium

102

327.8�

0.3

900.30

gN

L�1d�

112

30�

61mgL�

1—

—68

aNLR

:nitrogenload

ingrate;a

nam

mox

purity:thepe

rcen

tage

ofan

ammox

inthebiom

ass;MBBR:m

ovingbe

dbio

lmreactor;EMBRs:external

MBR;F

BR:

xedbe

dreactor;PA

N-AnR:p

artial

nitrication

andan

ammox

reactor;GSB

F:gran

ular

uidized

bedreactor;SG

SR:s

tirred

gassolidreactor.

39176 | RSC Adv., 2020, 10, 39171–39186 This journal is © The Royal Society of Chemistry 2020

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2014. Most of them were conducted in the one-stage congu-ration, and the most commonly applied reactors were SBRs,followed by granular systems and MBBRs.24

4. Characteristic and stability analysisof the anammox process in differentreactor setups

Different reactors used for the anammox process are summa-rized in Table 2. The performance such as the startup time,nitrogen-loading rate, removal efficiency, substrate concentra-tion, and the purication of sludge is described and comparedin detail. Themost commonly used SBR reactor has an NLR thatvaried from 0.3 kg m�3 d�1 to 1.6 kg m�3 d�1 with the highestremoval efficiency over 99%.6 The comparison of the mostcommonly used UASB and SBR processes with the maximumNRR and startup time is shown in Fig. 2. Generally, the UASBprocess can obtain a high NRR over 2 kg m�3 d�1. However, thatof the SBR reactor was lower than 2 kg m�3 d�1 with a shortertime for startup. By comparing the anammox sludge with anorganic C content lower than 35%25 and a content higher than99%,6 it was found that the anammox sludge with higher puritycan obtain a higher nitrogen removal efficiency. Similarly, theUASB reactors with different purities and operation conditionshad relatively large differences in the NRRs, which might varyfrom 0.5 kg m�3 d�1 (ref. 26) to 76 kg m�3 d�1. To data, thehighest NRR obtained was 76 kg m�3 d�1 when the UASBreactor was fed with substrate of low concentration under shortHRT.7 Other reactors for the anammox process were reported tohave different NRRs varying from 0.3 kg m�3 d�1 to 1.62 kg m�3

d�1 (Table 2).

Fig. 2 The start up time and maximum NRR comparison of theanammox process of the most used reactors (a) UASB and (b) SBR.

This journal is © The Royal Society of Chemistry 2020

Over the past twenty years, many studies carried out havefocused on the optimization of the anammox process parame-ters. The relative issues involving the substrate inhibition,temperature effect, organic matter and salinity have beenextensively investigated.27 The drawback of the anammoxprocess with quite low bacteria growth rate can be overcome byMBR, which is more effective than most other reactors forenrichment.5 A reactor conguration with a high capacity ofbiomass retention is essential for the anammox process.Recently, a novel process conducted in an MBR with pure free-cell suspension of highly active anammox was successful, witha higher growth rate than the specic maximum growth rateever reported for the biomass, 0.21–0.23 d�1.5 This strategy hasa number of potential capacities in the cultivation of seedinganammox in terms of practical engineering projects.2 MBR orMBBR and Nov-BFR with carriers can accumulate a certainamount of anammox to keep the process stable. Additionally,inherent with each technology are the advantages and disad-vantages of the process, and/or operation and maintenance.Discharging standards are oen the primary consideration inselecting a treatment technology.

Anammox processes conducted in different reactors canaccumulate different kinds of anammox bacteria following thesubstrata and operation conditions. Because of its potentials innitrogen removal during the biological process, the high treat-ment performance and stability of the anammox process havebeen constantly studied for engineering application. Most of thelab-scale enrichment anammox bacteria were ‘Ca. Brocadia’ and‘Ca. Kuenenia’, which are almost mono-species in the anammoxprocess, illustrating a competition relationship of the anammoxspecies owing to their different substrate affinities. It should beworth noting that under specic physiological conditions, Ca.Kuenenia outgrows ‘Ca Brocadia’, resulting in overgrowth whenthe substrate concentration is low.28,29 Meanwhile, the ecologicalroles of the functional microbe and their potentials should beextensively explored in order to attain new perspectives for furtherdesign, operation, and maintenance of the process.

Generally, a large number of gas bubbles will be generated ingas tunnels and gas pockets in some high-loading bioreactorsinoculated with anammox bacteria of high activity. It is specu-lated that the gas bubbles entrapped in the gas pockets mightbe a pivotal factor to cause sludge oatation, especially in theupow reactor, such as a UASB or an EGSB. Aer the hollows arelled with gas bubbles, the anammox granules will unavoidablyoat and be washed out of the reactor, leading to a failure of theanammox process. The granule oatation can be undoubtedlyresponsible for instability, or even failure of the anammoxreactor.30,31 The existence of this washout obstacle compels theimplementation of a large number of studies in pursuit ofa feasible way to overcome this obstacle.

5. Process-kinetic analysis ofdifferent anammox processes

Bibliometric analysis showed that the investigation on thereactor-kinetic models in the published paper was only one-

RSC Adv., 2020, 10, 39171–39186 | 39177

Tab

le3

Comparisonofthekinetics

usedforthean

ammoxreac

tora

Reactor

V(L)

T (�C)

Subs

trate

Inoculum

NLR

(gL�

1d�

1)

First-orde

rGrausecond-orde

rMod

ied

Stover–

Kincannon

Mon

od

Ref.

HRT

(d)

k 1 (1/d)

R2

a (1/d)b

k 2 (1/d)R2

Rm

KB

R2

Ks

(gL�

1)

Mmax

(1/d)

Kd

(1/d)

Y (mgVSS

permg

N)

R2

ILAB

24/20

30�

1Synthetic

wastewater

PN-anam

mox

gran

ular

420�

30NH

3–

NmgL�

1:

1.0–

0.06

666

.90.61

021.09

0.02

2.06

40.99

5422

.2927

.250.98

10.00

2446

0.00

7408

0.00

590.35

110.71

–0.94

34

Up

owlter

35Cultured

activated

sludg

e

0.93

–7.34

0.08

–0.6

0.44

0.17

21.4

0.96

—0.98

512

.412

0.97

9—

——

——

69

ANMR

35Floccu

lent

anam

mox

slud

ge

0.10

7–0.74

60.6–3.0

5.31

0.70

70.11

1.11

—0.99

57.89

8.98

0.99

90.19

20.16

9—

—0.59

935

ANMR

35Floccu

lent

anam

mox

slud

ge

115–29

7/15

3–31

30.6–2.9

5.30

50.70

60.11

1.11

0.99

548.98

7.89

0.99

860.19

20.16

930.59

949

MAR

0.8/

0.65

25Sa

ltysynthetic

Cultured

anam

mox

slud

ge

0.08

–1.94

0.25

–1.0

7.44

0.75

60.06

1.14

—0.99

16.41

7.37

0.99

30.10

70.95

2—

0.95

20.99

311

AUF

35Cultured

activated

slud

ge

270–30

50.08

–0.6

1.4

0.96

0.98

5512

12.4

0.97

9212

288–30

7UABF

35Anam

mox

gran

ules

360–70

01–0.25

h0.04

1.06

0.99

935

.738

.10.99

936

475–92

4UABF

35Anam

mox

gran

ules

400/52

81–0.25

h0.05

1.04

0.99

820

.721

.60.99

837

UASB

125

Synthetic

wastewater

Preservation

treatm

ents-

4+distille

dwater

50–300

/60

–300

3–9.62

h55

.556

.60.99

38

UASB

125

Synthetic

wastewater

Preservation

treatm

ents–40+

dimethyl

sulfoxide

50–300

/60

–300

3–9.65

h0.99

91.16

50.99

1666

.767

.90.99

238

UASB

5037

Synthetic

wastewater

Anam

mox

gran

ules

167–27

8/19

1–41

10.2–3.2

11.64

0.80

430.09

1.03

—0.99

8527

.827

.50.99

949

SUASB

8.6

35�

1Synthetic

wastewater

Nitrifying

slud

gean

dmature

anam

mox

gran

ule

5.63

2.5–24

h14

.814

.50.98

970

39178 | RSC Adv., 2020, 10, 39171–39186 This journal is © The Royal Society of Chemistry 2020

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Tab

le3

(Contd.)

Reactor

V(L)

T (�C)

Subs

trate

Inoculum

NLR

(gL�

1d�1)

First-orde

rGrausecond-orde

rMod

ied

Stover–

Kincannon

Mon

od

Ref.

HRT

(d)

k 1 (1/d)

R2

a (1/d)b

k 2 (1/d)R2

Rm

KB

R2

Ks

(gL�

1)

Mmax

(1/d)

Kd

(1/d)

Y (mgVSS

permg

N)

R2

UASB

135

�1

Synthetic

wastewater

0.16

�10

0.24

kgSm

3

d1wou

ld

14.6

95.2984

.030.97

5871

UASB

135

�1

Synthetic

wastewater

Cop

per(II)

recovery

0.4–2.4

h15

1.515

7.90.73

72

UASB

135

�1

Synthetic

wastewater

OTCrecovery

0.4–2.4

h21

2.822

8.40.74

672

UASB

1.5

35�

1Synthetic

wastewater

Re-startup

49.5

56.8

0.98

541

UASB

1.5

35�

1Synthetic

wastewater

Re-startup

454.552

80.99

541

UASB

1.5

35�

1Synthetic

wastewater

Re-startup

384.644

7.70.99

41

UASB

1.5

35�

1Synthetic

wastewater

Re-startup

476.255

30.99

641

UASB

1.5

35�

1Synthetic

wastewater

Re-startup

144.915

3.70.99

41

UASB

135

�1

Synthetic

wastewater

Singlefeed

70–2

661.52

–2.06

h15

17.5

0.93

442

UASB

135

�1

Synthetic

wastewater

Multifeed

70–2

671.52

–2.06

h27

.537

.50.93

242

UASB

Activated

slud

ge0.15

–2.8

0.2–3.2

11.6

0.80

40.09

1.03

—0.99

912

.111

.40.99

90.09

20.22

5—

—0.77

316

UASB

recycled

5037

Synthetic

wastewater

Anam

mox

gran

ular

0.28

–1<1

27.8

27.5

0.99

949

PNreactor5

28�

2Rejectwater

wtp

Lab-scale

ll-draw

reactor

13.9

300.96

73

UAF

2.5

36Rejectwater

wtp

Mesop

hilic

digester

ofamun

icipal

31.2

42.1

0.97

73

UAF

230

Synthetic

wastewater

Wastewater

treatm

ent

plan

t

0.93

–7.34

gL�

12–14

.40.43

950.17

51.39

70.96

40.98

612

.412

0.97

912

ILAR

3.8

30�

1Synthetic

wastewater

Mun

icipal

wastewater

treatm

ent

plan

t

(NH

4)2SO

4

0.39

–6.96g

L�1

12–21h

5.77

5.39

0.95

7274

aILAB:internal-lo

opairli

bio-pa

rticle

reactor;ANMR:non

-woven

mem

bran

ereactor;MAR:marinean

ammox

reactor;VAF:

upow

anaerobiclter;R2:correlationcoeffi

cien

t;k 1

andk 2

isthe

subs

trateremoval

rate

constan

t(1/d)of

each

equa

tion

.

This journal is © The Royal Society of Chemistry 2020 RSC Adv., 2020, 10, 39171–39186 | 39179

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tenth of the total. Meanwhile, the Stover–Kincannon model wasthe most commonly used one, which was followed by theMonod model and the Grau-second model (Fig. 1b). It wasconrmed by many studies that the anammox bacteria werevery sensitive to operational conditions, such as dissolvedoxygen, temperature, pH and composition of organic matters.Unfavorable conditions will largely reduce the activity of theanammox bacteria, and eventually cause a failure in bioreactorperformance. Therefore, it is of practical signicance to effec-tively augment the activity of the anammox bacteria in order toachieve a high-rate process under the mainstream conditions,which might cause a large decrease in the specic activity ofanammox. Most of the literature reached a consensus in thatthe process control for anammox is a prerequisite to practicallyensure the successful performance under unstable inuentconditions.32

The common bioreactors have certain shortcomings sincedifferent reactors have different optimal operating conditions.For instance, the UASB reactor oen encounters unsatisfactorysubstrate removal efficiency, severe sludge washout, and largedead zone areas. An EGSB is costly in term of the hugeconsumption of energy and operation cost. In addition, a bio-lm reactor has a long sludge age and sporadic sludge otation.The main objectives of process modeling are to control andevaluate the performance of the process, as well as to optimizethe system design and scale up the pilot plant investigations.33

For example, the concentrations and activities of anammoxmeasured in different reactors could be used in conjunctionwith models to control the process parameters, involving theinuent loading rate and hydraulic residence time in order to

Fig. 3 Reactor-kinetic validation assessment. (a), N2 gas production (b),

39180 | RSC Adv., 2020, 10, 39171–39186

maximize nitrogen removal. To date, process control is widelyconsidered in the literature as an essential factor to guaranteegood performance in various bioreactors.

Modeling and parameter identication are subjects withwide ranges, offering a realm of approaches and methods. Inmany practical industrial projects, most knowledge is availablein the form of heuristic rules achieved from rich experience ina variety of processes. Until now, the successful application ofanammox in practical projects is still facing huge challenges.The kinetics involves operational and environmental factors,affecting the utilization rates of the substrates. By means of thekinetics evaluation, the optimization on the plant design andprediction of the performance of treatment plants can be con-ducted,14,33 and the process models relating to the anammoxprocess can be further discussed (Table 3).11,14,34–38 Furthermore,the anammox enrichment culture can be signicantly promotedbecause the kinetics can provide a convincing basic recipe fordealing with the operational and environmental factorsaffecting the performance of substrate removal.11 It is wellknown that many mathematical models, such as rst-ordersubstrate removal models, Grau second-order substrateremoval models, Stovere–Kincannon models, and Monodmodels are widely adopted in the eld of wastewater treatment(Table 3). For example, the rst-order and second-ordersubstrate removal models are effective for estimating thekinetic constants in anammox processes.14,39,40 In addition, theMonod model was initially applied to represent the growth ofmicroorganisms, and widely employed to express the degrada-tion kinetics. Moreover, the Stovere–Kincannon model is one ofthe most expensively developed mathematical models for

13 substrate consumption (c),45 and the model comparison (d).15,45

This journal is © The Royal Society of Chemistry 2020

Tab

le4

Comparisonofkineticmodelsap

plie

dto

anam

moxreac

tors

Mod

els

Reactor

Inoculum

NLR

(gL�

1d�1)

HRT(d)

Con

stan

tsof

mod

els

R2

Referen

ce

First-order

mod

elUp

owlter

Culturedactivatedslud

ge0.93

–7.34

0.08

–0.6

0.44

0.17

269

ANMR

Floccu

lentan

ammox

slud

ge0.10

7–0.74

60.6–3.0

5.31

0.70

735

UASB

Activated

sludg

e0.15

–2.8

0.2–3.2

11.6

0.80

416

MAR

Culturedan

ammox

slud

ge0.08

–1.94

0.25

–1.0

7.44

0.75

611

UASB

Anam

mox

gran

ules

167–27

80.2–3.2

11.64

0.80

4349

191–41

1UASB

Activated

sludg

e0.93

–7.34

1.5–12

0.45

80.43

13UASB

Anaerobicdigestionslud

ge0.93

–7.34

1.5–12

0.56

10.04

13UASB

0.93

–7.34

1.5–12

0.79

80.18

13a

bGrausecond-orde

rmod

elUp

owlter

Culturedactivatedslud

ge0.93

–7.34

0.08

–0.6

1.4

0.96

40.98

569

ANMR

Floccu

lentan

ammox

slud

ge0.10

7–0.74

60.6–3.0

0.10

51.11

0.99

535

UASB

Activated

sludg

e0.15

–2.8

0.2–3.2

0.09

361.03

0.99

916

MAR

Culturedan

ammox

slud

ge0.08

–1.94

0.25

–1.0

0.05

541.13

60.99

111

AUF

Synthetic

med

ium

10.1–1.99

1.39

70.96

412

ANMR

Floccu

lentan

ammox

slud

ge11

5–29

70.6–2.9

0.10

541.11

010.99

5414

153–31

3UASB

Anam

mox

gran

ules

167–27

80.2–3.2

0.09

361

1.02

870.99

8515

191–41

1Activated

slud

ge0.93

–7.34

1.5–12

0.08

71.13

0.93

13UASB

Anaerobicdigestionslud

ge0.93

– 7.34

1.5–12

0.05

11.14

0.98

130.93

–7.34

1.5–12

0.09

11.20

0.97

13Rm

KB

Mod

ied

stover–

Kincannon

mod

elUp

owlter

Culturedactivatedslud

ge0.93

–7.34

0.08

–0.6

12.4

120.97

969

ANMR

Floccu

lentan

ammox

slud

ge0.10

7–0.74

60.6–3.0

7.89

8.98

0.99

935

UASB

Activated

slud

ge0.15

–2.8

0.2–3.2

11.4

12.1

0.99

916

MAR

Culturedan

ammox

slud

ge0.08

–1.94

0.25

–1.0

6.41

7.37

0.99

311

AUF

Anam

mox

slud

ge10

.1–1.99

1212

.40.97

912

ANMR

Floccu

lentan

ammox

slud

ge11

5–29

70.6–2.9

8.98

7.89

0.99

8614

UASB

Anam

mox

gran

ules

167–27

80.2–3.2

12.1

11.4

0.99

915

191–41

1Activated

slud

ge0.93

–7.34

1.5–12

0.89

21.01

90.94

13UASB

Anaerobicdigestionslud

ge0.93

–7.34

1.5–12

11.11

00.98

130.93

–7.34

1.5–12

3.33

4.03

70.98

13Rm

Ks

Mon

odmod

elANMR

Floccu

lentan

ammox

slud

ge0.10

7–0.74

60.6–3.0

0.16

90.19

20.59

935

EGSB

Anam

mox

gran

ules

0.76

–22.87

0.06

–0.33

0.63

20.20

80.98

675

UASB

Activated

slud

ge0.15

–2.8

0.2–3.2

0.22

50.09

20.77

316

MAR

Culturedan

ammox

slud

ge0.08

–1.94

0.25

–1.0

0.95

20.10

70.99

311

UASB

Anam

mox

gran

ules

167–27

80.2–3.2

0.22

460.09

240.77

2515

191–41

1UASB

Activated

sludg

e0.93

–7.34

1.5–12

——

—13

Anaerobicdigestion

slud

ge

This journal is © The Royal Society of Chemistry 2020 RSC Adv., 2020, 10, 39171–39186 | 39181

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Table 5 Most popular kinetic models used for the activity simulationa

Simulation equations Reference

q ¼ Sqmax

Ks þ S þ S2

KI

76

q ¼ qmax

�exp

�� S

KI

�� exp

�� S

KS

��77

q ¼ qmax

�1� exp

�� S

KS

��78

r ¼ Srmax

Ks þ S þ S2

KIH

79

r ¼ Srmaxð1� S=SmÞnKs þ S

80

r ¼ Srmaxð1� S=SmÞnS þ KsðS=SmÞm

81

r ¼ Srmax

Ks þ Sexp

�� S

kip

�82

a Where q is the specic substrate conversion rate constant (d�1); qmax isthe maximum specic substrate conversion rate constant (d�1); KS is thehalf saturation constant (mg N L�1); KI is the inhibition constant (mg Nper L); KIH is the inhibition constant of Haldane (mg N per L); kip is theinhibition constant of Aiba; rmax is the maximum specic activity (mgL�1); Sm is the maximum removal efficiency (mg L�1 d�1).

Fig. 4 SAA kinetic simulation of the EGSB-anammox biomass (a–f)13 and(h), and NF-nitrifying system (i).43

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determining the kinetic constants in immobilized bioreactors.Also, it was reported that the Stovere–Kincannon model hasbeen investigated in continuously operated mesophilic andthermophilic upow anaerobic lters for the treatment ofsoybean wastewater, paper pulp liquors, and simulated starchwastewater, and the determination of kinetic constants ina packed bed reactor for decolonization.11

A detailed comparison of different substrate removal kineticmodels for anammox reactors is listed in Table 4. Comparedwith the various processes conducted in the UASB, MAR, ANMRand up-lters, the result of the rst-order simulation showedthat the UASB had the highest K1 of 11.64 (d�1) with R2 being0.80, and the up-lter had the lowest K1 of 0.43 (d�1) with R2

being 0.439.12,15 All of the rst-order simulations proved that R2

was low, indicating the poor tting of the data. While these datasimulated by second-order simulation proved a higher R2,which was 0.99 or 0.98, respectively. Meanwhile, the modiedStover–Kincannon also gave a high R2 of 0.98 in a UAF system.12

In the UASB system, among the most commonly used modiedStover–Kincannon model, Rmax varied from 15 to 476, as re-ported due to the different operational conditions.41,42 Thedifference in the Umax values was probably due to the differentadopted reactor congurations, different treated wastewatercharacteristics and developed microorganisms in variousstudies. The Stover–Kincannon model was suitable for thekinetic analysis of a complicated anaerobic process. This ispartially due to the simplication of this model without diffu-sion of the modeling substrate, hydraulic dynamics and other

the different systems of anammox (g), DMX-deammonification system

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parameters, which might be key factors for the reactor perfor-mance, but were difficult to measure. The methods of validityassessment between the experiment and prospect can be con-ducted by the linear regression equation, y ¼ x (Fig. 3). Themodied Stover–Kincannon model and the Grau second-ordermodel are both practical because in the case that Umax isequal to kB, the Grau second-order model can be transformed tothe modied Stover–Kincannon model. Thus, the total nitrogenconcentration in the effluent predicted by the Grau second-order and modied Stover–Kincannon models may displayhigh correlation with actual measured data. Conversely, theMonod model and the rst-order model were proven to be notfeasible in many cases. The Grau second-order model and themodied Stover–Kincannon model were both acceptable topredict the total nitrogen concentration in the effluent sincetheir plot lines nearly coincided with the actual line (Fig. 3). Theprocess kinetic models also illustrated that the substrateconcentration and granule size should be carefully controlled asthe main process parameters. Different types of kinetic modelsshould be used to obtain the appropriate model. For the activitytest simulations, most popular kinetic models used for theactivity simulation are shown in Table 5. The specic anammoxactivity (SAA) kinetic simulation of the EGSB-anammox biomass(a–f)13 and the differences between the anammox (g), DMX-deammonication system (h), and the NF-nitrifying system(i)43 were compared and veried (Fig. 4). Other simple kineticASM1 models for granular biomass oatation were also inves-tigated.44 Among the kinetic models, the basic Monod equationfor the DMX process, the extended Edwards and Luong equa-tion for the AMX process, and the Andrews equation for the NFprocess were used and validated to be the most appropriateequations for the process.

A simplied kinetic model lacks the capacity of expressingthe detailed mechanism, but it can be used to properly describethe main characteristics of a reaction. A good model not onlyprovides a better understanding of the complex biological andchemical fundamentals, but is also fundamental for the processdesign, start-up, dynamics predictions, and process control andoptimization. Obviously, the simplied models with limitedvariables, which are conrmed suitable for practical engi-neering applications, were proved to be powerful tools foranammox at low growth rates. Although different kineticmodels were investigated to describe the anammox processcarried out in different reactors, the modied Stover–Kincan-non and Grau second-order models seemed to be the mostsuitable for describing nitrogen removal.4,12,37 However, thesemodels still had problems since the limitations of these kineticmodels cannot describe cellular metabolism and regulation.Moreover, these models did not give any insight to the variablesthat could inuence cell growth. In addition, these models shallbe further modied when the special phase of the process wassimulated to get the actual kinetics. More analysis methods andtechnologies for a more in-depth study shall be extensivelycarried out with the purpose of improving the understanding ofthe reaction mechanisms and microbial community dynamics.

This journal is © The Royal Society of Chemistry 2020

6. Conclusions

In the review, the process-kinetics of different anammoxcongurations with process performance was investigated.Among the congurations, SBR, UASB and MBR were the mostcommonly used and favored for bacterial accumulation. Thesimulation models of the kinetics were proved as valuableinformation for process construction and rector operation. Thekinetic model validation proved that the modied Stover–Kin-cannon model and Graus second-order model could be appro-priate for different reactor anammox processes. Rather than thecorrelation coefficient, the validation revealed that the modiedStover–Kincannon model was more appropriate for nitrogenremoval kinetics in most congurations. Based on the simula-tion of correlated indexes, the activity kinetic models illustratedthat the modied Han–levenspiel, Luong and Andrews modelswere the most appropriate kinetic models for the activitysimulation in the anammox process. The appreciate congu-rations could improve the denitrication efficiency, andcontribute to the biomass enrichment in the projects of engi-neering application.

Conflicts of interest

There are no conicts of interest to declare.

Acknowledgements

This work was supported by the National Natural ScienceFoundation of China (Grant No. 51608304) and the Scienticand Technological Projects of Henan Province (Grant No.172102210420).

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