Issues with P supply

Cordell et al., (2009)

Story of N

Matassa et al., (2015)

Recovery of P

Enabling Enhanced Recovery

ADM1

• Published 2002 (presented AD9) • Aggregation of previous approaches • Generalised model • Cited >1200 times • Over 500 sold • Widely available • Cited in >60% of AD modelling papers

How have needs changed

• Renewable energy now embedded in economy. • AD emerged as key method for organics

valorisation • New technology developing (particularly high

solids) • Emerging science utilises anaerobic processes in

new way • Nutrient recovery now a reality

Emerging Processes

liquid

gas

Microbial

Physicochemical

gas

CH4

CO2 H2OH2

NH3, AcH, ProH, BuH, ValH, LacH, CO2EtOH, others...

gasCH4

death/decay

H2

NH4+, Ac-, Pro-, Bu-, Val-, Lac-, HCO3

-

inerts

proteins carbohydrates lipids

growth

microbialbiomass

AA MS

HAc

LCFASO4

-

SH2SH-

NOx-

N2

SH2

Me n+

Me (n-1)+

MeS(n-2)

IRh

S0

composites

?

, Pror H, BuH, ValH,,,, LaLaLaLacH, CEtOHOHOHHH, ottotothehehehersrsrsrs...

SOSOSSOSS 4-----

SHSS 222

S0000OOOOOOOO222222

MMMeMMM n+n+n+n+n+n+

Me (n-1)+

MeMMMM S(n-2)))))

M

SS0000

NOx-

N2

IRIRh

cocococococom

INFLUENT WASTEWATER

ACTIVATED SLUDGE REACTORSSECONDARY

CLARIFIER

THICKENER

ANAEROBIC DIGESTER

DEWATERING

STORAGE TANK

BYPASS

EFFLUENT WATER

SLUDGE REMOVAL

ASM/ADMINTERFACE

ADM/ASMINTERFACE

GAS

STORAGE/HYDROLYSIS

OXIDATION

REDUCTION

PRECIPITATION

Fe+3 Fe+2

SO4-2 S-2

PO4-3 KMgPO4

Fe+2 Fe+3

S-2 SO4-2

PO4-3 FePO4

PRIMARYCLARIFIER

KMgPO4 PO4-3

Fe+3 Fe+2

SO4-2 S-2

S-2 FeSPO4

-3 Ca3(PO4) 2

MgNH4(PO4)

Challenges in P modelling

PCM1 • Identify generally applicable modelling

framework for PCM • Aims to extend ASM/ADM1 for common

basis • Focus on enabling plant wide phosphorous

prediction • Enables ancillary processes (S, Fe biological

transformations). • Approach rather than application focus.

Reactions in PCM White box or black box to user

Inside the box (Solution) • AE system very large (20 components, 118

species) • Can reduce no of algebraic variables by implicit

combination, but increases non-linearity • Gradient search technique (analytical Jacobian)

used so far • Key remaining issue is easy

modification/extension

Precipitation – outside the box

• Kazadi Mbamba (2015a,b) • Follows nth order approach in SI:-

Kazadi Mbamba et al., (2015a) Water Research

Kazadi Mbamba et al., (2015a) Water Research

Kazadi Mbamba et al., (2015a) Water Research

kStruv = 12 (±9) h-1

kCCM = 0.35 (±0.03) h-1

kLa = 5.5 (±0.49) h-1

ParameterPiggery

digestate

Ammonia (gNH4-N. m-3) 983

Calcium (g.m-3) 207

Magnesium (g.m-3) 126

Phosphorus (gP. m-3) 96

Inorganic carbon (g.m-3) 1150

pH 7.3

kStruv = 4.6 (±0.7) h-1

kACP = 3 (±0.5) h-1

kCCM < 0.22 h-1

ParameterSludge

digestate

Ammonia (gNH4-N. m-3) 1010

Calcium (g.m-3) 107

Magnesium (g.m-3) 49

Phosphorus (gP. m-3) 84

Inorganic carbon (g.m-3) 945

pH 7.4

Kazadi Mbamba et al., (2016) Water Research

Impact of Precip/PCM

Impact on final effluent

Beyond Activated Sludge • How to partition CPNK fully? • A-stage C,P only • Algae need light & CO2 • Purple bacteria need IR light&COD

0

5

10

15

20

25

30

35

40

45

0

100

200

300

400

500

600

700

0 5 10 15 20Time (hours)

PO4-

P an

d NH

4-N

(mg

L-1)

VFA-

COD

(mg

L-1)

HAc added SCOD

PO4-P

NH4-N

VFA-COD

Huelsen et al., (2014) Water Research

Poly-P without Carbon

Gas Phase for Single Cell Protein

Matassa, S., Batstone, D.J., Hülsen, T., Schnoor, J. and Verstraete, W. (2015) Can direct conversion of used nitrogen to new feed and protein help feed the world? Environmental Science and Technology 49(9), 5247-5254.

Using Models-CBA

NPV negative

‘maybe now’

‘probably later’‘maybe later’

‘probably never’

‘invest never’

value-to-cost, S/X

NPV positive1.0

vola

tility

(%)

LEM

Photo-bioreactor

Co-digestion

‘invest now’

-50%

-40%

-30%

-20%

-10%

0%

10%

0 0.5 1 1.5 2 2.5 3

A-stage

Struvite

https://www.atse.org.au/content/publications/reports/natural-resources/wastewater-an-untapped-resource.aspx

Challenges and Opportunities

• Model translation and dissemination • Develop new enhanced recovery techniques

(and apply them) – model based prototyping • Use models in new ways – e.g., economic

analysis, feasibility analysis • Model validation, particularly biology • Identify and develop new products –

chemical and biological