Energy and greenhouse footprints of Wastewater Treatment ... · 8/21/2009 · urban water security...
Transcript of Energy and greenhouse footprints of Wastewater Treatment ... · 8/21/2009 · urban water security...
Urban Water Security Research Alliance
ENERGY AND GREENHOUSE FOOTPRINTS OF WASTEWATERFOOTPRINTS OF WASTEWATER
TREATMENT PLANTS INSOUTH-EAST QUEENSLANDSOUTH-EAST QUEENSLAND
David de Haas & Paul LantLCA project
17 or 18 August 2009
ContentsContents1. Introduction
Wastewater Treatment Energy & GHG – Why? Urban Water Security Research Alliance in SE Queensland
2. Methodology Study Area Study Area Type of WWTPs & size Data inventory Calc lation methods Calculation methods
3. Results What emissions dominated? Scopes 1, 2 & 3 Uncertainty analysis
4. Conclusions What are the trends?
IntroductionIntroduction
Why calculate WWTP greenhouse ?gas?
Use ~ 5 to 10 W/EP (~500 to 1000 kWh/ML) electricity( ) y Can be a significant part of local government total GHG
footprint (~40 to 50%) Potential emissions up to:
~0.8 kg CO2-e per day per EP as methane
~0.3 kg CO2-e per day per EP as nitrous oxide
?? kg CO2-e per day per EP as carbon dioxide (non-biogenic)
NGERS (2007) – Australian Federal govt. requires reporting (effective July 2008) when exceeding certain th h ldthresholds
NGERS ThresholdsNGERS Thresholds
WWTP ~200,000 to 300,000 EP
Life Cycle AssessmentLife Cycle Assessment
•• Research alliance objective : LCA model ofResearch alliance objective : LCA model ofResearch alliance objective : LCA model of Research alliance objective : LCA model of SEQLD water gridSEQLD water grid Dams Water Treatment & Distribution Wastewater Collection Wastewater Treatment & Disposal (effluent & biosolids)p ( ) Water recycling/ Advanced treatment Desalination
U LCA t l t i f d i i kU LCA t l t i f d i i k•• Use LCA as a tool to inform decision makersUse LCA as a tool to inform decision makers Energy/ greenhouse & secure water supply Environmental impacts / burden Environmental impacts / burden
Previous LCA studiesPrevious LCA studies
• WWTP impacts dominated by operational inputs & outputs (power, chemicals, biosolids etc.)
• Embodied energy (construction/Embodied energy (construction/ demolition) relatively minorE l L di t l 2004 f• Examples : Lundie et al. 2004 for Sydney
Lundie et al (2004) – Sydney WaterLundie et al.(2004) Sydney Water
Lundie et al (2004) – Sydney WaterLundie et al.(2004) Sydney Water
MethodologyMethodology
Inventory data - WWTPsInventory data WWTPs
• South-EastSouth East Queensland
• Approx. total 40 no.Approx. total 40 no. WWTPs
• Surveyed 35 no. yWWTPs
• Beaudesert, Esk, Brisbane
Laidley, Gatton, Kilcoy etc. not included in t dstudy area
Two types of WWTPTwo types of WWTP
TYPE 1TYPE 1 TYPE 2TYPE 2TYPE 1TYPE 1• Primary sedimentation• BNR activated sludge• Anaerobic co-digestion of (primary
• Extended aeration BNR activated sludge
• Some with aerobic digestion of Anaerobic co digestion of (primary + waste activated sludges)
• Biogas collection some with on-site co-generation (electrical
gwaste activaeted sludge
• No biogas/ collection• No co-generation
power & heat)• Most with chemical
supplementation for nutrient removal (N&P)
• Many with chemical supplementation for nutrient removal (N&P)
removal (N&P)
WWTPs surveyed – size summaryWWTPs surveyed size summary
TYPE OF PLANT & DESCRIPTION
No. of plants surveyed of this T
% of TOTAL ADWF treated
for WWTP
% of Effluent
TOTAL N LOAD
(50%ILE) f
Approximate % of effluent
TOTAL P LOAD
(50%ILE) for WWTP
Current ADWF (ML/d) Indicative EP from ADWF, assuming 200 L/EP.d
Two biggest plants – no Type WWTPin
survey
for WWTP
surveyed
WWTP surveyed
Min Max Average Min Max Average
Type 1 – No Cogen 3 8% 7% 5% 5.7 24.1 13.3 29,000 121,000 66,000
gg padvanced P removal
yp g , , ,
Type 1 – Cogen 3 38% 44% 60% 15.1 123.7 63.6 76,000 619,000 318,000
Type 2 - Small 5 0.3% 0.6% 0.4% 0.075 0.46 0.30 400 2,300 1,500
Type 2 - Medium 17 19% 20% 8% 1.5 9.8 5.5 8,000 49,000 28,000
Type 2 - Large: 7 35% 28% 27% 10 4 54 0 24 8 52 000 270 000 124 000Type 2 Large: 7 35% 28% 27% 10.4 54.0 24.8 52,000 270,000 124,000
TOTAL no. of plants surveyed
35 100% 100% 100% 0.1 123.7 14.4 400 619,000 71,000
Effluent N & PEffluent N & P
Type 1 Type 2P ti f l t i
>10
5 to 10
>10 5 to 10<3Total N
Type 1 Type 2Proportion of plants in survey
3 to 5
<3
3to5
mgN/L
>5
3 to 5
>5<1 Total P2 to 5
1t 2
<1
2
1 to 2
Total PmgP/L
1 to 22 to 5
Data collectedData collected
• Flow (dry weather average)( y g )• Influent COD (or BOD), TKN and TP concentrations• Effluent Total N and Total P concentrations • Power consumption & Power generated (on site) from biogas (if any)• Power consumption & Power generated (on site) from biogas (if any)• Chemicals consumption • Biosolids• Screenings & grit • Biogas produced (if any) & methane content (if measured)• Flow of sludge stream(s) treated through anaerobic digestiong ( ) g g• Method of effluent disposal (ocean, estuary, river, wetlands or
irrigation)• Approx. % effluent volume disposed to effluent reuse (e.g. irrigation)Approx. % effluent volume disposed to effluent reuse (e.g. irrigation)
GHG calculationsGHG calculations
• Former AGO workbook methodology inadequate (WSAA, 2007)For WWTPs
• IPCC ‘Tier 3’ or NGERS (2008) Technical Guidelines ‘higher order’• First principles (mass balance approach) • WWTP functional areas (C & N in/out)• WWTP functional areas (C & N in/out)• Fugitive emissions
Raw sewage dissolved CH4 D it ifi ti N O Denitrification N2O Anaerobic digestion CH4 (not combusted) Biosolids CH4 Effluent N2O (receiving water/ environment) Effluent N2O (receiving water/ environment)
• Non-biogenic carbon (CO2)• Biosolids carbon sequestration (?)• ALL SCOPES: 1, 2 & 3 included
Scopes 1 2 & 3Scopes 1, 2 & 3
Scope 2: IndirectpEmissions from electicity consumed butgenerated elsewhere
Scope 3Other indirect emissions resulting from business butfrom business but emission sources are not owned e.g. from supply chain of consumables or Scope 1: Direct
Emissions from fossil fuels or other sources under direct control (e.g. own production process or vehicle fleet).
of consumables or transport of goods purchased
Emission factorsEmission factors
• Sources NGERS (2008) Technical Guidelines (e.g. power, fuels) Simapro® LCA software databases (e.g. chemicals) Literature survey (WSAA, 2007) (fugitive emissions) Other: literature or estimates (e.g. non-biogenic carbon)
• Major uncertaintiesMajor uncertainties Fugitive emissions factors
DN process off-gas N2O : ~500-fold range Biosolids disposal N2O or CH4 : ~5 to 100-fold rangep 2 4 g Effluent N2O : ~up to 1000-fold range
Non-biogenic organic carbon in wastewater (1% to 50%??) Carbon sequestration in biosolids disposed (5 to 25%??)
Uncertainty analysisUncertainty analysisPlant 1
• Monte Carlo simulation (Excel®-based Add-on)
• Input values for uncertain emission factors200,000
250,000
Total GHG emissions, kg CO2-e/d
Minimum, Maximum and ‘Best estimate’ (or Median) Normal distribution simulated in the model from these estimates
• Combined probability from multiple150,000Combined probability from multiple uncertainties100,000
50,000
00% 25% 50% 75% 100%
ResultsResults
GHG emissions resultsGHG emissions breakdown for example plants
GHG emissions resultsAssumptions 8 mg/L raw influent dissolved methane
10 % non-biogenic raw influent organics1% denitrified N as nitrous oxide
124 ML/d
100,000
120,000Raw Sewage Dissolved Methane
Disposal of Effluent
124 ML/d Type 1 + Cogen
54 ML/d T 2
80,000
day
Disposal of Biosolids
Anaerobic digesters/ biogas, incl. combustionDisposal of Screenings & Grit
Type 2
40 000
60,000
kg C
O2-
e/ Disposal of Screenings & Grit
Secondary Treatment Off-gases
Chemical & Fuel Consumption24 ML/d Type 1
20,000
40,000Imported Electrical Power
NGERS (2008) single facility threshold
6 ML/d T 2
0Plant I Plant II Plant III Plant IV
6 ML/d Type 2
Emissions by Scope – Type 1Emissions by Scope Type 1
Plant I: Type 1 (with biogas + cogen.)
37%
20%
8%
Scope 1 N2O20% Scope 1, N2OScope 1, CH4Scope 1, CO2Scope 2Scope 3
17%17%
Plant II: Type 1 (with biogas but no cogen.)
25%
10% Scope 3
14%
Scope 1, N2OScope 1, CH4Scope 1, CO2Scope 2
5%47%
pScope 3125 tonnes/d CO2-e
124 ML/d ADWF
43 tonnes/d CO2-e
24 ML/d ADWF
Emissions by Scope – Type 2Emissions by Scope Type 2
Plant III: Type 2 (Extended aeration, large)
22%
13%
Scope 1 N2OPl t IV T 2 (E t d d ti di )
13%
Scope 1, N2OScope 1, CH4Scope 1, CO2Scope 2S 3
19%17%
Plant IV: Type 2 (Extended aeration, medium)
4%48%
Scope 3
17%
Scope 1, N2OScope 1, CH4Scope 1, CO2Scope 2
5%42%
Scope 394 tonnes/d CO2-e
54 ML/d ADWF
12 tonnes/d CO2-e
7.9 ML/d ADWF
Snapshot of GHG from SE QLD WWTPsSnapshot of GHG from SE QLD WWTPsGHG emissions per day
Total GHG, including lift pumps. Error bars denote combined uncertainties (5%ile & 95%ile) from Monte Carlo simulations180)
120
140
160
(tonn
es C
O2-
e/ d
Potential biosolids C-sequestration
Uncertainties (Min - Max.) incl. in Monte Carlo Simulations:Raw influent dissolved CH4 (1 -30 mg/L CH4)% Raw influent non-biogenic COD (0 - 30%)
Secondary process off-gas N2O (0.01 to 5% of N-denitrified)
40
60
80
100
" G
HG
em
issi
ons Potential biosolids C sequestration
Total Greenhouse Gas emissions
-20
0
20
40
"Bes
t est
imat
e"
Assuming 20% biosolids C sequestered (long term)
Nambo
ur STP
Redcli
ffe W
WTP
Elanora
WW
TP
Marooc
hy S
TPOxle
y STP
Lugg
age P
oint S
TPNud
gee S
TP
Rosew
ood S
TP
Woodfo
rd STP
Karana
Dow
ns STP
Mt Cott
on S
TP
STP: prop
osed
upgra
de
Fairfie
ld STP
Carole
Park S
TPBrib
ie STP
Sunco
ast (B
li Bli)
STPWac
ol STP
Coolum
STP
Capala
ba STP
Clevela
nd S
TP
Wynnu
m STP
Victori
a Poin
t STP
Brenda
le STP
Burpen
gary
East S
TP
Thorne
side S
TP
Goodn
a (Red
bank
) STP
Caboo
lture
South
STP
Beenle
igh W
WTP
Noosa
Coa
stal S
TP
Bunda
mba STP
Murrum
ba D
owns
STP
Sandg
ate STP
Merrim
ac W
WTP
Gibson
Islan
d STP
Coomba
bah W
WTP
Cooroy
ST S B
Go C M
PLANT NAME Data not available for some plants
GHG emissions per ML
4
GHG emissions per MLTotal GHG, including lift pumps. Error bars denote combined uncertainties (5%ile & 95%ile) from Monte Carlo simulations
3
Uncertainties (Min - Max.) incl. in Monte Carlo Simulations:Raw influent dissolved CH4 (1 -30 mg/L CH4)% Raw influent non-biogenic COD (0 - 30%)
Secondary process off-gas N2O (0.01 to 5% of N-denitrified)
Three plants with power
2
HG
em
issi
ons
ML
AD
WF)
generation from biogas
y = 1.9773x-0.093
R² = 0.3295 y = 4.177x-0.278
1
t est
imat
e" G
Hon
nes
CO
2-e/
R² = 0.8951
0"Bes
t(to
GHG emissions for Plant Type : Extended aeration BNR-Act. Slg.GHG emissions for Plant Type : PST/An. Dig./BNR-Act. Sludge. Assuming 20% biosolids C sequestered (long term)
-10.01 0.1 1 10 100
ADWF treated (ML/d)
Potential biosolids C-sequestrationg q ( g )
GHG emissions per ML
4
GHG emissions per MLTotal GHG, excluding lift pumps. Error bars denote combined uncertainties (5%ile & 95%ile) from Monte Carlo simulations
Uncertainties (Min - Max.) incl. in Monte Carlo Simulations:
3
s
( )Raw influent dissolved CH4 (1 -30 mg/L CH4)% Raw influent non-biogenic COD (0 - 30%)
Secondary process off-gas N2O (0.01 to 5% of N-denitrified)
Three plants with power
2
GH
G e
mis
sion
s/ M
L A
DW
F)
Three plants with power generation from biogas
y = 1.9127x-0.101
R² = 0.3509y = 3.3484x-0.235
R² = 0.8979
1
st e
stim
ate"
Gto
nnes
CO
2-e
1
0"Bes (t
GHG emissions for Plant Type : Extended aeration BNR-Act. Slg.
GHG emissions for Plant Type : PST/An. Dig./BNR-Act. Sludge.Potential biosolids C-sequestration
Assuming 20% biosolids C sequestered (long term)
-10.01 0.1 1 10 100
ADWF treated (ML/d) Data not available for some plants
Power consumption per ML
3000
Gross plant power consumption per ML ADWFexcluding lift pumps.
y = 1076.5x-0.167
R² = 0 3705y = 2868.2x-0.364
R² = 0 8953
2500
h/M
L)
R 0.3705 R = 0.8953
1500
2000
w-s
peci
fic (k
Wh
Three plants with power generation
860
10121000
1500
onsu
med
, flo
w
Upper 95% conf. interval
Mean 860708
500
Plan
t pow
er c
o
Plant Power per ML for Plant Type : Extended aeration BNR-Act. Slg.
Pl t P ML f Pl t T PST/A Di /BNR A t Sl d
Mean
Lower 95%
00.01 0.1 1 10 100
P
ADWF treated (ML/d)
Plant Power per ML for Plant Type : PST/An. Dig./BNR-Act. Sludge.
Power consumption vs N removalPower consumption vs. N removal4
GHG emissions per MLTotal GHG, excluding lift pumps. Error bars denote combined uncertainties (5%ile & 95%ile) from Monte Carlo simulations
3
Uncertainties (Min - Max.) incl. in Monte Carlo Simulations:Raw influent dissolved CH4 (1 -30 mg/L CH4)% Raw influent non-biogenic COD (0 - 30%)
Secondary process off-gas N2O (0.01 to 5% of N-denitrified)
Th l t ith
2
G e
mis
sion
sL
AD
WF)
Three plants with power generation from biogas
y = 1.5678x0.0442 y = 1.1903x0.2006
1
st e
stim
ate"
GH
Gto
nnes
CO
2-e/
M
y 1.5678xR² = 0.0163 R² = 0.1191
0
"Bes (t
GHG emissions for Plant Type : Extended aeration BNR-Act. Slg.
GHG emissions for Plant Type : PST/An. Dig./BNR-Act. Sludge. A i 20% bi lid C t d (l t )
-11 10 100
Effluent Total N (mgN/L)
GHG emissions for Plant Type : PST/An. Dig./BNR Act. Sludge.Potential biosolids C-sequestration
Assuming 20% biosolids C sequestered (long term)
ConclusionsConclusions
ConclusionsConclusions
• Useful inventory of operating data from WWTPs in SEQLDy p g• Uncertainty in emission factors highlighted
Fugitive emissions of CH4 & N2O Non-biogenic organics in raw sewage Non biogenic organics in raw sewage Carbon sequestration in biosolids
• Uncertainties appear to influence results in range: ~mean (±20%)• Typical emissions flow specific basis ~1 2 5 tonnes CO e /ML• Typical emissions, flow-specific basis ~1 - 2.5 tonnes CO2-e /ML• Economies-of-scale: lower emissions/ML with increasing plant size•• Type 1Type 1 plants: lower emissions only with power recovery from
bi di ti & bianaerobic digestion & biogas• Trade off with advanced nutrient removal LCA• Need to extend study to full SEQ Water Cycle
Thank you
www.urbanwateralliance.org.au