Interlinkages between operational conditions and direct and...
Transcript of Interlinkages between operational conditions and direct and...
Università di PalermoDipartimento di Ingegneria Civile, Ambientale, Aerospaziale, dei Materiali (DICAM)
Athens
14-16 September 2016
Interlinkages between operational
conditions and direct and indirect
greenhouse gas emissions in a moving bed
membrane biofilm reactor
G. Mannina, M. Capodici, A. Cosenza, D. Di Trapani
Introduction
Wastewater treatment entails:
• direct emissions of greenhouse gases (GHGs), such as
nitrous oxide (N2O)
• indirect emissions resulting from power requirements
N2O Unwanted even at small levels due to the high global
warming potential 310 higher than CO2
Introduction
reduction of NO2- as terminal
electron acceptor to N2O (AOB
denitrification)
incomplete oxidation of
hydroxylamine (NH2OH) to NO2
intermediate of the incomplete
heterotrophic denitrification
N2O Production Pathways
Nitrification Denitrification
Introduction
Process operations aimed at the reduction of N2O could
conflict with the effluent quality and increase the operational
costs
Challenge
Operationalcosts GHG
emissionEffluentquality
To identify GHG mitigation strategies as trade-off between operational costs
and effluent quality index is a very ambitious challenge
Aim
Performing a multivariate analysis
+
University Cape Town (UCT) moving bed (MB) membrane
bioreactor (MBR) pilot plant.
Simple model for interlinkage among operational conditions/influent
features/effluent quality and emitted N2O.
Methods
Pilot plant
Anaerobic Tank Anoxic Tank Aerobic Tank
MBR Tank
Clean In Place Tank
ODR
Qin
Qout
QR2
QR1
QRAS
Gas
Funnel
Gas
Funnel
Gas
Funnel
Gas
FunnelSuspended
Carriers
QIN = 20 L h-1
QR1 = 20 L h-1
QRAS = 80 L h-1
150 days of experimentation
Mixture of real and synthetic
wastewater!
Three experimental phases:
Phase I: SRT = ∞
Phase II: SRT = 30 days
Phase III: SRT = 15 days
QR2 = 100 L h-1
QOUT = 20 L h-1
Pilot plant
PURON 3 bundle ultrafiltration module (pore size
0.03 μm, surface 1.4 m2)
AMITECH carriers in anoxic and aerobic
reactors with a 15 and 40% filling fraction
respectively
TSS, VSS, CODTOT, CODSOL, N-NH4,N-NO3, N-NO2,
TN, TP, P-PO4, DO, pH, T,
N-N2O as gas and dissolved
Two time per week in each tank
Measured data
Indirect emissions
Pw [kWh m-3] energy required for the aerationPeff [kWh m-3] energy required for permeate extractiongpower,GHG geconversion factors, 0.7 gCO2eq and 0.806 € kWh-1
EF [€m-3] cost of the effluent fine including N2O
The Operational Costs (OCs) were evaluated using conversionfactors (Mannina and Cosenza, 2015 ):
Indirect emissions
QIN and QOUT are the influent and effluent flow, respectively;Daj is the slope of the curve EF versus Cj
EFFwhen CjEFF< CL,j (in this
case, the function Heaviside =0);Dbj represents the slope of the curve EF versus Cj
EFFwhen CjEFF> CL,j
(in this case, the function Heaviside =1);b0,j are the increment of the fines for the latter case.
The effluent fine (EF) was evaluated using:
EF =1
t2 - t1×
1
QIN
×QOUT ×Da j ×Cj
EFF + QOUT( )×
b0, j + Cj
EFF -CL, j( )×Db j - Da j( )××
××
×
×
××
×
×
×××Heaviside×Cj
EFF -CL, j( )( )j=1
n
××
×
××
×
×
××
×
×
××
×
×
××
t1
t2
× ×dt
Indirect emissions
bCOD, bTN, bPO, bN2Ogas and bN2O,L are the weighting factors of theeffluent CODTOT, TN, PO, liquid N2O in the permeate and gaseousN2O.
The effluent quality index (EQI) was evaluated using:
EQI =1
T×1000×
bCOD ×CODTOT + bTN ×TN + bPO×PO+
bN2Ogas×N2Ogas + bN2O,L ×N2OL
×
××
×
×××QOUT ×dt
to
t1
×
Multiregression analysis
Performed to point out general relationships for the N-N2O
and the plant operation conditions or the available measured
data
Two type of analysis
Complex regressionsSimple linear regression
Simple linear regression
Y = dependent variable; X1 = independent variable; c1, c2 regression coefficients
N2O-N fluxANAER (N2O-N flux emitted from the anaerobic tank)
N2O-N fluxANOX (N2O-N flux emitted from the anoxic tank)
N2O-N fluxAER (N2O-N flux emitted from the aerobic tank)
N2O-N fluxMBR (N2O-N flux emitted from the MBR tank)
N2O-N dissolvedOUT (N2O-N permeate dissolved concentration)
Dependent variables
Complex regressions
Y = dependent variable; X1,…,Xm= independent variable; c1,…,cn regression coefficients
∑N2O-N flux (sum of the N2O-N flux emitted from each tank)
N2O-N dissolvedOUT (N2O-N permeate dissolved concentration)
Dependent variables
Multiple linear (LINm)
Multiple exponential (EXP)
Sum of exponential
(SumEXP)
Independent variables Influent concentration
Effluent concentration
Intermediate concentration
N-NO2_AER, N-NO2_ANOX, DOAER, DOANOX, pHAER, pHANOX, DOMBR
CODTOT,OUT, BOD5,OUT, N-NH4,OUT, N-NO3,OUT, NO2-N,OUT, P-PO4,OUT
CODTOT, IN, N-NH4,IN, PTOT,IN, P-PO4,IN, C/N
hCOD,BIO, hCOD,TOT, hNITR, hDENIT, hNTOT, hP
Performance indicators
Operational conditions
TSS*, SRT, Biofilm*
Numerical settings
10,000 Monte Carlo simulations varying coefficients
Evaluation of Nash and Sutcliffe efficiency for each simulation
Ymeas,i = measured value of the ith dependent state variable; Ysim,i = simulated
value of the ith dependent state variable; Yaver,meas,i = average of the measured
values of the ith dependent state variable
Results
Simple linear regression analysisMaximum efficiency
Dependent variables
N2O-N
fluxANAER
N2O-N
fluxANOX
N2O-N
fluxAER N2O-N fluxMBR N2O-N dissolvedOUT
Phase
I
Independent
variableTSS NO2-NANOX NO2-NANOX NH4-NIN NO3-NOUT
Efficiency 0.11 0.52 0.52 0.2 0.1
II
Independent
variableNO2-NANOX NO2-NANOX DOAER hNITR CODOUT
Efficiency 0.35 0.6 0.5 0.26 0.72
III
Independent
variablepHAER Biofilm Biofilm PO4-POUT NO2-NAER
Efficiency 0.12 0.36 0.67 0.52 0.94
Varying the SRT different variables can be adopted to
predict the N2O
N2O dissolved in the permeate
depend on CODOUT
NO2 accumulation influence the N2O
production
Simple linear regression analysis
0 0.002 0.004 0.006 0.008Parameter- c2
N2O-N dissolvedOUT
(f)
-19 -18.5 -18 -17.5 -17Parameter- c1
N2O-N flux AER
(d)
-6.5 -6 -5.5 -5 -4.5Parameter- c1
N2O-N flux ANOX
(b)
0.0
0.1
0.2
0.3
0.4
9 9.5 10 10.5 11
Effic
iency [
-]
Parameter- c2
N2O-N flux ANOX
(a)
0.4
0.5
0.6
0.7
34 34.5 35 35.5 36
Effic
iency [
-]
Parameter- c2
N2O-N flux AER
(c)
0.0
0.2
0.4
0.6
0.8
1.0
0 0.02 0.04 0.06
Effic
iency [
-]
Parameter- c1
N2O-N dissolvedOUT
(e)
Scatter plots Phase III
High dependence Poor dependence
Combining effect
Complex multiregression analysisLINm - Maximum efficiency
∑N2O-N flux N2O-N dissolvedOUT
Efficiency Efficiency
Independent
variable0.015 0.244
C/N
N-NH4,IN
TSS
Biofilm
SRT
DOAER
N-NO2_AER
pHAER
DOANOX
N-NO2_ANOX
pHANOX
LINm poorly reproduces the measured
data for ∑N2O-N flux (efficiency 0.015).
Efficiency obtained for the N2O-N
dissolvedOUT is slightly higher than for
∑N2O-N flux (equal to 0.244)
Complex multiregression analysisEXP and SumEXP - Maximum efficiency
EXP SumEXP
∑N2O-N flux N2O-N dissolvedOUT ∑N2O-N flux N2O-N dissolvedOUT
Efficiency Efficiency Efficiency EfficiencyIndependent
variable 0.125 0.164 0.198 0.178C/N
C/N
N-NH4,IN
N-NH4,IN
TSS
TSS
Biofilm
Biofilm
SRT
SRT
DOAER
DOAER
N-NO2_AER
N-NO2_AER
pHAER
pHAER
DOANOX
DOANOX
N-NO2_ANOX
N-NO2_ANOX
pHANOX
pHANOX
Poor efficiency values obtained
for both the investigated
dependent variables
Conclusions
Reasonable agreements for simple regression equations
Dependency of N2O flux with SRT and plant sections
SRT of Phase III makes the conditions of N2O production
more sharped
None of the investigated equations for complex multivariate
analysis is able to provide satisfactory efficiencies
Message to take home!
The interactions among the key factors affecting the
N2O make difficult to establish an unique equation valid
for different operational conditions for predicting N2O
Università di PalermoDipartimento di Ingegneria Civile, Ambientale, Aerospaziale, dei Materiali (DICAM)
Athens
14-16 September 2016
Thank you for your attention
Giorgio [email protected]
Acknowledgements
This research was funded by Italian Ministry of Education, University and Research (MIUR) through the
Research project of national interest PRIN2012 (D.M. 28 dicembre 2012 n. 957/Ric − Prot. 2012PTZAMC)
entitled “Energy consumption and GreenHouse Gas (GHG) emissions in the wastewater treatment
plants: a decision support system for planning and management − http://ghgfromwwtp.unipa.it”
PRIN Research project of
national interest
2012
Green House Gases emissions from Wastewater
Treatment Plants
Università di PalermoDipartimento di Ingegneria Civile, Ambientale, Aerospaziale, dei Materiali (DICAM)
Athens
14-16 September 2016
FICWTMOD2017 - Frontiers International Conference on wastewater treatment and modelling
1st International Conference
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www.ficwtmod2017.it