dea - università degli studi di brescia multi-objective optimization to select effective pm10...
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DEA - Università degli Studi di Brescia
Multi-objective optimization to select effective PM10 control policies in
Northern ItalyC. Carnevale, E. Pisoni, M. VoltaDipartimento di Elettronica per l’Automazione
Università degli Studi di Brescia, Italy
DEA - Università degli Studi di Brescia
Methodology: research aim
To develop a secondary pollution control plan:• Multi-objective optimization:
– Objective 1: Air Quality Index (AQI)– Objective 2: Internal Costs (C)
• for a mesoscale domain– Milan CityDelta domain (Northern Italy)
DEA - Università degli Studi di Brescia
Methodology: multi-objective problem
Jmin
emission reduction costs
PM exposure index (meanPM)
set of the feseable solutions
decision variables: reduction of the precursor emissions
)( CJ
DEA - Università degli Studi di Brescia
ji
D
d
djiDJI , 1
,11
spdjiE
,,,
daily (d) cell (i,j) precursor (p) emissions for CORINAIR sectors (s) for the basecase scenario
PpSs
sp , decision variable set: precursor (p) reduction for
CORINAIR sector (s)
spdji
dji E ,,
,,
domain yearly mean PM exposure (g/m3):
source-receptor models
PMNHSOxNOVOCP ,3,2,,
111, SSS
PM precursors
CORINAIR sectors (s)
Methodology: Obj 1 - the Air Quality Index: ()
DEA - Università degli Studi di Brescia
Methodology: Obj 2 - emission reduction Costs (C)
sp
spspspsp cEC,
,,,,
spspc ,, unit cost curve for precursor (p) and CORINAIR sector (s)
Cost curves used are estimated on the basis of RAINS-IIASA database
An emission reduction cost curve has been assessed for each CORINAIR sector.
DEA - Università degli Studi di Brescia
Case study:domain
300x300km2
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4900
4950
5000
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5150
TORI NO
MI LANO
GENOVA
TRENTO
VERONA
PI ACENZA
MODENA
BRESCI A
VARESE
BERGAMO
SONDRI O
PARMA
NOVARA
ALESSANDRI A
0
200
400
600
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1400
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(m )
Milan domain
DEA - Università degli Studi di Brescia
Case study: Obj 1: AQI
• Pollutant concentration are computed by 3D deterministic chemical transport multiphase modelling system – Time consuming
• Identification of source-receptor models (Neuro-fuzzy Networks), describing the nonlinear relation between decision variables (emission reduction) and air quality objective, processing the simulations of TCAM
DEA - Università degli Studi di Brescia
Case study: Obj 1 - GAMES
Continental modeloutput
Land useTopography
DiagnosticModel Output
LocalMeasurements
RAMS-CALMETMeterological
Model
TCAM
Initial andBoundary condition
Pre-processors
3D concentration fields
POEM-PMEmission
Model
Radiosounding
Emissioninventories
Emission Fields
3D meteo fields
VOC, PM speciation
Profiles
TemporalProfiles
SystemEvaluation
Tool
IC, BC
DEA - Università degli Studi di Brescia
Case study: Obj 1 - TCAM model
• gas phase chemical mechanisms: SAPRC90, SAPRC97, COCOH97, CBIV
• 21 aerosol chemical species• 10 Size classes
– Size varying during the simulation– Fixed-Moving approach
• processes involved:– Condensation/Evaporation– Nucleation– Aqueous Chemistry
Shell
Core
DEA - Università degli Studi di Brescia
Case study: Obj 1 - TCAM simulations
• base case simulation:– 300 x 300 km2, 60 x 60 cells, cell resolution: 5x5 km2 – 11 vertical layers– emission and meteorological fields: JRC (CityDelta Project)– initial and boundary conditions: EMEP– simulation takes several days of CPU time– simulation period: year 1999
• alternative scenario:– CLE: current legislation– MFR: most feasible reduction
emission scenario
precursor Base case
[ton/year]
CLE
[%]
MFR
[%]
NOx 466 803 -29.79 -44.50
VOC 718 087 -38.16 -58.74
SOx 714 796 -77.49 -90.64
NH3 172 389 0.51 -35.12
PM10 176 726 -39.65 -77.19
DEA - Università degli Studi di Brescia
Case study: Obj 1 - SR models
• 4-layer NF architecture– Number of MF for input: 2– Number of rules: 25=32– Nodes of hidden layer: 8
• Input data: daily NOx,VOC, PM10, NH3, SOx emissions (CDII)
• Target data: daily PM10 concentration computed by the GAMES system (CDII)
DEA - Università degli Studi di Brescia
Case study: Obj 1 - SR models
• Identification of a neural network for each group of 6x6 TCAM grid cells
400 450 500 550 600 650
4900
4950
5000
5050
5100
5150
MI LANO
GENOVA
TRENTO
VERONA
PI ACENZA
MODENA
BRESCI A
VARESE
BERGAMO
SONDRI O
PARMA
NOVARA
ALESSANDRI A
0
200
400
600
800
1000
1200
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1600
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2000
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2600
2800
3000
DEA - Università degli Studi di Brescia
Case study: Obj 1 - SR models validation
BIAS Scatter Plot
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MI LANO
TRENTO
VERONA
PI ACENZA
MODENA
BRESCI A
VARESE
BERGAMO
SONDRI O
PARMA
NOVARA
ALESSANDRI A
-0 .20
-0.15
-0.10
-0.05
-0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0 5 10 15 20 25 30 35 40 45 500
5
10
15
20
25
30
35
40
45
50
PM10 simulated by TCAM 3d model
PM
10 s
imul
ated
by
NF
sys
tem
y=2x
y=x
y=0.5x
DEA - Università degli Studi di Brescia
Case study: Obj 2 - Cost functions
• Fitting the costs of the available technologies:
– considering 2nd order polynomial functions– with the constraint of estimating a monotonically increasing
and convex function.
y = 11419x2 - 182,13x + 380,88
0
500
1000
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0% 10% 20% 30% 40%
un
it c
os
t (K
€)
NOx, sector 3:
DEA - Università degli Studi di Brescia
Case study: optimization problem solution
• Weighted Sum Method
• Constraints1. Maximum Feasible Reductions:
2. Technologies reducing both precursors
))()1()((min
C
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11
0.00 0.54 0.00 0.19 0.49 0.33 0.47 0.61 0.06 0.00 0.00
0.31 0.22 0.46 0.29 0.00 0.00 0.29 0.25 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.35 0.00
0.00 0.59 0.09 0.40 0.00 0.00 0.41 0.39 0.69 0.00 0.00
0.00 0.14 0.08 0.75 0.00 0.00 0.18 0.14 0.00 0.00 0.00
sNOxR ,
sVOCR ,
spsp R ,,0
sNHR ,3
sPMR ,
sSOR ,2
DEA - Università degli Studi di Brescia
Case study: Results (pareto boundary)
Optimisation performed only on the 50% cells with highest mean PM concentration
DEA - Università degli Studi di Brescia
Case study: Results (VOC)
road transport (7), resid. combustion plants (2)
road transport (7)
DEA - Università degli Studi di Brescia
Case study: Results (NOx)
industrial combustion (3), public power plants (1), production processes (4)
road transport (7), public power plants (1), production processes (4)
DEA - Università degli Studi di Brescia
Case study: Results (PM)
waste treatment (9), production processes (4), other mobile sources (8)
road transport (7), production processes (4), other mobile sources (8)
DEA - Università degli Studi di Brescia
Case study: Results (SO2)
production processes (4), road transport (7), other mobile sources (8)
production processes (4),
DEA - Università degli Studi di Brescia
Conclusions
– A procedure to formulate a multi-objective analysis to control PM exposure has been presented;
– The procedure implements neuro-fuzzy networks tuned by the outputs of a deterministic 3D modelling system;
– The methodology has been applied over Milan CityDelta domain (Northern Italy): a strong reduction of 70% of air qualiy index can be attained with only 15% of maximum costs
DEA - Università degli Studi di Brescia
Current activities
– Uncertainty analysis:• cost curves• NOx/VOC and NOx/PM reduction functions for transport sectors• sensitivity of source-receptor models to NH3 emission reduction
– CityDeltaIII simulations to extend source-receptor model calibration and validation sets;
– source-receptor models for mean PM10 and PM2.5 concentrations: spatial resolution 10x10km2;
– PM2.5 two-objective optimization– Ozone and PM10 two-objective optimization