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PLANNING BY NEURAL NETWORKS OF INTERVENTIONS FOR THE REDUCTION OF OZONE CONCENTRATION IN LOMBARDY. Giorgio Corani Dipartimento di Elettronica ed Informazione - Politecnico di Milano. Outline. Ozone problem in Lombardy Traditional simulation approaches - PowerPoint PPT Presentation
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PLANNING BY NEURAL NETWORKS OF INTERVENTIONS FOR THE REDUCTION OF OZONE CONCENTRATION IN LOMBARDY
Giorgio Corani
Dipartimento di Elettronica ed Informazione - Politecnico di Milano
Outline
Ozone problem in Lombardy
Traditional simulation approaches
A novel approach: use ANN to generate the results produced by traditional deterministic models, greatly shortening the computation times.
A two-objectives problem: ozone reduction (min concentrazioni) and minimization of removal costs (min costi)
Results
Introduction
In the stratosphere (some 30000m over the ground), ozone protects the Earth from dangerous UV radations (see the ozone hole problem)
but in the atmosphere, ozone is dangerous for both humans and crops (ground ozone)
Ozone recommended standards
World Health Organization: prescribes 120 g/m3 on the 8-hours moving average
Quality standard: 200 g/m3 on the hourly mean, to be exceeded no more than once in a month (objective often not met)
Ozone is a secundary polluttant
Polluttats trends in Milan
1990 2000 %
SO2 30 8 - 73%
CO 4.8 1.8 -62%
NOx 270 140 -48%
O3 25 38 +50%
PM10 40(1998)
40 -
Yearly average in Milan(g/m3)
Source: “Rapporto 2001 sulla qualità dell’aria a Milano”
Air quality in Milan and Lombardy
SO2, NOx, CO are well under control; they have been largely reduced over the last 15 years
PM10 and O3 ozone (summer only) constitute instead a major health concern
Problem overview (1)
Ozone formation depends mainly on “precursors”:
NOx mainly due to road transports (76%) and heating (21%)
VOC - volatile organic compounds, such as CO, CH4mainly due to solvent use (44%) and road transport (49%)
Since chemical reactions develop in some hours (or in a few days), ozone values over a certain site are due to NOx-VOC sources located at many km of distance (transport)
Ozone peaks are usually observed in suburban areas
secondary polluttantsecondary polluttant
Problem overview (2) High ozone ground levels concentrations observed since the
70’s in USA and Europe The process takes place only at high temperatures (over 30 C)
In Lombardy increasing ozone trends claim for effective reduction policies
0
100
200
300
400
500
1991 1993 1995 1997 1999
mg
/m^3
Maximum obs. hourly O3Hourly O3 law thresholdsummer average (Apr -Sep)
Sources classificanion
CORINAIR: defines 13 typologies of emission sources (e.g.: road transports, industrial plants, waste disposal,.. ecc.)
The costs of the implementation of reduction policies for different polluttants in the different sectors have been estimated by (IAASA, 2000)
Aims of the research To design effective ozone reduction policies for
Lombardy region solving a multi-objective optimisation problem…
ozo
ne p
ollu
tion
red
ucti
on
[%
max]
0%
30%
60%
100%
20% 40% 60% 80% 100%
reduction costs [% max]
?
Methodology Selection of a meaningful ozone indicator (max 8h average)
Scenarios simulations through CALGRID, an eulerian photochemical model (time consuming)
ANN training to map CALGRID inputs to the simulated ozone indicator
Precursors reduction costs evaluation (IAASA, 2000)
Decision variables selection (precursors reduction rates in each emission sector)
Solution of the multi-objective optimization problem, modelling ozone dynamics through ANN
Photochemical simulation (1)Orography
Wind field
VOC emissions
Requires as inputs on each cell: Orography
Hourly emissions
Hourly wind field
Such gridded data are obtained through ad hoc pre-processing
Photochemical simulation (2)
Returns 3-D Ozone concentration fields
Given the computational effort, we analysed few scenario simulations, assuming a uniform VOC/NOx reduction rate on the whole domain
Meteorological conditions: 5-7 June 1996
- 35% NOx - 35% VOC
- 35% VOC and NOx
+ 35% VOC, NOx
How to perform an optimization analysis?
Artificial neurons
xt
xt-1
xt-2
...
w1,1
w1,r
b
1
input
neuron
= f(Wx+b)
xt-
xt --1
...
jkkjj bxwz
Weighted sum of the inputs (cfr. dendriti)
Logistic activation function
Artificial Neural Networks (ANN)
x0
x1
x2
xr
...
f
w1,1
wn,r
input Hidden layer(n neurons)
Forecast
Output neuron
1ty
Emission and receptors
Receptor (4km * 4km) : a given cell in the gridded domain (ozone indicator evaluation)
Emissions (12km * 12 km) : cells in the square centered in the receptor (emission patterns, initial concentration conditions) 4 km4 km 4 km4 km 4 km4 km
The neural emission-receptor modelInputs (at
each emission cell):
•Daily Nox emissions (24 h)
•Daily VOC emissions(24 h)
• NOx and VOC initial conditions
•Elevation above sea level
Ozone indicator (max 8h average on the receptor)
Hidden layer:
Neural network training
PCA analysis (45 inputs -> 22 inputs) Generalization ability: early stopping Levenberg - Marquardt algorithm 26 hidden nodes
ANN Results
The mountain part of the region is insensitive to both NOx and VOC reductions on the whole domain; thus, we focus on the plain part
(VOC -limited)
Data Set 8 - Test Set - BACINO di PIANURATarget (t) vs. Output rete (a)
Correlazione: R = 0,912
T
A
40
50
60
70
80
90
100
40 50 60 70 80 90 100
Correlation Correlation R = R = 0.9120.912
The network fits well the data, and can be exploited for optimization purposes.
12000 km^2
Reduction policies
The policy design requires to select a VOC reduction rates for: solvent use (470 ton/day) road transport (408 ton/day) waste treatment (110 kg/day) fossil fuel distribution (50 ton/day) production without combustion (23 ton/day)
Reduction costs for each sector are known (IAASA, 2000)
Optimization
rs: reduction for sector s: Rs maximum feasible
Eijs : VOC emission on cell (i,j) for sector s
cs: reduction costs function for sector s
Ii,j : ozone indicator on cell (i,j)
s
jisjir
sss
ij
ssji
r
R
rIpollution
rcErcosts
s
,,
,
r0
:by costrained
)(min)min(
)(min)min(
Pareto Boundary
0
0.5
1
0 0.2 0.4 0.6 0.8 1
Costs (% with respect to the maximum)
Ozo
ne r
educ
tion
(%
wit
h re
spec
t to
the
max
)
70% maximum feasible ozone reduction30% maximum costs
Solution analysis
0%
20%
40%
60%
80%
100%
0% 20% 40% 60% 80% 100%
Costs [% maximum]
Ozo
ne
reduct
ion [%
max
imum
]
Proc wth comb Fuel distribSolventRoad transportWaste
VO
C
Conclusions
The optimisation problem has been solved thanks to ANN ability in non linear dynamic and computational speed
The main result is that noticeable improvements in ozone level are reachable even through moderate investments, provided that these are targeted to some sectors, such as road transport and industrial solvents.