Uma Rede de Tecnologia e Qualidade
Emission Reduction through Analysis,
Modelling and Control
Marta Almeida
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Emission Reduction Through Analysis, Modelling and Control
ERAMAC
•
ERAMAC was carried out with a financial grant from the
Research Fund for Coal and Steel (RFCS) of the European
Community
• Partners
CORUS (coordination) Anderson DR, Aries E,
Ciaparra D, Schofield MJ UK
SECHAUD Le Louer P France
BFI Brandenburger J Germany
SIDENOR Unamuno I Spain
ISQ Almeida SM Portugal
CRM Steyls D Belgium
What was the objective?
The project was aimed at reducing emissions of organic
species (VOCs and SVOCs), NO
X, SO
2and particulates from
iron and steelmaking processes by an integrated study:
• involving impact measurement and modelling;
• process control through the application of soft-sensing
predictive modelling;
• the development of a novel wet contactor for reducing
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Emission
Measurements
PTS SO2 NOx VOC BTEX PAHs/PCBsHow did we make it?
Process Control
Receptor
Measurements
PTS/PM10 SO2 NOx VOC BTEX PAHsSoft sensing
Dispersion model
Development and
validation of
measurements
techniques
PAH measurements in the EAF stack
- Predominant PAHs were naphtalene (40 mg/Nm3)
- Low molecular weight PAHs (2 and 3 ringed PAHs) presented the highest contribution for total PAH (85%).
0 100000 200000 300000 M2 00 5-1 M2 005 -3 M2 005 -5 M2 005 -7 M2 005 -9 M2 005-11J200 5-2 J200 5-4 J200 5-6 J200 5-8 J200 5-10 M2 00 6-2 M2 00 6-4 M2 00 6-6 M2 00 6-8 M2 00 6-10 J200 6-1 J200 6-3 J200 6-5 J200 6-7 J200 6-9 C onc e n tr a ti on ( n g/ m 3 ) Dibenz(ah)anthracene Indeno(1,2,3)-cd-pyrene Benzo(ghi)perylene Benzo(a)pyrene Benzo(b+j+k)fluoranthene Chrysene Benzo(a)anthracene Pyrene Fluoranthene Anthracene Phenanthrene Fluorene Acenaphtene Acenaphtylene Naphthalene 3rd campaign 4th campaign 5th campaign 6th campaign
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Percent contribution of PAHs to total B[a]Peq
• For comparative purposes the PAH concentrations were expressed as benzo[a]pyrene equivalents.
• PAH concentrations were weighted in relation to the carcinogenic potential of individual PAH compounds using toxic equivalency factors (TEF)
0 5 10 15 20 25 30 35 40 Na p h Ac y Ac e Fl u o r Ph e n An t Fl a n t Py r B[ a ]A n t Ch ry B[ b + j] F la n t B[ a ]P B[ g h i] P e r I[ 1, 2, 3] pyr D[ a h ]a n t %
Benzo(a)pyrene and benzo(b+j+k)fluoranthene - compounds that contributed the most to the overall toxicity of EAF emissions.
PCB measurements in the EAF stack
- Among the 209 PCB congeners, only PCB 28, 52, 101, 138, 153 and 180 were determined in EAF stack. The predominant compounds was PCB 28 (23%) 0 200 400 600 800 1000 1200 1400 M 2 005-1 M 2 005-2 M 2 005-3 M 2 005-4 M 2 005-5 M 2 005-6 M 2 005-7 M 2 005-8 M 2 005-9 M 2 005-10 M 2 005-11 J2005-1 J 2 005-2 J 2 005-3 J 2 005-4 J 2 005-5 J 2 005-6 J 2 005-7 J 2 005-8 J 2 005-9 J 2 005-10 M 2 006-1 M 2 006-2 M 2 006-3 M 2 006-4 M 2 006-5 M 2 006-6 M 2 006-7 M 2 006-8 M 2 006-9 M 2 006-10 M 2 006-11 J2006-1 J 2 006-2 J 2 006-3 J 2 006-4 J 2 006-5 J 2 006-6 J 2 006-7 J 2 006-8 J 2 006-9 J 2 006-10 Co n cen tr at io n ( n g /m 3 ) PCB 180 PCB 153 PCB 138 PCB 101 PCB 52 PCB 28 3rd campaign 4th campaign 5th campaign 6th campaign
0 200 400 600 800 1000 1200 1400 M 2 005-1 M 2 005-2 M 2 005-3 M 2 005-4 M 2 005-5 M 2 005-6 M 2 005-7 M 2 005-8 M 2 005-9 M 2 005-10 M 2 005-11 J2005-1 J 2 005-2 J 2 005-3 J 2 005-4 J 2 005-5 J 2 005-6 J 2 005-7 J 2 005-8 J 2 005-9 J 2 005-10 M 2 006-1 M 2 006-2 M 2 006-3 M 2 006-4 M 2 006-5 M 2 006-6 M 2 006-7 M 2 006-8 M 2 006-9 M 2 006-10 M 2 006-11 J2006-1 J 2 006-2 J 2 006-3 J 2 006-4 J 2 006-5 J 2 006-6 J 2 006-7 J 2 006-8 J 2 006-9 J 2 006-10 Co n cen tr at io n ( n g /m 3 ) PCB 180 PCB 153 PCB 138 PCB 101 PCB 52 PCB 28 3rd campaign 4th campaign 5th campaign 6th campaign
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VOC measurements in the EAF stack
- VOC emission in the EAF varied between 1.7 and 6.2 ppm with an average of 3.6 ppm
- Results indicate that benzene was the major contributor for BTEX total concentration
Data Evaluation
A statistical study was carried about the influence of the variability of some parameters such as the type of scrap, the injected oxygen and the graphite on the BTEX emission
BTEX R2 = 0,8412 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Measured Values (mg/m3) Calc u lat ed v a lues ( m g /m 3 )
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Data Evaluation
It has been demonstrated that dust emissions depended on the type and state of bag filters and the hours of operation of the bag filter equipment as compared to the hours of the EAF operation. As particulate emissions were a function of bag filter operation, an increase in steel production did not necessarily mean that emissions would increase
Dust evolution 0 10 20 30 40 50 60 70 80 90 100 27/05/2006 0:00:00 29/05/2006 0:00:00 31/05/2006 0:00:00 02/06/2006 0:00:00 04/06/2006 0:00:00 06/06/2006 0:00:00 08/06/2006 0:00:00 10/06/2006 0:00:00 12/06/2006 0:00:00 14/06/2006 0:00:00 Dust concent (mg/ Nm 3) NO
DATA Change of filters
Dust evolution 0 10 20 30 40 50 60 70 80 90 100 27/05/2006 0:00:00 29/05/2006 0:00:00 31/05/2006 0:00:00 02/06/2006 0:00:00 04/06/2006 0:00:00 06/06/2006 0:00:00 08/06/2006 0:00:00 10/06/2006 0:00:00 12/06/2006 0:00:00 14/06/2006 0:00:00 Dust concent (mg/ Nm 3) NO
Dispersion modelling
Hourly maximum value
-10 -8 -6 -4 -2 0 2 4 6 8 10 -10 -8 -6 -4 -2 0 2 4 6 8 10 0 0.41 0.83 1.2 1.7 2.1 2.5 2.9 3.3 3.7 TSP (micg/m3) -10 -8 -6 -4 -2 0 2 4 6 8 10 -10 -8 -6 -4 -2 0 2 4 6 8 10 0 1.93 3.9 5.8 7.7 9.7 12 13.5 15 17 TSP (micg/m3)
Annual average value
Average (calculated) (µg/m3) Maximum (calculated) (µg/m3) Average (measured) (µg/m3) TSP 4.3 19 83 NOx 1.2 5.1 25 SO2 1.2 5.1 11 PAH 6.9×10-3 3.1×10-2 - B[a]P 4.9×10-6 2.2×10-5 - Fugitive emissions Dust ressuspension Vehicles circulation
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CONCLUSIONS
•
PAHs, PCBs, VOCs (namely BTEX) and dust have been
measured in the EAF stack considering different operational
conditions (type of scrap, type of steel elaborated,…)
• Some relationships between the measured emissions data
and operational conditions have been established
• Gases and dust have been monitorized in the local
environment and a dispersion model has been developed
• Dust data and process data have been used in order to
develop a predictive model
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