Emission Reduction through Analysis, Modelling and Control

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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

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What was the objective?

The project was aimed at reducing emissions of organic

species (VOCs and SVOCs), NO

X

, SO

2

and 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/PCBs

How did we make it?

Process Control

Receptor

Measurements

PTS/PM10 SO2 NOx VOC BTEX PAHs

Soft sensing

Dispersion model

Development and

validation of

measurements

techniques

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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.

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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

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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

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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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Uma Rede de Tecnologia e Qualidade

Thank You!

smalmeida@isq.pt

www.isq.pt

instituto de soldadura e qualidade Marta Almeida

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