Identification of CO2 and O2 emissions dynamics in a natural gas furnace through flame images, ARMAX models, and Kalman filtering

Efficient diagnosis of emissions from combustion processes plays a key role in their control, an essential part of the overall effort to mitigate the increasing greenhouse effect. In industrial furnaces, a set of sensors (CO x , SO x , NO x ) at the exhaust is used to monitor pollutant rates, thus p...

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Bibliographic Details
Published in:Journal of the Brazilian Society of Mechanical Sciences and Engineering 2021, Vol.43 (5)
Main Authors: Silva Neto, Gustavo C., Chui, Danilo S., Martins, Flavius P. R., Fleury, Agenor T., Furnari, Fausto, Trigo, Flávio C.
Format: Article
Language:English
Subjects:
Autoregressive moving average
Autoregressive moving-average models
Carbon dioxide
Charge coupled devices
Combustion
Engineering
Exhaust gases
Flue gas
Furnaces
Greenhouse effect
Kalman filters
Mechanical Engineering
Natural gas
Pollutants
Technical Paper
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Summary:Efficient diagnosis of emissions from combustion processes plays a key role in their control, an essential part of the overall effort to mitigate the increasing greenhouse effect. In industrial furnaces, a set of sensors (CO x , SO x , NO x ) at the exhaust is used to monitor pollutant rates, thus providing the necessary information for control purposes. In the case of natural gas furnaces, measurements of O 2 and CO 2 contents are used to check the condition of the combustion process. In this work, we propose a method to estimate the O 2 and CO 2 contents at the exhaust of a natural gas prototype furnace from images of flames grabbed by a charge-coupled device (CCD) camera. Feature vectors obtained from computer processing of the grabbed images are used as input data to identify auto-regressive moving average (ARMAX) “black box” models having CO 2 content as output. Estimates of O 2 content by a Kalman filter running a preliminary ARMAX model help the overall performance of the method. Results show that the flame dynamics identified model is capable of yielding statistically significant estimates of both O 2 and CO 2 composition in the flue gas up to 10 s before the arrival of actual O 2 measurements. This outcome suggests that the inclusion of the proposed method in the closed-loop control strategy of similar combustion processes might be advantageous.
ISSN:1678-5878
1806-3691
DOI:10.1007/s40430-021-02967-w