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1.
J Air Waste Manag Assoc ; 74(5): 319-334, 2024 05.
Artigo em Inglês | MEDLINE | ID: mdl-38377314

RESUMO

Mid-wavelength infrared (MWIR) imaging Fourier transform spectrometers (IFTSs) are a promising technology for measuring flare combustion efficiency (CE) and destruction removal efficiency (DRE). These devices generate spectrally resolved intensity images of the flare plume, which may then be used to infer column densities of relevant species along each pixel line-of-sight. In parallel, a 2D projected velocity field may be inferred from the apparent motion of flow features between successive images. Finally, the column densities and velocity field are combined to estimate the mass flow rates for the species needed to calculate the CE or DRE. Since the MWIR IFTS can measure key carbon-containing species in the flare plume, it is possible to measure CE without knowing the fuel flow rate, which is important for fenceline measurements. This work demonstrates this approach on a laboratory heated vent, and then deploys the technique on two working flares: a combustor burning natural gas at a known rate, and a steam-assisted flare at a petrochemical refinery. Analysis of the IFTS data highlights the potential of this approach, but also areas for future development to transform this approach into a reliable technique for quantifying flare emissions.Implications: Our research is motivated by the need to assess hydrocarbon emissions from flaring, which is a critical problem of global significance. For example, recent studies have shown that methane destruction efficiency of flaring from upstream oil may be significantly lower than the commonly assumed figure of 98%; work by Plant et al. , in particular, suggest that this discrepancy amounts to CO2 emissions from 2 to 8 million automobiles annually, considering the US alone. Similarly, the international energy agency (IEA) estimates a global flare efficiency of 92%, which translates in 8 million tons of CH4 emitted by flares in 2020. Highlighted by these studies and supported by the World Bank initiatives toward zero routine flaring emissions, there is an urgent need for oil and gas industry to assess their flare methane emission, and overall hydrocarbon emissions. At the very least, it is critical to identify problematic flare operating conditions and means to mitigate flare emissions. Focusing on remote quantification of plume species, the measurement technique and quantification method presented in this paper is a considerable step forward in that direction by computing combustion efficiency and key components for destruction efficiency.


Assuntos
Poluentes Atmosféricos , Espectroscopia de Infravermelho com Transformada de Fourier/métodos , Poluentes Atmosféricos/análise , Monitoramento Ambiental/métodos , Gás Natural/análise
2.
Appl Opt ; 57(21): 6219-6228, 2018 Jul 20.
Artigo em Inglês | MEDLINE | ID: mdl-30118004

RESUMO

Continuum removal is vital in hyperspectral image analysis. It enables data to be used for any application and usually requires approximations or assumptions to be made. One of these approximations is related to the calculation of the spectra of the background's blackbody temperature. Here, we present a new method to calculate the continuum removal process. The proposed method eliminates the calculation for ground-based hyperspectral infrared imagery by applying two acquisition sets before and after using the heating source. The approach involves a laboratory experiment on a long-wave infrared (LWIR; 7.7-11.8 µm), with a LWIR-macro lens, an Infragold plate, and a heating source. To calculate the continuum removal process, the approach applies non-negative matrix factorization (NMF) to extract Rank-1 NMF, estimate the downwelling radiance, and compare it with that of other conventional methods. NMF uses gradient-descent-based rules (GD) and non-negative least-squares (NNLS) optimization algorithms to obtain Rank-1 NMF. A comparative analysis is performed with 1%-20% additive noise for all algorithms by using the spectral angle mapper and normalized cross correlation (NCC). Results reveal the promising performance of NMF-GD (average of 72.5% similarity percentage using NCC) and NMF-NNLS (average of 77.6% similarity percentage using NCC).

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