10.1007/s40095-019-00314-3

Flare performance modeling and set point determination using artificial neural networks

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  • Vijaya Durga Damodara*1,
  • Arokiaraj Alphones1,
  • Daniel H. Chen1,
  • Helen H. Lou1,
  • Christopher Martin2,
  • Xianchang Li3,
  1. Dan F. Smith Department of Chemical Engineering, Lamar University, Beaumont, TX, 77710, US
  2. Department of Chemistry and Biochemistry, Lamar University, Beaumont, TX, 77710, US
  3. Mechanical Engineering, Lamar University, Beaumont, TX, 77710, US
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Published in Issue 2019-08-13

How to Cite

Damodara, V. D., Alphones, A., Chen, D. H., Lou, H. H., Martin, C., & Li, X. (2019). Flare performance modeling and set point determination using artificial neural networks. International Journal of Energy and Environmental Engineering, 11(1 (March 2020). https://doi.org/10.1007/s40095-019-00314-3
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Abstract

Abstract Current EPA regulations mandate a minimum combustion zone heating value of 270 BTU/scf and a net heating value dilution parameter of NHV dil  ≥ 22 BTU/ft 2 for all steam/air/non-assisted flares while maintaining a high combustion efficiency (CE). To achieve the target performance along with satisfying the EPA regulations, it is necessary to understand the influence of various operating parameters. Studying the effect of operating parameters through experiments is both expensive and time consuming. It is more cost effective to use validated models to guide flare operations. In this study, controlled flare test data conducted from 1983 to 2014 with a wide range of exit velocities, heating values, and fuel compositions have been modeled. The purpose of this study is to develop models that can be robustly used in the industry to achieve the desired CE without visible emissions (smoke). Steam-/air-assist rates, exit velocity, and the vent gas composition, which can be either controlled or measured in flare operations, are used as independent variables in the models. Neural network (NN) models were developed for the air-assisted, steam-assisted, and non-assisted flares using various types of fuels like propylene, propane, natural gas, methane, and ethylene. The flare performance models such as CE and opacity were developed using neural network toolbox in MATLAB. NN models for steam and air-assisted flare tests are in good agreement with experimental data and have been demonstrated by the average correlation coefficient of 0.95 and 0.97 for air-assisted and steam-assisted flare data, respectively. The very low mean absolute errors of 1.1% and 1.4% for air-assisted and steam-assisted flare data, respectively, also indicate the robustness of the NN models. 2-D and 3-D contour plots are presented to show the effect of key operating parameters. The set points (amount of steam/air/make-up fuel required) at the Incipient Smoke Point (ISP) and for Smokeless Flaring (SLF) have been developed based on the neural network models performed in this study. Desirable operating inputs can be set for the ISP and for SLF (Opacity ≤ Opacity ISP ) subject to heating value constraints (NHV dil  ≥ 22 BTU/ft 2 & NHV CZ  ≥ 270 BTU/scf) with a high CE (≥ 96.5%) for the 1984 EPA and 2010 TCEQ flare study test cases.

Keywords

  • Combustion efficiency,
  • Opacity,
  • Neural network,
  • Incipient smoke point
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