Flare performance modeling and set point determination using artificial neural networks

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.

Review of Computational Techniques for the Analysis of Abnormal Patterns of ECG Signal Provoked by Cardiac Disease

“…The different features extracted in this analysis are FD, spectral entropy, QRS length, kurtosis, QRS amplitude, and mean of the power spectral density. These features were fed to the KNN and GMM classifiers to diagnose ischemia to obtain an accuracy of 99% and 98.24%, respectively [134]. Applied KNN classifier to diagnose MI and obtained an accuracy of 99.31%.…”