Monte Carlo Analysis of Hazardous Air Pollutant Emissions from Industrial Flares

摘要

Flares are essential in safely managing excess combustible gases that can result from normal operations as well as process upsets and emergency conditions in refineries, chemical plants, and other industrial facilities. Government regulations require that facilities inventory the emissions from flaring events. However, direct measurement of flare emissions is difficult and often impractical. Recently, EPA has issued proposed rule changes to 40 CFR Parts 60 and 63 to amend the current emissions standards for hazardous air pollutants for petroleum refineries. Although the rule changes propose new flare operation guidelines based on results from industrial-scale testing, quantification of flare emissions is still based on the assumption that a flare operating within the required operation envelope achieves high combustion efficiency. The current practices for estimating flare emissions generally incorporate a combination of several methodologies. Common methodologies include applying emissions factors as specified by AP-42 or assuming a 98% combustion efficiency if the flare is operating in accordance to 40 CFR 60.18. In general, these methods have been developed for properly operating flares that are smokeless, not over-steamed or over-aerated. For scenarios where a flare may not be properly operating, these methods cannot be used. This work overviews methodologies including statistical and flare scaling analyses that can be used to estimate emissions from flares operating both in and outside of the regulatory operational environment. In particular, a statistical Monte Carlo method based on the recent EPA-directed testing is presented. The advantage of the Monte Carlo scheme is that it not only estimates the most probable emission for any given flaring event, but also provides upper and lower emissions bounds based on statistical confidence intervals. To determine ground-level impact on the surrounding environment, the statistical estimate of pollutant emissions can be used as inputs to dispersion models (CALPUFF, etc.). Compared to other non-statistical methods, the bounded inputs are advantageous because they provide best and worst case ground-level impact analyses. This method uses the currently available testing data and will naturally become more accurate as more data becomes available in the future.