Dispersion modeling of thermal power plant emissions on stochastic space

Abstract

This study aims to couple a deterministic atmospheric dispersion solver based on Gaussian model with a nonintrusive stochastic model to quantify the propagation of multiple uncertainties. The nonintrusive model is based on probabilistic collocation framework. The advantage of nonintrusive nature is to retain the existing deterministic plume dispersion model without missing the accuracy in extracting the statistics of stochastic solution. The developed model is applied to analyze the SO 2 emission released from coal firing unit in the second stage of the National Thermal Power Corporation (NTPC) in Dadri, India using Burban^conditions. The entire application is split into two cases, depending on the source of uncertainty. In case 1, the uncertainties in stack gas exit conditions are used to construct the stochastic space while in case 2, meteorological conditions are considered as the sources of uncertainty. Both cases develop 2D uncertain random space in which the uncertainty propagation is quantified in terms of plume rise and pollutant concentration distribution under slightly unstable atmospheric stability conditions. Starting with deterministic Gaussian plume model demonstration and its application, development of stochastic collocation model, convergence study, error analysis, and uncertainty quantification are presented in this paper.

Modelling Hourly Atmospheric Stability and Mixing Height Using Metrological Surface Data

“…One of the most important meteorological input parameters into air pollution models are the atmospheric stabilities and atmospheric boundary layer or Mixing Height (MH) [1][2][3][4]. Also these factors are used in other field such as study wind turbine and atmospheric cooling efficiency [5,6].…”