Document Type : Full article

Abstract

The aim of this study was to investigate the particulate dispersion from Kerman Cement Plant. The upwind – downwind method was used to measure particle concentration and a cascade impactor was applied to determine particle size distribution. An Eulerian model, Gaussian plume model and an artificial neural network have been used to compute and predict concentration of PM₁₀ from Kerman Cement Plant. Eulerian model incorporates source related factors, meteorological factors, surface roughness and particle settling to estimate pollutant concentration from continuous sources. The measured data have been used to create an artificial neural network for predicting suspended particle concentration from Kerman Cement Plant. The data includes particle concentration, distance from source, mixing height, lateral and vertical dispersion parameters and 10 meters wind speed. The performance of these models has been compared with the measured data. The AAPD (Average Absolute Percent Deviation) parameter for the results of the Eulerian model, Gaussian model and ANNs was 25.53%, 15.38% and 5.91% respectively.
 
 
 
 
 

Keywords

• Air pollution
• Particulate Dispersion
• Gaussian Plume Model
• Artificial neural networks (ANNs)

Main Subjects

• Modeling and Simulation
• Process Control and Engineering, Process Safety, HSE