Investigation and Modelling of Ughelli West Gas Plant Gaseous Pollutants

Abstract

Gaseous contamination of environment poses great challenges to
human health. Inadequate models to predict the concentration of
gaseous pollutants had led to the poor control and monitoring of gas
plant gaseous pollutants. This study focused on the modelling and
analysis of Ughelli West gas plant gaseous pollutants located in
Delta State, Nigeria. Aeroqual multi-parameter environmental
monitor (series 500), was employed to monitor the concentrations of
volatile organic compounds (VOCs), oxides of nitrogen (NO2),
oxides of sulphur (SO2), ozone (O3) and methane (CH4). The
concentrations of the particulate matter (PM2.5 of these gases were
obtained at each monitoring point on daily bases for a period of
twelve weeks using Aerocet-531 SPM meter. Sky master thermo
anemometer (SM-28) was used to obtain the important climatic
variables (wind speed, atmospheric pressure, ambient temperature
and relative humidity) which affect the dispersion of gaseous
pollutants. The maximum concentration of each monitored gaseous
pollutant during the twelve weeks (12) monitoring period was
selected and recorded for data processing. In this study,
mathematical models were developed for predicting each gaseous
pollutant such as volatile organic compounds (VOCs), oxides of
nitrogen (NO2), oxides of Sulphur (SO2), ozone (O3) and methane
(CH4). The curve fitting tool in Matrix Laboratory {MATLAB
(2016a)} was employed to select models and to model the exact
mathematical relationship between the pollutant concentrations and
the flare distance; then the pollutants concentrations were predicted
beyond the experimental distance of 500m from the flare point, the
models were validated using coefficient of determination (R2
), and
root mean square error (RMSE). Based on the parameters, it was
observed that the quadratic polynomial model had the lowest root
mean square error value of 0.6053 and coefficient of determination
R
2
value of 0.9919. The results obtained from the validation shows
good predictability and adequacy of the models developed in
petroleum drilling processes.