Multi-Factorial Analysis of Atmospheric Noise Pollution Level Based on Emitted Carbon and Heat Radiation during Gas Flaring

Abstract

This paper presents a multi-factorial analysis of atmospheric noise pollution level based on emitted carbon and heat radiation during gas flaring. An empirical model; three factorial in nature was derived, validated and used for the noise pollution level analysis. The derived model showed that the noise pollution level was basically dependent on gas flaring output parameters such as emitted carbon and heat radiation since the three occur at the same time, and also on reference distance from flare point, total associated gas and total gas produced. The validity of the model; ϑ=D Log ϕ[Log ϕ (0.0001ζ2+ζ)+₰]-1 was rooted on the core model expression D / ϑ ≈ S ζ2 + ζ + (₰ / Log ϕ) where both sides of the expression are correspondingly approximately equal. Regression model was used to generate results of noise pollution level, and its trend of distribution was compared with that from derived model as a way of verifying its validity relative to experimental results. The results of this verification translated into very close alignment of curves, dimensions of shapes and areas covered. These translated into significantly similar trend of data point’s distribution for experimental (ExD), derived model (MoD) and regression model-predicted (ReG) results. Evaluations from generated results indicated that noise pollution level per unit radiated heat & emitted carbon as obtained from experiment and derived model were 60.42 and 60.00 dBA / Kw m-2 & 4.01 x10-4 and 3.98x10-4 dBA /ton respectively. Standard errors incurred in predicting noise pollution level for each value of the radiated heat, emitted carbon & Total associated gas/ Total gas produced; TAG/TGP as obtained from experiment and derived model were 6.6533 and 5.7521%, 6.6405 and 3.1291 % & 6.6616 and 3.9963% respectively. The least and highest deviation of model-predicted noise pollution level (from experimental results) were 1.62 and 21.42%, implying a model operational confidence level range 78-98%.