Comparative Studies on Modelling and Optimization of Soil Parameters on Crude Oil Contaminated Mangrove and Clay Soils Remediation Using Box-Behnken RSM

Abstract

The combined effect of three selected soil parameters (moisture, nutrient, and pH) on the degradation rate of hydrocarbons in
mangrove and clayey soils was successfully modeled using Box–Behnken design matrix of Response Surface Methodology (RSM). The method of study using the biostimulation technology aims to investigate, optimize and compare the relative effects of the selected soil variables and textures on the degradation rate of Bonny light crude oil in the soil samples. Two sets of sixteen experimental buckets containing 1 kg of contaminated soil were set up for each soil sample. The soil factors studied at three levels
was the independent variables. The residual (TPH) in the experimental buckets, monitored using gas chromatography/mass spectrometry was the dependent parameter. Results from each experimental unit showed a measurable reduction in TPH with
time. The statistical analyses of the experimental results and model predictions reveal that nutrient has a pronounced major
influence on crude oil degradation and that mangrove soil in comparison to clay soil favors faster crude oil degradation under
atmospheric conditions. The predicted optimum parameters for nutrient, pH, and moisture were 0.143kg/kg soil, 6.7 and 0.23kg/kg soil, for the clay soil 0.160kg/kg soil, 8.00 and 0.140kg/kg soil for the mangrove soil. The predicted value of 81.48% for
the mangrove soil and 63.5% for the clay soil at the optimum with a regression coefficient of 99.35% and 99.09 is suggesting that
the developed quadratic regression models are accurate and reliable