Response surface methodology approach to the optimization of oil hydrocarbon polluted soil remediation using enhanced soil washing

Abstract

ABSTRACT:One of the environmental concerns in recent decades is the prevalence of different pollutants in soil. Hence, the importance of remediation has led to the development of various methods to remediate polluted soil. Among these methods, soil washing has gained significant attention to treat polluted soils. In this paper, the response surface methodology was applied in order to determine the optimal conditions for total petroleum hydrocarbon remediation using nonionic surfactant Brij35 in soil environment. The effect of different factors in soil washing process including surfactant solution concentration and volume, washing time, age of pollution and frequency of washing are evaluated.The predicted values for total petroleum hydrocarbon remediation efficiency by the response functions are in a very close agreement with experimental data (R -2 = 98.75 %). The second order model was developed as experimented response and optimal conditions were obtained by analyzing the contour and surface plots and also by solving the regression equation using LINGO 9.0 software. The optimal concentrations (8 g/L), volume of surfactant solution (500 mL), washing time (75 min), age of pollution (29 days) and frequency of washing (three times) are determined. In this optimal condition, the removal efficiency has been observed to be 93.54 % which conforms to the results of process optimization using response surface methodology. Based on the results, it can be concluded that the response surface methodology is a suitable approach to determine the optimal conditions of soil washing to remediate organic hydrophobic pollutants using the nonionic surfactant Brij35 from the soil.

Full Factorial Design of Screening Experiments for Biosurfactant Enhanced Remediation of Hydrophobic Substances in Soil

“…Estimates of P-value for the quadratic model are > 0.05, therefore the model can describe experimental data at 95.0 % confidence level. The same results about quadratic relationships between the process variables have been reported for the removal efficiency of total hydrocarbons from soil by nonionic surfactant in the Reference [9].…”

“…References on the application of factorial screening design in the field of biosurfactants can be found in the area of process optimization for biodegradation of ethyl benzene and xylene compounds in mixtures [8], for efficiency increase in the removal of total petroleum hydrocarbons [9] and diesel [10], for location of the most suitable temperature and surfactant concentration [11] and for determination of recovery efficiency of enzymes [12].…”

Screening of lipid high producing mutant from rhodotorula glutinis by low ion implantation and study on optimization of fermentation medium

“…The response surface methodology (RSM) is a combination of mathematical and statistical approaches used for modeling different phenomena and optimizing the experimental results as a function of various parameters [9]. The main advantage of RSM is the reduced number of experimental runs needed to provide sufficient information for statistically acceptable result.…”

Mitigation of heavy metals in different vegetables through biological washing techniques

“…Dietary sources of heavy metals include contaminated food stuff such as fruits, vegetables and drinking water (Kachenko & Singh, 2006). As vegetables are the source of human consumption so the soil-to-plant transfer quotient is the main source of human exposure (Kalali et al, 2011). Use of waste water for crop cultivation is recognized as important source of heavy metals in soil (Mapanda et al, 2005).…”