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

2011 ◽  
Vol 8 (2) ◽  
pp. 389-400 ◽  
Author(s):  
A. Kalali ◽  
T. Ebadi ◽  
A. Rabbani ◽  
S. Sadri Moghaddam
2020 ◽  
Vol 38 (3-4) ◽  
pp. 79-93
Author(s):  
Zahra Sheikhi Alman-Abad ◽  
Hossein Pirkharrati ◽  
Farrokh Asadzadeh ◽  
Mahdi Maleki-Kakelar

Heavy metal wastes generated from mining activities are a major concern in developing countries such as Iran. Increasing concentrations of these metals in the soil make up a severe health hazard due to their non-degradability and toxicity. In this study, batch washing experiments were conducted in order to investigate the removal efficiency of zinc by biodegradable chelates, tartaric acid. For this purpose, soil samples were collected from the zinc contaminated soil in the region of the Angouran, Zanjan, Iran. Hence, optimization of batch washing conditions followed using a three-level central composite design approach based on the response surface methodology. The results demonstrated that the effects of pH, tartaric acid concentration, and interaction between selective factors on the zinc removal efficiency were all positive and significant (P < 0.05). An optimum zinc removal efficiency of 89.35 ±2.12% was achieved at tartaric acid concentration of 200 mM l−1, pH of 4.46, and incubation time of 120 min as the optimal conditions. Accordingly, response surface methodology is appropriately capable to determine and optimize chemical soil washing process to remediate heavy metal polluted soil.


2018 ◽  
Vol 26 (4) ◽  
pp. 241-250 ◽  
Author(s):  
Liwei He ◽  
Bin Li ◽  
Ping Ning ◽  
Xiao Gong

This research presents the optimization of soil washing conditions in the removal of multiple heavy metals (Cu-Pb-Zn-Cd) under the using of ethylenediaminetetraacetic acid (EDTA). The optimum combination of washing parameters in a bench-scale soil washing experiments is determined by response surface methodology (RSM). Central composite design is applied after single factor experiment, EDTA concentration, solid-to-liquid ratio and washing time are evaluated variables for the removal processes, and the regression models of HMs are constructed. The results show that, EDTA concentration and solid-to-liquid ratio are significant factors for this process. Subsequently, 50% of Cu removal was set as the optimum target to optimize the combined conditions, through the building of multiple quadratic regression models, the optimal condition combination is determined that EDTA concentration is 0.0026 mol·L-1, solid-to-liquid ratio is 1:22, washing time is 3.89 h, the extraction rate of Pb, Zn, Cd is predicted to be 78%, 75% and 71%, respectively.


PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8578
Author(s):  
Befkadu Abayneh Ayele ◽  
Jun Lu ◽  
Quanyuan Chen

Surfactant-enhanced soil washing has been used for remediation of organic pollutants for an extended period, but its effectiveness and wide application was limited by the high concentration of surfactants utilized. In this work, the efficiency of conventional soil washing performance was enhanced by 12–25% through the incorporation of air bubbles into the low concentration surfactant soil washing system. Surfactant selection pre-experiment using aerated and conventional soil washing reveals Brij 35 > TX100 > Tween 80 > Saponin in diesel oil removal. Optimization of the effect of time, surfactant concentration, pH, agitation speed, and airflow rate in five levels were undertaken using Response Surface Methodology and Central composite design. The optimum degree of variables achieved was 90 min of washing time, 370 mg/l of concentration, washing pH of 10,535 rpm of agitation speed and 7.2 l/min of airflow rate with 79.5% diesel removal. The high predicted R2 value of 0.9517 showed that the model could efficiently be used to predict diesel removal efficiency. The variation in efficiency of aeration assisted and conventional soil washing was variable depending on the type of surfactant, organic matter content of the soil, particle size distribution and level of pollutant weathering. The difference in removal efficiency of the two methods increases when the level of organic matter increases and when the particle size and age of contamination decreases.


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