2019
DOI: 10.1016/j.jenvman.2018.03.126
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Efficient cadmium removal from aqueous solutions using a sample coal waste activated by rhamnolipid biosurfactant

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Cited by 31 publications
(8 citation statements)
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“…In order to analyse the effect of operational parameters on process responses, firstly, it is necessary to develop a parametric model that can accurately forecast the intended response in the operating area, i.e., inside the low to high intended range for parameters (Boveiri et al, 2019). For modelling experimental data, the Design Expert v.7.0 software (Demo version 7.0.0, from Stat-Ease Inc., Minneapolis, MN, USA) was used.…”
Section: Statistical Analysis Of Resultsmentioning
confidence: 99%
“…In order to analyse the effect of operational parameters on process responses, firstly, it is necessary to develop a parametric model that can accurately forecast the intended response in the operating area, i.e., inside the low to high intended range for parameters (Boveiri et al, 2019). For modelling experimental data, the Design Expert v.7.0 software (Demo version 7.0.0, from Stat-Ease Inc., Minneapolis, MN, USA) was used.…”
Section: Statistical Analysis Of Resultsmentioning
confidence: 99%
“…Based on the obtained values of q max and k f , our tobaccobased AC is superior (in terms of Cd 2+ uptake) to grape stem biosorbent [56], AC from Ulva Lactuca [74], AC from oak wood [94], carbon activated with rhamnolipid biosurfactant [95], having a similar capacity than the composite AC (carbon/zirconium oxide) [96] (Table 6). The order for the best adjustments found using nonlinear models in the adsorption of Cd 2+ by CT in natura + CO 2 was: Freundlich > D-R > Langmuir > Sips > Liu > Temkin; for nonlinear models applied on CT NaOH + CO 2 : Langmuir > D-R > Temkin > Liu > Sips > Freundlich.…”
Section: Adsorption Equilibrium Studies and Comparison Between Linear And Non-linear Forms Of Isothermsmentioning
confidence: 91%
“…The first step in analysing the impact of operational variables on the process responses is developing a parametric model that can accurately predict the desired response in the operating space, i.e., within the low to high levels intended for the variables [37]. In the second step, after developing the initial model by the software, abnormal data were identified by examining the model parameters and the model was optimized by the user to achieve the best fitting results.…”
Section: Statistical Analysis Of Optimization Doementioning
confidence: 99%