Modelling, analysis and optimization of adsorption parameters for H3PO4 activated rubber wood sawdust using response surface methodology (RSM)

Kalavathy, M. Helen; Iyyaswami, Regupathi; Pillai, Magesh Ganesa; Miranda, Lima Rose

doi:10.1016/j.colsurfb.2008.12.007

Cited by 184 publications

(89 citation statements)

References 31 publications

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“…Moreover, sawdust contains different functional groups that take part in and increase adsorption capacity (Gad et al 2013). Sawdust has optimal adsorption properties and can be employed in large-scale removal of contaminants from water (Mane et al 2007;Kalavathy et al 2009;Naiya et al 2009). The removal of heavy metals by adsorption is preferable to other techniques such as precipitation, ion exchange, adsorption, filtration, electrodeposition, and reverse osmosis both in terms of economy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Effect of Particle Size on the Sorption of Lead from Water by Different Species of Sawdust: Equilibrium and Kinetic Study

Nnaji

Emefu

2017

BioResources

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Both the effect of particle size and initial concentration on the adsorption of lead by sawdust of two timber species were investigated using batch experimental equilibrium and kinetic studies. A 100% lead removal efficiency was recorded for the optimum particle size of 1.18 mm for Pycnanthus angolensis and 0.85 mm for Khaya ivorensis at an initial lead concentration of 10 mg/L. Freundlich isotherm (0.83 ≤ R 2 ≤ 0.96 for Khaya ivorensis and 0.94 ≤ R 2 ≤ 1.0 for Pycnanthus angolensis) performed better than Langmuir and Temkin isotherms. The Dubinin-Radushkevich isotherm was used to ascertain the sorption mechanism. Mean sorption energy (12.48 kJ/mol for Pycnanthus angolensis and 13.42 kJ/mol for Khaya ivorensis) indicated that adsorption was by ion exchange. The pseudo-first order kinetic model (0.96 ≤ R 2 ≤ 1.0 for Khaya ivorensis and 0.90 ≤ R 2 ≤ 1.0 Pycnanthus angolensis) performed better than others with respect to R 2 values, while the intraparticle diffusion model performed better than the other kinetic models with respect to absolute mean error (AME).

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Section: Introductionmentioning

confidence: 99%

Effect of Particle Size on the Sorption of Lead from Water by Different Species of Sawdust: Equilibrium and Kinetic Study

Nnaji

Emefu

2017

BioResources

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“…Larger F-and smaller p-values suggest more significant corresponding variables (Amini et al 2008;Kalavathy et al 2009). The lack of fit measures the failure of the model to represent data in the experimental domain at points that are not included in the regression.…”

Section: Discussionmentioning

confidence: 99%

Effect of Enzymatic Pretreatment on the Preparation and Properties of Soy-Based Adhesive for Plywood

Chen¹,

Zeng²,

Qin³

et al. 2015

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Response surface methodology was employed to determine the effects of enzymatic pretreatment temperature, time, and pH on the reducing sugar content and bonding strength of soy-based adhesives (SBAs). Plywood specimens bonded by the SBAs with Pinus massoniana veneer were then produced. A significant positive correlation was observed between reducing sugar content and the bonding strength of SBAs. The effects of pretreatment temperature and time on bonding strength were also significant, but insignificant with respect to reducing sugar content; the effects of enzymatic pretreatment time on response values were the smallest. The optimal enzymatic pretreatment conditions of SBA were a pretreatment temperature of 54 °C, a pretreatment time of 20.0 min, and a pretreatment pH of 5.1. Under these conditions, the reducing sugar content and bonding strength (boiling-water test) of SBAs were 2.93% and 0.62 MPa, which were higher than the control by 113.9% and 30.6%, respectively. X-ray diffraction (XRD) indicated that the ordered degree of soy protein decreased, but the ordered structure had no variation when defatted soy flour was treated by enzymes with combination of acid, salt, and alkali. The SBAs contain more active functional groups and have better water resistance after curing.

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“…RSM is a statistical method based on the multivariate non-linear model and can be used for optimization of the resource use (Zulkali et al, 2006;Kalavathy et al, 2009). Furthermore, RSM consists of designing experiments to provide adequate and reliable measurements of the response, developing a mathematical model having the best fit to the data obtained from the experimental design, and determining the optimal value of the independent variables that produces a maximum or minimum response (Montgomery, 2001;Kalavathy et al, 2009). It is also useful for studying the interactions of various parameters affecting the response.…”

Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

“…RSM examines the responses of several factors by varying them simultaneously with a limited number of experiments. Therefore, RSM is a powerful tool for statistical modelling and optimization of the resources using lesser required number of experimental runs according to the experimental design (Cojocaru and Zakrzewska-Trznadel, 2007;Kalavathy et al, 2009). The response surface can be expressed as follows: (1) Where y is the response variable and xi is the independent variable.…”

Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

Management of nitrogen fertilizer, irrigation and plant density in onion production using response surface methodology as optimization approach

Mansouri¹,

Bannayan²,

Moghaddam³

et al. 2014

Afr. J. Agric. Res.

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Modelling, analysis and optimization of adsorption parameters for H3PO4 activated rubber wood sawdust using response surface methodology (RSM)

Cited by 184 publications

References 31 publications

Effect of Particle Size on the Sorption of Lead from Water by Different Species of Sawdust: Equilibrium and Kinetic Study

Effect of Particle Size on the Sorption of Lead from Water by Different Species of Sawdust: Equilibrium and Kinetic Study

Effect of Enzymatic Pretreatment on the Preparation and Properties of Soy-Based Adhesive for Plywood

Management of nitrogen fertilizer, irrigation and plant density in onion production using response surface methodology as optimization approach

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