Adsorptive removal of heavy metals by magnetic nanoadsorbent: an equilibrium and thermodynamic study

Shirsath, Dhanraj S.; Shirivastava, V. S.

doi:10.1007/s13204-014-0390-6

Cited by 62 publications

(20 citation statements)

References 22 publications

(20 reference statements)

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“…The objective of this study was the utilization of magnetite nanomaterials as adsorbent for the removal from wastewater of a resilient cationic surfactant, benzyldodecyldimethylammonium bromide (benzalkonium bromide). Taking into consideration the disadvantages of conventional methods and conventional adsorbent materials used for the wastewater treatment such as low efficiency for pollutants removal at low concentration, it was chosen a nanomaterial which have more advantages compared with the conventional ones: small particle size, increased surface area, therefore improving adsorption capacities for the removal of pollutants form wastewater [30,31]. Treatment of wastewater containing surfactants by adsorbent nanomaterials offered a non-polluting alternative to the standard one.…”

Section: Introductionmentioning

confidence: 99%

Benzalkonium Bromide Cationic Surfactant Removal from Wastewater Using Magnetite Nanomaterial

Păun¹,

Covaliu²,

Pirvu³

et al. 2020

Rev. Chim.

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The cationic surfactants have various applications being found in personal care products, detergents, cleaning products, disinfectants, hair conditioners, etc. Cationic surfactants are presented in domestic or industrial wastewater, being very resistant pollutants to biodegradation during conventional wastewater treatment (WT) methods, which generated the need to develop new efficient alternative methods. One of the alternative to conventional WT methods involves the utilization of the adsorbent nanomaterials, which are highly efficient, easy to operate, environmental friendly and cost effective. In this study, it was investigated the adsorption process of a cationic surfactant, benzyldodecyldimethylammonium bromide (benzalkonium bromide) from wastewater on magnetite nanomaterial. The adsorption investigation of benzalkonium bromide was analyzed by TOC analysis and the maximum adsorption efficiency obtained was 91.4 %. The experimental data fitted very well with Langmuir model, the correlation coefficient (R2) being 0.9792.

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Section: Introductionmentioning

confidence: 99%

Benzalkonium Bromide Cationic Surfactant Removal from Wastewater Using Magnetite Nanomaterial

Păun¹,

Covaliu²,

Pirvu³

et al. 2020

Rev. Chim.

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“…The most popular kinetic models are Lagergren pseudo first-order and pseudo second-order kinetics. Among them, the pseudo second-order kinetic model has been regarded as the best fitted model with aqueous chromium adsorption onto different magnetic nanosorbents [11], aqueous Zn(II) ion adsorption onto Fe 3 O 4 NPs [9], and aqueous Cu(II) ion adsorption onto CMNPs [10].…”

Section: Adsorption Kinetics Modelmentioning

confidence: 99%

“…For the purpose of the present study, we refer to data formerly obtained in three other works. The extraction of zinc(II) ions via the bath adsorption process on magnetic Fe 3 O 4 nanoparticles (case study 1) from wastewater has already been carried out and reported on by Shirsath and Shirivastava [9], who concluded that the process could be influenced by different factors, such as the pH level, contact time (t), adsorbent dose, and initial concentration of Zn(II) ions. See Table 5 for a summary of the details.…”

Section: Data Collectionmentioning

confidence: 99%

ANN-assisted forecasting of adsorption efficiency to remove heavy metals

Buaisha¹,

Balku²,

Özalp-Yaman³

2019

Turk J Chem

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In wastewater treatment, scientific and practical models utilizing numerical computational techniques such as artificial neural networks (ANNs) can significantly help to improve the process as a whole through adsorption systems. In the modeling of the adsorption efficiency for heavy metals from wastewater, some kinetic models have been used such as pseudo first-order and second-order. The present work develops an ANN model to forecast the adsorption efficiency of heavy metals such as zinc, nickel, and copper by extracting experimental data from three case studies. To do this, we apply trial-and-error to find the most ideal ANN settings, the efficiency of which is determined by mean square error (MSE) and coefficient of determination (R 2). According to the results, the model can forecast adsorption efficiency percent (AE%) with a tangent sigmoid transfer function (tansig) in the hidden layer with 10 neurons and a linear transfer function (purelin) in the output layer. Furthermore, the Levenberg-Marquardt algorithm is seen to be most ideal for training the algorithm for the case studies, with the lowest MSE and high R 2. In addition, the experimental results and the results predicted by the model with the ANN were found to be highly compatible with each other.

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“…These methods have been found to be limited, because of the high capital and operating costs or the infectiveness in meeting stringent effluent standards. Therefore several approaches have been studied for the development of inexpensive and abundant adsorbents such as sawdust [10], live biomass [11], clay [12,13] and agricultural byproducts, Zn(II) removal by magnetic nanoadsorbents [14][15][16][17]. Each methods has been found to be limited for the cost, complexity and efficiency, as well as secondary wastes, for example the electrolysis process often take higher operational costs and chemical precipitation may generates secondary wastes [18].…”

Section: Introductionmentioning

confidence: 99%

Rapid Removal of Nickel(II) by Coconut Leaves Powder as Bioadsorbent

Lalsing¹

2019

JACS

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In the present paper adsorption technique was employed for the removal of heavy metal Ni(II). The heavy metals are important for proper functioning of biological systems but their deficiency or excess lead to a large number of disorders. Industrial effluents discharged contain higher amount of heavy metals such as Ni, Cd, Hg, Cr and Cu. In this research article low cost and eco-friendly coconut leaves powder as a bioadsorbent were employed for the removal of Ni(II) from industrial wastewater. The batch adsorption study were conducted for the removal of Ni(II) along with different parametric studies of Initial concentration of nickel metal, adsorbent dose, pH, contact time and temperature. The coconut leaves powder bioadsorbent as an experimental research was applied for the systematic and effective investigation to determine the optimal conditions of the operation variables. The employed coconut leaves powder (CLP) bioadsorbent gives positive results, the adsorption of Ni(II) is very rapid and most of fixation occurs at first 15 minutes. The adsorption kinetics obeys second order kinetic. The CLP adsorbent were characterized by SEM analysis before and after treatment, which shows sufficient adsorption of Ni(II) on CLP. The result suggests that coconut leaves powder bioadsorbent can be beneficial in Ni(II) removal from the contaminated water.

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Adsorptive removal of heavy metals by magnetic nanoadsorbent: an equilibrium and thermodynamic study

Cited by 62 publications

References 22 publications

Benzalkonium Bromide Cationic Surfactant Removal from Wastewater Using Magnetite Nanomaterial

Benzalkonium Bromide Cationic Surfactant Removal from Wastewater Using Magnetite Nanomaterial

ANN-assisted forecasting of adsorption efficiency to remove heavy metals

Rapid Removal of Nickel(II) by Coconut Leaves Powder as Bioadsorbent

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