Kinetic studies validated by Artificial Neural Network simulation for the removal of dye from simulated waste water by the activated carbon produced from Acalypha indica leaves

Veeraragavan, Aruna Janani; Shanmugavel, Rajesh; Abraham, Nesarani; Subramanian, Deepalakshmi; Pandian, Sivakumar

doi:10.1016/j.eti.2020.101244

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…In this regard, the impact of contact time on the removal of MO dye by activated carbon is depicted in Figure 6(d). From the results, it is inferred the equilibrium time and CT are in a direct relationship, i.e., adsorption increases with increase in time and attains equilibrium at the maximum adsorption capacity of adsorbent, which is attributed to the availability of adequate surface active sites on the sorbent surface for the adsorption of dye [38]. However, after reaching 180 min of CT, the equilibrium state has been reached due to the reduction of available active sites followed by the lowered concentration gradient of MO dye.…”

Section: Resultsmentioning

confidence: 92%

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Optimization Studies on Methyl Orange (MO) Dye Adsorption using Activated Carbon Nanoadsorbent of Ocimum basilicum Linn Leaves

Janani

Gokul

Dhivya

et al. 2023

Journal of Nanomaterials

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This study is focused on the application of activated carbon nanoadsorbent derived from Ocimum basilicum Linn (sweet basil) leaves for the removal of methyl orange dye from an aqueous solution. The Ocimum basilicum Linn leaves are dried, powdered, cured with H2SO4, and thermally treated to form an activated carbon biosorbent. Sorbent characterization studies like scanning electron microscope (SEM) and Fourier transform infrared (FTIR) spectroscopy have revealed the adsorption of the methyl orange dye from their aqueous solution in the batch mode process. The biosorbent has shown a maximum adsorption capacity of 1.54 mg g−1 at 10 mg l−1 concentration, 1.2 g sorbent dosage, pH of 3, contact time of 180 min, and pHpzc at 3.9. Experimental results are analyzed using equilibrium models and it is found that the Langmuir isotherm model and kinetic model fit well and also the results have corresponded well with pseudo-first order. The intraparticle diffusion (IPD) mechanism has shown that pore diffusion occurs at a slower rate. The Elovich model has displayed that adsorption is affected by film diffusion. From the statistical optimization studies, it is demonstrated that Box–Behnken model can correlate the good agreement between experimental and predicted values. The highest adsorption capacity for the nanoadsorbent was found using quadrate models and optimizing the variables at a time of 237 min, initial dye concentration of 5.31 mg l−1, adsorbent dose of 1.22 g, and pH of 4.23.

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Section: Resultsmentioning

confidence: 92%

“…The sorbent dosage is varied from 0.8 to 2 g and the q m value of adsorbent is raised from 0.829 to 1.357 mg g −1 , respectively. Improved dye penetration is facilitated by the available large surface area of sorbent and accessibility of adsorption sites [38]. At q m = 1.2 g, the maximum uptake capacity reaches an equilibrium state.…”

Section: Resultsmentioning

confidence: 99%

Optimization Studies on Methyl Orange (MO) Dye Adsorption using Activated Carbon Nanoadsorbent of Ocimum basilicum Linn Leaves

Janani

Gokul

Dhivya

et al. 2023

Journal of Nanomaterials

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“…Other recent studies on the ANN modeling of adsorption isotherms and kinetics include the fluoride adsorption on rice husk-derived biochar modified with Fe or Zn [158], the removal of brilliant green dye using mesoporous Pd–Fe magnetic nanoparticles immobilized on reduced graphene oxide [15], the adsorption of diazinon pesticide on a magnetic composite clay/graphene oxide/Fe 3 O 4 [159], the removal of crystal violet and methylene blue on magnetic iron oxide nanoparticles loaded with cocoa pod carbon composite [160], the arsenide removal employing mesoporous CoFe 2 O 4 /graphene oxide nanocomposites [161], the adsorption of perfluorooctanoic acid on copper nanoparticles and fluorine-modified graphene aerogel [17], the uptake of dicamba (3,6-dichloro-2-methoxy benzoic acid) by MIL-101(Cr) metal-organic framework [16], the phosphorous adsorption on polyaluminum chloride water treatment residuals [162], the use of iron doped-rice husk for the chromium adsorption/reduction [163], the removal of methyl orange dye by an activated carbon derived from Acalypha indica leaves [164], the lead adsorption by a hydrochar obtained from the KOH activated Crocus sativus petals [165], the adsorption of the cefixime antibiotic using magnetic composite beads of reduced graphene oxide-chitosan [13], the use of graphene oxide-cyanuric acid nanocomposite for the lead adsorption [14], the arsenic removal by an adsorbent consisting of iron oxide incorporated carbonaceous nanomaterial derived from waste molasses [12], the fluoride adsorption by chemically activated carbon prepared from industrial paper waste [18], the methylene blue adsorption with polyvinyl alcohol/carboxymethyl cellulose-based hydrogels [166], the modeling of adsorption properties of biochar and resin for the removal of organic compounds [167], and the removal of lead from waster with a magnetic nanocomposite [168].…”

Section: Applications Of Anns To Model the Adsorption Of Water Pollut...mentioning

confidence: 99%

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

Reynel-Ávila

Aguayo‐Villarreal

Díaz-Muñoz

et al. 2022

Adsorption Science & Technology

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The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

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“…It has the advantages of low cost, no secondary pollution, high efficiency, and eco-friendly recycling resources [ 9 ]. Currently, the desired bioremediation materials can be obtained from various sources, such as the plants Acalypha indica [ 10 ], Saccharomyces [ 11 ], Aspergillus [ 12 ], white-rot fungi [ 13 ], and algae [ 14 , 15 ], which have been widely used for the removal of organic pollutants from wastewater. In addition, it has been reported that peroxidases [ 16 ] and laccases [ 17 , 18 ] among biological enzymes also degrade organic pollutants; however, existing studies have shown that free microorganisms/enzymes have low stability, short service lives, and are not easy to separate and recover, which limits their further industrial application [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Immobilization of Biomass Materials for Removal of Refractory Organic Pollutants from Wastewater

Liu

Yang

Zhang

et al. 2022

IJERPH

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In the field of environmental science and engineering, microorganisms, enzymes and algae are promising biomass materials that can effectively degrade pollutants. However, problems such as poor environmental adaptability, recycling difficulties, and secondary pollution exist in the practical application of non-immobilized biomass materials. Biomass immobilization is a novel environmental remediation technology that can effectively solve these problems. Compared with non-immobilized biomass, immobilized biomass materials have the advantages of reusability and stability in terms of pH, temperature, handling, and storage. Many researchers have studied immobilization technology (i.e., methods, carriers, and biomass types) and its applications for removing refractory organic pollutants. Based on this, this paper reviews biomass immobilization technology, outlines the mechanisms and factors affecting the removal of refractory organic pollutants, and introduces the application of immobilized biomass materials as fillers for reactors in water purification. This review provides some practical references for the preparation and application of immobilized biomass materials and promotes further research and development to expand the application range of this material for water purification.

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Kinetic studies validated by Artificial Neural Network simulation for the removal of dye from simulated waste water by the activated carbon produced from Acalypha indica leaves

Cited by 9 publications

References 27 publications

Optimization Studies on Methyl Orange (MO) Dye Adsorption using Activated Carbon Nanoadsorbent of Ocimum basilicum Linn Leaves

Optimization Studies on Methyl Orange (MO) Dye Adsorption using Activated Carbon Nanoadsorbent of Ocimum basilicum Linn Leaves

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

Immobilization of Biomass Materials for Removal of Refractory Organic Pollutants from Wastewater

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