Single and competitive dye adsorption onto chitosan–based hybrid hydrogels using artificial neural network modeling

Pauletto, Paola S.; Gonçalves, Janaína Oliveira; Pinto, Luiz Antônio de Almeida; Dotto, Guilherme L.; Salau, Nina Paula Gonçalves

doi:10.1016/j.jcis.2019.10.106

Cited by 81 publications

(47 citation statements)

References 35 publications

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“…Application of ANN in the textile industry has also been observed in recent decades, such as: in the prediction of the surface temperature of the fabrics (Bahadir et al, 2019); in heat and moisture propagation in light nonwoven fabrics (Rahnama et al, 2013); in the interaction between hetero cyclic dyestuffs and cellulose (Funar-Timofei et al, 2012); in removing dyes from textile e uents via adsorption onto chitosan-based hybrid hydrogels (Pauletto et al, 2020), by radiolytic degradation (Padmanaban et al, 2018), by biodegradation (Prado et al, 2013;Torbati et al, 2014) or UV/H 2 O 2 , Fenton or photo-Fenton advanced oxidation processes (do Nascimento et al, 2017;Mohajerani et al, 2011;Rosa et al, 2015;Rosa et al, 2020;de Moraes et al, 2021).…”

Section: Ann In the Textile Industrymentioning

confidence: 99%

“…This is because of the large quantities of inorganic salt and alkali used to ensure e cient utilisation and xation of dyestuffs, which are largely non-biodegradable and toxic to aquatic life (Fernandes et al, 2020;Rosa et al, 2015). Moreover, many commercial dyestuffs are a cocktail of active dyestuff and various by-products that are recalcitrant to biological degradation and accumulate in the e uent after the dyeing process (Pauletto et al, 2020). Conventional wastewater treatment processes cannot degrade such compounds or detoxify the e uent.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

Rosa

Guerhardt

Júnior

et al. 2021

Preprint

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This work explores the modeling and optimization of the conditions to obtain a set of blue pigments for dyeing reactive cotton, by means of an approach that combines the techniques response surface methodology (RSM) and artificial neural network (ANN). By means of RSM technique the interactions and the effects of the main process variables (factors) on the behavior of coloristic intensity (K.S-1) were investigated. For this, a 26 central composite rotational design (CCRD) was carried out considering the factors temperature, NaCl, Na2CO3, NaOH, processing time and RB5 concentration. The results obtained show that all investigated factors have considerable effect on the behavior of K.S-1. The data produced in the dyeing experiments were used to build and train a Multilayer Perceptron ANN (MLP-ANN) to predict K.S-1, being the input layer of the MLP-ANN designed according to the results achieved by the RSM. The non-linear behavior of dyeing with RB5 was successfully modeled by a three-layer MLP-ANN comprising 6 input neurons, 15 hidden neuros, and 1 output neuron to indicate the value of K.S-1. The results achieved in the performed simulations confirmed the ANN effectiveness to predict K.S-1 values in RB5 the dyeing process, with high coefficient of determination (R2=0.942). The developed approach allowed the composition of a table containing optimized conditions to obtain a set of colors of the blue palette using RB5 dye, varying from sky blue to oxford blue, which will facilitate the assembly of the dyes. Finally, the experiments conducted in this work allowed the development of a computational tool to support the dyeing process, saving chemical inputs and time in cotton dyeing with specific dyestuff.

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Section: Ann In the Textile Industrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

Rosa

Guerhardt

Júnior

et al. 2021

Preprint

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“…Signals were disseminated to the hidden neuron via a system of weighted connections where the actual processing was performed and were nally disseminated to the output layer. 47 The number of hidden neurons was determined on the basis of trial and error, which forms the training process. 48 Two neurons that show the adsorption capacities of dicamba and MCPA were predicted as the output layer.…”

Section: Articial Neural Network (Ann)mentioning

confidence: 99%

Adsorption of dicamba and MCPA onto MIL-53(Al) metal–organic framework: response surface methodology and artificial neural network model studies

et al. 2020

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“…Wastewater pollutants include dyes, surfactants, oils, lubricants, organic solvents, petroleum derivatives, and pharmaceuticals such as antibiotics, anti-allergy, and hormones [ 1 , 2 ]. Artificial dyes, which are largely used in various industries such as textiles, food, cosmetics, leather, paper, and pharmaceuticals, are highly dangerous organic pollutants [ 3 , 4 ]. Dyes are mutagenic agents even at low concentrations and render an undesirable color to water bodies [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Hydrogels Based on Poly([2-(acryloxy)ethyl] Trimethylammonium Chloride) and Nanocellulose Applied to Remove Methyl Orange Dye from Water

et al. 2021

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Bio-based hydrogels that adsorb contaminant dyes, such as methyl orange (MO), were synthesized and characterized in this study. The synthesis of poly([2-(acryloyloxy)ethyl] trimethylammonium chloride) and poly(ClAETA) hydrogels containing cellulose nanofibrillated (CNF) was carried out by free-radical polymerization based on a factorial experimental design. The hydrogels were characterized by Fourier transformed infrared spectroscopy, scanning electron microscopy, and thermogravimetry. Adsorption studies of MO were performed, varying time, pH, CNF concentration, initial dye concentration and reuse cycles, determining that when the hydrogels were reinforced with CNF, the dye removal values reached approximately 96%, and that the material was stable when the maximum swelling capacity was attained. The maximum amount of MO retained per gram of hydrogel (q = mg MO g−1) was 1379.0 mg g−1 for the hydrogel containing 1% (w w−1) CNF. Furthermore, it was found that the absorption capacity of MO dye can be improved when the medium pH tends to be neutral (pH = 7.64). The obtained hydrogels can be applicable for the treatment of water containing anionic dyes.

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Single and competitive dye adsorption onto chitosan–based hybrid hydrogels using artificial neural network modeling

Cited by 81 publications

References 35 publications

Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

Adsorption of dicamba and MCPA onto MIL-53(Al) metal–organic framework: response surface methodology and artificial neural network model studies

Hydrogels Based on Poly([2-(acryloxy)ethyl] Trimethylammonium Chloride) and Nanocellulose Applied to Remove Methyl Orange Dye from Water

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