Sonophotocatalytic treatment of AB113 dye and real textile wastewater using ZnO/persulfate: Modeling by response surface methodology and artificial neural network

Asgari, Ghorban; Shabanloo, Amir; Salari, Mehdi; Eslami, Fatemeh

doi:10.1016/j.envres.2020.109367

Cited by 137 publications

(55 citation statements)

References 62 publications

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“…Recently, the textile industry has been studying the ANN combined with response surface methodology (RSM) in its processes (Vedaraman et al, 2017) and mainly in the treatment of textile e uent, such as: in reactive blue 21 dye removal by photoozonation process (Mehrizad and Gharbani, 2016), in the degradation of organic dyes by sonochemistry combined with Au, Ag and Pd nanoparticle (Moghaddari et al, 2018), in sonophotocatalytic degradation of AB113 dye using ZnO/persulfate (Asgari et a., 2020) or in azo dye RB5 biodegradation using fungal enzymes (Fernandes et al, 2020). However, there are no studies using ANN and RSM techniques in predicting the color of a dye using the intensity of color.…”

Section: Ann In the Textile Industrymentioning

confidence: 99%

“…In this sector, reactive dyestuff, which reacts with the bre and water, is used widely for dyeing cellulosic bres. Because of its reaction with water, this dyestuff is a major constituent of e uent; approximately 10-15% of the total dyestuff is disposed as a part of the e uent (Asgari et a., 2020;Lucato et al, 2012;Prado et al, 2013). Therefore, it is important to optimize dyeing processes that use this reactive dyestuff in order to obtain the best coloristic intensity, minimise the salt discharge, and reduce the pollutants in the generated 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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“…Oxidants as a source of radicals could be added into some AOPs to enhance degradation efficiency. Hydrogen peroxide (H 2 O 2 ), 10 ozone (O 3 ) and persulfate (PS) have been used to enhance sonophotocaltyic treatment performance 9,11 …”

Section: Introductionmentioning

confidence: 99%

Hydrogen peroxide enhanced sonophotocatalytic degradation of acid orange 7 in aqueous solution: optimization by Box–Behnken design

Wei

Rahaman²,

Zhao

et al. 2021

J of Chemical Tech & Biotech

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BACKGROUND Sonophotocatalysis has gained great attention for research into practical applications to treat refractory organic dye from wastewater. However, its degradation efficiency needs to be improved further. To further enhance the sonophotocatalytic performance, here we report a novel hydrogen peroxide (H2O2)‐assisted titania (TiO2)‐based sonophotocatalyic system (H2O2‐US‐UV/TiO2) for degradation of acid orange 7 (AO7). RESULTS Effects of operating parameters including catalyst dosage, concentration of the oxidative agents and initial pH were analyzed through a response surface method coupled with Box–Behnken experimental design (RSM‐BBD) and experiments. Introducing H2O2 into the US‐UV/TiO2 system can significantly enhance AO7 removal. A quadratic model was proposed for predicting AO7 degradation using the RSM‐BBD method, and the statistical significance of the model was confirmed by ANOVA. The real AO7 removal efficiency (95.8%) under experimental conditions of 0.5 g L−1 TiO2, 4 mmol L–1 H2O2, and initial pH 3 was comparable with the predicated value (98.6%) obtained from this model. Quenching tests revealed that the hydroxyl radical (˙OH) was the key reactive species in the sonophotocatalytic system. Degradation products including 2‐naphthol, 4‐carboxylphenylglycine, cinnamic acid and 1,10‐Decanediol were identified. CONCLUSION Adding H2O2 improved the degradation performance of AO7 in the US‐UV/TiO2 system; H2O2 concentration was a more significant experimental factor than initial pH and TiO2 dosage. A degradation pathway of AO7 removal was suggested according to results of liquid chromatography‐mass spectrometry. The novel hybrid H2O2‐US‐UV/TiO2 system can be utilized as an efficient process for recalcitrant dye wastewater treatment. © 2021 Society of Chemical Industry (SCI).

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“…Furthermore, biological processes are necessary long time and high energy for treatment [15]. For this reason, it is necessary to use different methods in which the dyes are mineralized or converted into more biodegradable substances [19].…”

Section: Introductionmentioning

confidence: 99%

Color and COD Removal from Real Textile Wastewater using Nanoscale Zero-Value Iron (nZVI)

Tepe

Tunç

Hanay

2021

Gazi University Journal of Science

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Nanoscale zero-value iron (nZVI) has a high specific surface area and significant abilities to reduce contaminants. In this study, the removal of color and COD from real textile wastewater was studied by nZVI particles. A series of experiments were conducted to evaluate the effect of operational parameters such as solution pH, nZVI dosage and temperature on color and COD removal. The results showed that the color and COD removals mainly depend on solution pH. The color removal efficiencies were 96.3% at 436 nm, 97.8% at 525 nm and 98.0% at 620 nm, respectively at nZVI dosage of 0.3 g/L and initial pH of 3 after 180 minutes of reaction time at 25 ○C. Moreover, the maximum COD removal efficiency obtained under these conditions was 86%.

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Sonophotocatalytic treatment of AB113 dye and real textile wastewater using ZnO/persulfate: Modeling by response surface methodology and artificial neural network

Cited by 137 publications

References 62 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

Hydrogen peroxide enhanced sonophotocatalytic degradation of acid orange 7 in aqueous solution: optimization by Box–Behnken design

Color and COD Removal from Real Textile Wastewater using Nanoscale Zero-Value Iron (nZVI)

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