Effective removal of arsenide from aqueous solutions using mesoporous CoFe2O4/graphene oxide nanocomposites assisted by artificial intelligence

Wu, Xianliang; Hu, Jiwei; Wang, Xingfu; Xin, Ling; Li, Caifang; Wei, Xionghui

doi:10.1007/s42823-021-00239-0

Cited by 7 publications

(3 citation statements)

References 56 publications

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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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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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“…A genetic algorithm is a random global search and optimization method that imitates the development of a natural biological evolution system. This algorithm can automatically obtain and accumulate knowledge about the search space during the search process and adaptively control the search process to obtain the optimal solution (Wu et al 2021). PSO is a population intelligence algorithm that is designed by simulating the predation behaviors of birds.…”

Section: Neural Network Modelingmentioning

confidence: 99%

Photocatalytic degradation of tetracycline hydrochloride by novel Fe3O4/g-C3N4/rGO magnetic nanocomposites mechanism: modeling and optimization

Shan

et al. 2022

Preprint

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The photocatalytic degradation of antibiotics requires a good separation efficiency of photogenerated electron-hole pairs and a wide visible light absorption range. Current studies have discussed the successful preparation of ferroferric oxide/graphite carbon nitride/reduced graphene oxide (Fe3O4/g-C3N4/rGO). The phase structure and morphology of Fe3O4/g-C3N4/rGO composites were characterized by XRD, HR-TEM, SEM and EDS. The obtained composites were used to degrade tetracycline hydrochloride to evaluate its photocatalytic activity. The effects of four variables on the degradation of tetracycline hydrochloride were analyzed by the response surface method and artificial intelligence (gradient regression tree, random forest, artificial neural network, etc.). The results showed that the graphite carbon nitride in the catalyst maintained its original structure and that the photocatalytic activity was significantly improved. The degradation rate of tetracycline hydrochloride was 86.7%. The artificial neural network combined with a genetic algorithm was the best model for predicting the degradation of tetracycline hydrochloride by nanocomposites. The degradation of tetracycline hydrochloride was in accordance with the pseudosecond-order kinetic model. The proposed photocatalytic mechanism study indicated that ∙O2− and ∙OH radicals are the foremost reactive species that have vital roles in the photodegradation process. This finding provides a theoretical reference for the photocatalytic degradation of antibiotics (or analogous pollutants) in wastewater.

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“…It is based on the physiological connections within the brain, and its purpose is to simulate some mechanisms of the brain and achieve several speci c functions (Wu et al, 2020). At present, ANN has been applied in numerous elds, including water environmental monitoring and assessment (Aghav et al, 2011;Wu et al, 2021). Shi et al (2021) analyzed the back propagation arti cial neural network, a self-adapting algorithm, proposed to assess cumulative risks to aquatic ecosystems.…”

Section: Introductionmentioning

confidence: 99%

The effects of socioeconomic activities on water quality in Hainan Island, south China

Qiao

et al. 2023

Preprint

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Water quality is intimately related to the livelihood of the numerous people, and affects the development and operating benefits of various industries in society. This study clarifies the effects of human-driven economic activities on inland water quality in Hainan Island, and reveals relationships between water quality and tourism on the island. Based on previously monitored data, this study uses a static Bayesian network and radial basis function neural network (RBFNN) to model and predict the future water quality. From 2012 to 2015, water quality in the Nandu, Wanquan and Changhua Rivers was good (at level II, GB3838-2002). The static Bayesian network demonstrated that Gross Output Value (GOV) of agriculture, GOV of fishery, GOV of animal husbandy and chemical oxygen demand (COD) discharge will significantly affect water quality in the Nandu and Changhua Rivers. The effect of tourism on water quality in Wanquan River was significantly higher than that on the Nandu and Changhua Rivers. In the Wanquan River, the DO content fluctuated greatly in comparison to the other two rivers, and unexpectedly, increased tourism led to higher DO values. However, it remains necessary to closely monitor negative changes in water quality due to tourism, especially in Wanquan River and eastern Hainan province. The developed RBFNN showed that the changes in water quality were predicted accurately in comparison with experimental values in the present study and the water quality also is continuously improving. Overall, results suggest that current anthropogenic socioeconomic activities had a modest effect on water quality in Hainan Island. Agriculture, fishery, animal husbandy and COD discharge were relatively important factors affecting water quality, while tourism had a perceptible effect in eastern Hainan. Our findings provide a reference for the balance of water quality, people’s livelihood and economic development (tourism and port construction) in Hainan province.

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Effective removal of arsenide from aqueous solutions using mesoporous CoFe2O4/graphene oxide nanocomposites assisted by artificial intelligence

Cited by 7 publications

References 56 publications

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

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

Photocatalytic degradation of tetracycline hydrochloride by novel Fe3O4/g-C3N4/rGO magnetic nanocomposites mechanism: modeling and optimization

The effects of socioeconomic activities on water quality in Hainan Island, south China

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