Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO

Fan, Mingyi; Hu, Jiwei; Cao, Rensheng; Xiong, Kangning; Wei, Xionghui

doi:10.1038/s41598-017-18223-y

Cited by 99 publications

(34 citation statements)

References 63 publications

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“…The relative influence of the individual variable was calculated by the following Garson equation [ 37 , 38 ]:

where I ab is the relative importance of the j th input variable to the output variable; w x is the connection weight; and a , e , and b are the number of neurons in the input layer, hidden layer, and output layer, respectively.…”

Section: Methodsmentioning

confidence: 99%

Removal of Crystal Violet by Using Reduced-Graphene-Oxide-Supported Bimetallic Fe/Ni Nanoparticles (rGO/Fe/Ni): Application of Artificial Intelligence Modeling for the Optimization Process

Ruan

et al. 2018

Materials

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Reduced-graphene-oxide-supported bimetallic Fe/Ni nanoparticles were synthesized in this study for the removal of crystal violet (CV) dye from aqueous solutions. This material was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS), Raman spectroscopy, N2-sorption, and X-ray photoelectron spectroscopy (XPS). The influence of independent parameters (namely, initial dye concentration, initial pH, contact time, and temperature) on the removal efficiency were investigated via Box–Behnken design (BBD). Artificial intelligence (i.e., artificial neural network, genetic algorithm, and particle swarm optimization) was used to optimize and predict the optimum conditions and obtain the maximum removal efficiency. The zero point of charge (pHZPC) of rGO/Fe/Ni composites was determined by using the salt addition method. The experimental equilibrium data were fitted well to the Freundlich model for the evaluation of the actual behavior of CV adsorption, and the maximum adsorption capacity was estimated as 2000.00 mg/g. The kinetic study discloses that the adsorption processes can be satisfactorily described by the pseudo-second-order model. The values of Gibbs free energy change (ΔG0), entropy change (ΔS0), and enthalpy change (ΔH0) demonstrate the spontaneous and endothermic nature of the adsorption of CV onto rGO/Fe/Ni composites.

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“…The relative influence of the individual variable was calculated by the following Garson equation [ 37 , 38 ]:

Section: Methodsmentioning

confidence: 99%

Removal of Crystal Violet by Using Reduced-Graphene-Oxide-Supported Bimetallic Fe/Ni Nanoparticles (rGO/Fe/Ni): Application of Artificial Intelligence Modeling for the Optimization Process

Ruan

et al. 2018

Materials

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“…Large relative errors are noticed in the P f estimates < 5%, however results indicated that an absolute error and mean absolute error are more appropriate for this estimate. Such a model evaluation metric is commonly used in regression analysis [35] and RSM predictions [36] for percentage quantities. The maximum absolute error in P f prediction is 4% and the MAE is about 2%.…”

Section: Validation Simulationsmentioning

confidence: 99%

Geometry optimization for structural reliability and performance of a thermoelectric generator

Karri

2019

SN Appl. Sci.

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The opposing requirements for the performance and reliability of a thermoelectric generator (TEG) are investigated with numerical finite element analysis simulations. The parameters that significantly influence the operational performance and structural reliability of a TEG are considered for simultaneous optimization. The design of experiments based response surface optimization methodology in conjunction with results from finite element analysis was employed to identify optimal parameters from the structural reliability and performance (power output and efficiency) perspectives. A high temperature bismuth telluride based TEG couple was used in the studies to demonstrate the general applicability of the methodology. The response surface methodology is found to be a robust technique that can expedite the process of geometry optimization of a TE device during the research and development phase of TEG devices fabricated from novel thermoelectric materials.

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“…ANN model was proposed to investigate copper removal from wastewater by adsorption on fungal biomass [27] and cadmium sorption by Shelled Moringa Oleifera Seed Powder from aqueous solution [28]. Moreover, ANN genetic algorithm and particle swarm optimization modeling was used for the prediction of copper removal from aqueous solutions by reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) magnetic nanocomposites [29]. The Cr(VI) removal efficiency of PIMs cannot be determined by conventional mathematical techniques alone because of non-linear, differential and complicated process.…”

Section: Introductionmentioning

confidence: 99%

Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes

Yaqub

Eren

Eyüpoğlu

2019

Environmental Engineering Research

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In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R 2 values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.

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Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO

Cited by 99 publications

References 63 publications

Removal of Crystal Violet by Using Reduced-Graphene-Oxide-Supported Bimetallic Fe/Ni Nanoparticles (rGO/Fe/Ni): Application of Artificial Intelligence Modeling for the Optimization Process

Removal of Crystal Violet by Using Reduced-Graphene-Oxide-Supported Bimetallic Fe/Ni Nanoparticles (rGO/Fe/Ni): Application of Artificial Intelligence Modeling for the Optimization Process

Geometry optimization for structural reliability and performance of a thermoelectric generator

Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes

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