Electrochemical degradation of Reactive Black 5 with surface response and artificial neural networks optimization models

Electrochemical degradation of Reactive Black 5 with surface response and artificial neural networks optimization models

Artificial neural network modeling and statistical analysis are used to optimize electrochemical removal of Reactive Black 5 using a Ti/(RuO2)0.8-(Sb2O3)0.2 electrode. Experimental design was used to analyze the influence of dye concentration (26.4-93.6 ppm), electrolyte concentration (NaCl (0.0062-...

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Bibliographic Details
Published in:Separation science and technology Vol. 53; no. 16; pp. 2647 - 2661
Main Authors: Viana, Danilo F., Salazar-Banda, Giancarlo R., Leite, Manuela S.
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis Ltd 02-11-2018
Subjects:
Abbreviations
Absorbance
Artificial neural networks
Cadmium
Chemical oxygen demand
Chromium
CI Reactive Black 5
Color
Color removal
Colour
Current density
Degradation
Design of experiments
Dyes
Electrochemical oxidation
Electrochemistry
Electrodes
Electrolysis
Electrolytes
Energy consumption
Errors
Experimental design
Heavy metals
Mathematical models
Metal concentrations
Modelling
Neural networks
Optimization models
Organic chemistry
Oxidation
Oxidation process
Particle swarm optimization
Performance indices
Performance prediction
Removal
Response surface methodology
Sodium chloride
Statistical analysis
Statistical methods
Titanium
Wavelength
X-ray diffraction
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Summary:Artificial neural network modeling and statistical analysis are used to optimize electrochemical removal of Reactive Black 5 using a Ti/(RuO2)0.8-(Sb2O3)0.2 electrode. Experimental design was used to analyze the influence of dye concentration (26.4-93.6 ppm), electrolyte concentration (NaCl (0.0062-0.1238 M), and current density (0.62-12.38 mA cm-2) in a batch treatment system. The response surface methodology and artificial neural network were appropriate methods to optimize the operating conditions in electrochemical degradation process. Optimization models was developed to assess the performance of the electrochemical degradation, where color removal percentage, COD removal percentage, and the energy consumption (KW/m3) were considered. A total removal of color and decrease in up to 73.77% of chemical oxygen demand within 180 min of treatment was obtained using the optimized conditions. The final neural network model, characterized by a 4-8-3 architecture, presenting performance index determination coefficient (R2) of 0.982 and mean square error of 0.0146. The neural model developed demonstrated an efficiently predictive performance and to optimize the parameters of the electrochemical oxidation process.Abbreviations: Absi (λmáx): Initial absorbance in the maximum wavelength; Absf (λmáx): Final absorbance in the maximum wavelength; ANN: Artificial Neural Network; CD: Current Density; CE: Counter Electrode; COD: Chemical Oxygen Demand; CR: Color Removal; DY: Dye Concentration; E: Energy consumption; EC: Electrolyte Concentration; I: Current Applied; MMO: Mixed Metal Oxide; MSE: Mean Squared Error; n: Amount of Experiments; PSO: Particle Swarm Optimization; R2: Determination Coefficient; RB5: Reactive Black 5; RE: Reference Electrode; RMSE: Square Root Mean Error; T: Electrolysis Time; U: Neuron Activation Value; V: Voltage; Vs: Solution Volume; XRD: X-Ray Diffraction; WE: Working Electrode; yical: Removal Percentage Obtained by the Model; yiexp: Removal Percentage Obtained Experimentally; [beta]: Lower and Upper Limits
ISSN:0149-6395
1520-5754
DOI:10.1080/01496395.2018.1463264