Abstract
An artificial neural network (ANN) approach with response surface methodology (RSM) technique has been applied to model and optimize the removal process of Brilliant Green dye by batch electrocoagulation process. A multilayer perceptron (MLP) - ANN model has been trained by four input neurons which represent the reaction time, current density, pH, NaCl concentration, and two output neurons representing the dye removal efficiency (%) and electrical energy consumption (kWh/kg). The optimized hidden layer neurons were obtained based on a minimum mean squared error. The batch electrocoagulation process was optimized using central composite design with RSM once the ANN network was trained and primed to anticipate the output. At optimized condition (electrolysis time 10 min, current density 80 A/m2, initial pH 5 and electrolyte NaCl concentration 0.5 g/L), RSM projected decolorization of 98.83% and electrical energy consumption of 14.99 kWh/kg. This study shows that the removal of brilliant green dye can be successfully carried out by a batch electrocoagulation process. Therefore, the process is successfully trained by ANN and optimized by RSM for similar applications.
Highlights
Industries like textiles, leather, paper, pulp, printing, and dyeing are major consumers of synthetic dyes
An artificial neural network (ANN)- response surface methodology (RSM) combination is an emerging reproducible process modelling and optimization technique used by a few researchers [21, 22] for dye removal, to save time and cost of experimentation
A backpropagation algorithm was used, which trains itself with a Electrolysis Time (x1, minutes) Current Density (x2, A/m2)
Summary
Industries like textiles, leather, paper, pulp, printing, and dyeing are major consumers of synthetic dyes. As the electrocoagulation process is a current induced approach, the removal should be optimized with the energy consumption to achieve cost reduction for industrial application. An artificial neural network (ANN)- response surface methodology (RSM) combination is an emerging reproducible process modelling and optimization technique used by a few researchers [21, 22] for dye removal, to save time and cost of experimentation. The CCD being a robust technique, is successfully used recently for optimization by researchers for adsorption of heavy metals on multi-wall carbon nanotubes [26, 27], graphene oxide [28, 29], and oxidative removal of dye by Fenton's reagent [30]
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