An artificial neural network combined with response surface methodology approach for modelling and optimization of the electro-coagulation for cationic dye

Manisha S. Kothari, Kinjal G. Vegad, Kosha A. Shah, Ashraf Aly Hassan

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Article numbere08749
JournalHeliyon
Volume8
Issue number1
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Artificial neural network
  • Colour removal efficiency
  • Electrical energy consumption
  • Electrocoagulation process
  • Response surface methodology

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'An artificial neural network combined with response surface methodology approach for modelling and optimization of the electro-coagulation for cationic dye'. Together they form a unique fingerprint.

Cite this