Work

Artificial neural network model for extracting knowledge from the electro‐Fenton process for acid mine wastewater treatment

Year: 2024

Type: article

Abstract: In this study, artificial neural networks (ANNs) were employed to analyze the complex interactions between electro‐Fenton (EF) process variables (plate spacing, current intensity [CI], initial pH, aeration rate) and the Fe(II) and Mn(II) removal efficiency from wastewater. After experimenting with 69 different ANN architectures, the 4‐8‐8‐2 architecture was identified as more efficient, achieving higher accuracy (adj. R2 of 0.93 for Fe(II) and 0.96 for Mn(II)) than the published model. The research provides valuable insights into the correlation between EF process parameters and removal efficiency, guiding the optimization of wastewater treatment processes. Sensitivity analysis revealed that CI significantly affects Mn(II) and Fe(II) removal efficiency. A user‐friendly graphical interface was created based on the synaptic weights of the best model to enable practical predictions. It is designed to be accessible even to users without programing experience.

Source: CLEAN - Soil Air Water

DOI: 10.1002/clen.202400029

Authors: Anoop Kumar Maurya, Pasupuleti Lakshmi Narayana, Uma Maheshwera Reddy Paturi, Subba Reddy Nagireddy Gari

Institutions: Korea Institute of Materials Science, Gyeongsang National University

Language: English

Topic: Advanced oxidation water treatment

Subfield: Water Science and Technology

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: bronze

OpenAlex ID: W4402054094

FWCI:

Cites: 50

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Related to: 10