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A platform for research: civil engineering, architecture and urbanism
Artificial neural network model for extracting knowledge from the electro‐Fenton process for acid mine wastewater treatment
https://orcid.org/0000-0001-5843-466X
https://orcid.org/0000-0003-4206-4515
AbstractIn 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.
Artificial neural network model for extracting knowledge from the electro‐Fenton process for acid mine wastewater treatment
https://orcid.org/0000-0001-5843-466X
https://orcid.org/0000-0003-4206-4515
AbstractIn 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.
Artificial neural network model for extracting knowledge from the electro‐Fenton process for acid mine wastewater treatment
https://orcid.org/0000-0001-5843-466X
https://orcid.org/0000-0003-4206-4515
AbstractIn 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.
Artificial neural network model for extracting knowledge from the electro‐Fenton process for acid mine wastewater treatment
CLEAN Soil Air Water
Maurya, Anoop Kumar (author) / Narayana, Pasupuleti Lakshmi (author) / Paturi, Uma Maheshwera Reddy (author) / Nagireddy Gari, Subba Reddy (author)
2024-10-01
Article (Journal)
Electronic Resource
English
Removing sulfide from spent caustic petrochemical wastewater with electro-Fenton treatment
| Taylor & Francis Verlag | 2021