Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) approaches

Abstract

Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from an aqueous medium. An adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward artificial neural network (ANN) were used in this study. Four operational factors were chosen to examine their influence on the adsorption study: pH, contact duration, initial Cu (II) ions concentration, and adsorbent dosage. Using sawdust from wood, prediction models of copper (II) ions adsorption were optimized, created, and developed using the ANN and ANFIS models for tests. The result indicates that the determination coefficient for copper (II) metal ions in the training dataset was 0.987. Additionally, the ANFIS model’s R² value for both pollutants was 0.992. The findings demonstrate that the models presented a promising predictive approach that can be applied to successfully and accurately anticipate the simultaneous elimination of copper (II) and dye from the aqueous solution.

Keywords:

• ANN
• ANFIS
• modeling
• copper (II), sawdust
• adsorption

Acknowledgments

The Tshwane University of Technology is acknowledged for providing facilities.

Author contribution

Banza Jean Claude: conceptualization, methodology, formal analysis, investigation, data curation, writing, writing- original draft.

Maurice Stephane Onyango: formal analysis, validation, formal analysis, investigation, writing-reviewing, and editing.

Disclosure statement

The authors declare that they have no known competition for financial interests or personal relationships that could have influenced the work reported in this article.

Data availability statement

The data supporting this study’s findings are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.