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Related Experiment Video

Updated: Jun 9, 2026

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for Cu(II) Through Microwave Pre-Pyrolysis
10:44

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for Cu(II) Through Microwave Pre-Pyrolysis

Published on: February 12, 2019

Biomass-Derived Activated Carbon for Congo Red Dye Adsorption: Machine-Learning-Based Prediction and Comparative

Sujesh Sudarsan1, Ramesh Vinayagam1, Raja Selvaraj1

  • 1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.

ACS Omega
|June 8, 2026
PubMed
Summary

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Machine learning models accurately predict Congo Red dye removal using activated carbon from Spathodea campanulata flowers. Adaptive neuro-fuzzy inference system (ANFIS) demonstrated the best performance for optimizing dye adsorption.

Area of Science:

  • Environmental Chemistry
  • Materials Science
  • Computational Chemistry

Background:

  • Congo Red (CR) is a carcinogenic azo dye prevalent in industrial wastewater.
  • Effective removal of CR is crucial due to its persistence and environmental risks.
  • Activated carbon from biomass offers a sustainable adsorbent material.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting CR dye adsorption onto Spathodea campanulata activated carbon (SCAC).
  • To identify the most effective modeling approach for optimizing CR removal processes.
  • To understand the influence of operational parameters on dye adsorption.

Main Methods:

  • Utilized a dataset of 180 batch experiments with varying pH, dosage, initial CR concentration, contact time, and temperature.

Related Experiment Videos

Last Updated: Jun 9, 2026

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for Cu(II) Through Microwave Pre-Pyrolysis
10:44

Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for Cu(II) Through Microwave Pre-Pyrolysis

Published on: February 12, 2019

  • Constructed and compared four machine learning models: Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR).
  • Performed global sensitivity analysis on the optimized ANFIS model to rank parameter importance.

    • Main Results:

    • ANFIS (R² = 0.9722) and ANN (R² = 0.9710) exhibited superior performance in modeling CR adsorption compared to SVM (R² = 0.9597) and MLR (R² = 0.8667).
    • MLR showed limited capability in capturing the nonlinear adsorption behavior.
    • Sensitivity analysis indicated contact time and initial CR concentration as the most influential parameters for adsorption.

    Conclusions:

    • ANFIS provides a robust and accurate framework for modeling and optimizing Congo Red dye removal using SCAC.
    • Machine learning approaches, particularly ANFIS, are valuable tools for predicting and enhancing wastewater treatment processes.
    • SCAC shows significant potential for the efficient removal of carcinogenic azo dyes from industrial effluents.