Artificial neural networks for insights into adsorption capacity of industrial dyes using carbon-based materials

Sara Iftikhar, Nallain Zahra, Fazila Rubab, Raazia Abrar Sumra, Muhammed Burhan Khan, Ather Abbas*, Zeeshan Haider Jaffari

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Organic waste-derived carbon-based materials (CBMs) are commonly applied in sustainable wastewater treatment and waste management. CBMs can remove toxic, non-biodegradable and carcinogenic pollutants such as dyes which include indigo, triphenylmethyl, azo, anthraquinone and phthalocyanine derivatives. Nonetheless, their diverse composition, surface properties, presence of numerous surface functional groups and the altering adsorption experimental conditions to which they are applied against the elimination of organic dyes make it challenging to completely understand the removal mechanism. Herein, a dataset of 1514 data points was compiled from various published peer-reviewed journals along with additional adsorption experiments conducted in this study. Artificial neural networks (ANN) based machine learning (ML) model was compared with other ML and a deep learning model named Tab-Transformer and the findings proposed ANN showed superior prediction performance for adsorption capacity as a function of adsorbent synthesis conditions, adsorbent physical characteristics and adsorption experimental conditions. The hyperparameters of ANN model was optimized using Bayesian optimizer and the batch size, activation and units were proven to be more important than the number of hidden layers and learning rate. The ANN model exhibits a higher coefficient of determination (R2 = 0.98) and lower root mean square error (RMSE = 46.95 mg/g) values for test dataset. Feature importance using SHapley Additive exPlanations (SHAP) analysis suggested that the adsorption characteristics with 51.4% was the most important in the ANN prediction followed by the adsorption experimental condition (31.2%) and adsorbent synthesis condition (17.4%). Moreover, the impact of six most important features were individually analyzed. Finally, a detailed discussion on the environmental impact of the presented ANN model is also included.

Original languageEnglish (US)
Article number124891
JournalSeparation and Purification Technology
StatePublished - Dec 1 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.


  • Adsorption
  • Artificial neural network
  • Carbon-based materials
  • Industrial dyes
  • Machine learning

ASJC Scopus subject areas

  • Analytical Chemistry
  • Filtration and Separation


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