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Artificial neural network (ANN) approach for prediction and modeling of breakthrough curve analysis of fixed-bed adsorption of iron ions from aqueous solution by activated carbon from Limonia acidissima shell

  • Shilpi Das ORCID logo EMAIL logo and Susmita Mishra
Published/Copyright: July 28, 2021
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International Journal of Chemical Reactor Engineering
From the journal International Journal of Chemical Reactor Engineering Volume 19 Issue 11

Abstract

The present research article explored the potential of activated carbon prepared from Limonia acidissima shell to adsorb total Fe ions from aqueous solution in a packed bed up-flow column. The effect of essential factors such as bed height (3–5 cm), initial concentration (30–50 mg/L), and flow rate (3.32–5.4 mL/min) on the performance of the column bed was investigated. The adsorption capacity augmented with an increase in bed height and initial adsorbate concentration but declined with an increase in flow rate. The maximum uptake capacity of 209.6 mg/g was achieved at 5 cm bed height, 3.32 mL/min, and 50 mg/L initial concentration. The bed depth service time (BDST) model was used to analyze the experimental data and determine the characteristic parameters of the packed bed reactor suitable for designing large-scale column studies. The Adams–Bohart, Thomas, and Yoon–Nelson models were applied to the experimental data to predict breakthrough curves using non-linear regression. The artificial neural network (ANN) based model was able to efficaciously predict the column performance using the Levenberg–Marquardt (LM) algorithm. A comparison between the experimental data and model results contributed to a high degree of correlation, specifying that the preliminary information was in good agreement with the ANN predicted data.

Keywords: activated carbon; ANN; fixed-bed adsorption; iron ion; Limonia acidissima shell
Corresponding author: Shilpi Das, Department of Chemical Engineering, National Institute of Technology, Rourkela 769008, Odisha, India, E-mail:

Acknowledgment

The authors are grateful to the National Institute of Technology, Rourkela, for providing us the infrastructure and instrument facility for carrying out the research work.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2021-03-09
Accepted: 2021-07-12
Published Online: 2021-07-28

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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  2. Review
  3. Book Review: Industrial green chemistry, Editors: Serge Kaliaguine and Jean-Luc Dubois
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https://doi.org/10.1515/ijcre-2021-0053
Keywords for this article
activated carbon; ANN; fixed-bed adsorption; iron ion; Limonia acidissima shell

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Book Review: Industrial green chemistry, Editors: Serge Kaliaguine and Jean-Luc Dubois
  4. Articles
  5. Experimental characterization, TDDFT-DFT, and spin effect on [PEG/H2O–ZrO2/TiO2]h hybrid nanofluid 3D flow as potential ceramic industry application
  6. A simulation study of nonideal mixing effect on the dynamic response of an exothermic CSTR with Cholette’s model
  7. CO2 utilization by dry reforming of CH4 over mesoporous Ni/KIT-6 catalyst
  8. CFD-based simulation to reduce greenhouse gas emissions from industrial plants
  9. Beneficiation of phosphate-siliceous slates via acetic acid
  10. Artificial neural network (ANN) approach for prediction and modeling of breakthrough curve analysis of fixed-bed adsorption of iron ions from aqueous solution by activated carbon from Limonia acidissima shell
  11. A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes
  12. Removal of a reactive dye from simulated textile wastewater by environmentally friendly oxidant calcium peroxide
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