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Estimation of 2,4-dichlorophenol photocatalytic removal using different artificial intelligence approaches

  • Narjes Esmaeili , Fatemeh Esmaeili Khalil Saraei EMAIL logo , Azadeh Ebrahimian Pirbazari , Fatemeh-Sadat Tabatabai-Yazdi , Ziba Khodaee , Ali Amirinezhad , Amin Esmaeili and Ali Ebrahimian Pirbazari
Published/Copyright: April 13, 2022
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Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling Volume 18 Issue 2

Abstract

Photocatalytic degradation is one of the effective methods to remove various pollutants from domestic and industrial effluents. Several operational parameters can affect the efficiency of photocatalytic degradation. Performing experimental methods to obtain the percentage degradation (%degradation) of pollutants in different operating conditions is costly and time-consuming. For this reason, the use of computational models is very useful to present the %degradation in various operating conditions. In our previous work, Fe3O4/TiO2 nanocomposite containing different amounts of silver nanoparticles (Fe3O4/TiO2/Ag) were synthesized, characterized by various analytical techniques and applied to degradation of 2,4-dichlorophenol (2,4-DCP). In this work, a series of models, including stochastic gradient boosting (SGB), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), the improvement of ANFIS with genetic algorithm (GA-ANFIS), and particle swarm optimization (PSO-ANFIS) were developed to estimate the removal percentage of 2,4-DCP. The model inputs comprised of catalyst dosage, radiation time, initial concentration of 2,4-DCP, and various volumes of AgNO3. Evaluating the developed models showed that all models can predict the occurring phenomena with good compatibility, but the PSO-ANFIS and the SGB models gave a high accuracy with the coefficient of determination (R2) of 0.99. Moreover, the relative contributions, and the relevancy factors of input parameters were evaluated. The catalyst dosage and radiation time had the highest (32.6%), and the lowest (16%) relative contributions on the predicting of removal percentage of 2,4-DCP, respectively.

Keywords: 2,4-dichlorophenol; adaptive neuro-fuzzy inference system; artificial neural network; photocatalytic removal; stochastic gradient boosting
Corresponding author: Fatemeh Esmaeili Khalil Saraei, Data Mining Research Group, Fouman Faculty of Engineering, College of Engineering, University of Tehran, P.O. Box 43515-1155, Fouman, 43516-66456, Iran, E-mail:

Funding source: University of Tehran

Award Identifier / Grant number: Unassigned

Acknowledgments

The authors appreciate significant comments of Engineer Ali Peik Herfeh in ANN and ANFIS modeling.

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

  2. Research funding: The authors wish to acknowledge the financial support of University of Tehran for supporting this research.

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

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/cppm-2021-0065).

Received: 2021-10-13
Accepted: 2022-03-24
Published Online: 2022-04-13

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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  2. Research Articles
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  4. Dynamic behavior of CO2 adsorption from CH4 mixture in a packed bed of SAPO-34 by CFD-based modeling
  5. Competitive adsorption of heavy metals in a quaternary solution by sugarcane bagasse – LDPE hybrid biochar: equilibrium isotherm and kinetics modelling
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https://doi.org/10.1515/cppm-2021-0065
Keywords for this article
2,4-dichlorophenol; adaptive neuro-fuzzy inference system; artificial neural network; photocatalytic removal; stochastic gradient boosting

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Experimental and simulation assessment to mitigate the emission of sulfide toxic gases and removing main impurities from Zn + Pb + Cu recovery plants
  4. Dynamic behavior of CO2 adsorption from CH4 mixture in a packed bed of SAPO-34 by CFD-based modeling
  5. Competitive adsorption of heavy metals in a quaternary solution by sugarcane bagasse – LDPE hybrid biochar: equilibrium isotherm and kinetics modelling
  6. Estimation of 2,4-dichlorophenol photocatalytic removal using different artificial intelligence approaches
  7. Design of a new synthetic nanocatalyst resulting high fuel quality based on multiple supports: experimental investigation and modeling
  8. Numerical study on thermal-hydraulic characteristics of flattened microfin tubes
  9. Development of binary models for prediction and optimization of nutritional values of enriched kokoro: a case of response surface methodology (RSM) and artificial neural network (ANN)
  10. A mathematical model for the activated sludge process with a sludge disintegration unit
  11. Process design and economic assessment of large-scale production of molybdenum disulfide nanomaterials
  12. Review
  13. Modified optimal series cascade control for non-minimum phase system
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