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Prediction and Optimization of Pentachlorophenol Degradation and Mineralization in Heterogeneous Catalytic Ozonation Using Artificial Neural Network

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Abstract

In this study, artificial neural network was used to predict pentachlorophenol (PCP) degradation in aqueous solution by catalytic ozonation process in a laboratory-scale semi-batch reactor. The catalyst used in this process was the alumina (γ-Al2O3). Results indicated that after 60 min optimal condition: 0.5 g/L of (γ-Al2O3), 0.5 L/min the flow rate of ozone, pH 8 and 100 mg/L PCP initial concentration, 96% of target pollutant was degraded in catalytic ozonation process. In artificial neural network evaluation, a comparison between the model data and laboratory results revealed a high degree of correlation that indicated the model was capable of defining the PCP elimination efficiency with high accuracy. Artificial neural network predicted results are very close to the experimental results with correlation coefficient (R2) of 0.989 and mean square error of 0.000421. The sensitivity analysis indicated that all studied variables (pH, dosage of catalyst and initial concentration of PCP) have strong influence on PCP degradation.

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Funding

We would like to acknowledge the Hamadan University of Medical Sciences, Iran, for technical and financial supports (thesis) to conduct this work.

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Authors and Affiliations

  1. Department of Environmental Health Engineering, Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran

    Ghorban Asgari, Alireza Rahmani & Fateme Samiee

  2. Department of Computer Sciences, Bu-Ali sina University, Hamadan, Iran

    Muharram Mansoorizadeh

  3. Department of Environmental Health Engineering, Neyshabur University of Medical Sciences, Neyshabur, Iran

    Aliakbar Mohammadi

Authors
  1. Ghorban Asgari
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  2. Alireza Rahmani
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  3. Muharram Mansoorizadeh
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  4. Aliakbar Mohammadi
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  5. Fateme Samiee
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Correspondence to Fateme Samiee.

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Ghorban Asgari, Rahmani, A., Mansoorizadeh, M. et al. Prediction and Optimization of Pentachlorophenol Degradation and Mineralization in Heterogeneous Catalytic Ozonation Using Artificial Neural Network. J. Water Chem. Technol. 42, 164–170 (2020). https://doi.org/10.3103/S1063455X20030042

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  • DOI: https://doi.org/10.3103/S1063455X20030042

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