• Title of article

    Selection of the best leachate treatment method for the waste of leek fields using Analytic Hierarchy Process (AHP)

  • Author/Authors

    Nabavi, Erfan Faculty of Civil Engineering - Khaje Nasir Toosi University of Technology - Tehran, Iran , Sabour, Mohammadreza Faculty of Civil Engineering - Khaje Nasir Toosi University of Technology - Tehran, Iran , Dezvareh, Ghorban Ali Faculty of Civil Engineering - Khaje Nasir Toosi University of Technology - Tehran, Iran

  • Pages
    18
  • From page
    153
  • To page
    170
  • Abstract
    A large amount of fruit and vegetable waste is generated every day in big cities. The efficient disposal of such biodegradable waste can be considered a challenge. Leachate contains large amounts of pollutants, and treating it is very complex, expensive, and requires a variety of hybrid processes. This study used the Analytic Hierarchy Process (AHP) to analyze suitable treatment methods for the leachate from fruit and leek fields. Quantitative and qualitative parameters or a combination of these parameters were used as defined in Expert Choice software. The criteria used for this purpose included chemical oxygen demand (COD), biochemical oxygen demand (BOD), COD/BOD, temperature, TOC, pH, total dissolved solids (TDS), total suspended solids (TSS), and time. These criteria, which are important for leachate classification, were identified and extracted by experts; their importance was ranked by AHP software. The research process was divided into two parts to ascertain a faster method: the significance of the parameter time and the insignificance of the parameter time. Biological treatment methods outperformed the other methods where the parameter time was insignificant. In the cases where the parameter time was significant, chemical methods and, in particular, two methods with ozone compounds (Ozone + GAC, Ozone + H2O2) outperformed the other methods.
  • Keywords
    AHP , AOP , Leachate treatment , Organic leachate
  • Journal title
    advances in Environmental Technology
  • Serial Year
    2021
  • Record number

    2703252