Multi-objective optimization of ciprofloxacin antibiotic removal from an aqueous phase with grey taguchi method

M. Salari, G. R. Rakhshandehroo*, M. R. Nikoo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Optimization methods are used to study and survey the optimal values for input factors and effect of optimized parameters on response variables. In this study, the effect of different factors on ciprofloxacin (CIP) removal of water soluble was studied. In this regard, a multi-objective optimization was performed utilizing the Taguchi method based on a grey relational analysis. Optimum levels of factors were determined to optimize three responses simultaneously with grey Taguchi. Meanwhile, grey relational analysis was applied to model and optimize three target responses, namely, CIP removal, chemical oxygen demand (COD) removal, and sludge to iron ratio. Multi-objective optimization results obtained based on grey relational analysis showed that the optimal value of the input factors were CIP concentration of 100 mg/L, H2O2 concentration of 100 mM, Fe(II) concentration of 10 mM, pH of 3, and a reaction time of 15 min. To confirm the results, the values obtained through a confirmation test were examined. Multi-objective optimization results from process factors were determined by analysis of variance (ANOVA) analysis and grey Taguchi method. Based on ANOVA analysis for the grey relational grade, Fe(II) concentration and H2O2 concentration were found to be the most influencing factors.

Original languageEnglish
Pages (from-to)530-541
Number of pages12
JournalJournal of Water and Health
Issue number4
Publication statusPublished - Aug 2018


  • Ciprofloxacin
  • Grey relational analysis
  • Homogeneous Fenton process
  • Multi-objective optimization
  • Taguchi method

ASJC Scopus subject areas

  • Water Science and Technology
  • Waste Management and Disposal
  • Public Health, Environmental and Occupational Health
  • Microbiology (medical)
  • Infectious Diseases


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