• Emmanuel Kweinor Tetteh Faculty of Engineering and the Built Environment, Durban University of Technology
  • Sudesh Rathilal Faculty of Engineering and the Built Environment, Durban University of Technology
Keywords: Chemical oxygen demand, COD, design of experiment, DOE, response surface methodology, RSM


Industrial waste oil in water from oil refineries and petrochemical processing poses a major environmental concern. Environmental pollution from these wastewaters is increasing and will continue to rise due to a growing demand for petrochemical products and energy. The composition of these industrial wastes varies from location to location as well as with manufacturing processes. In terms of water quality issues, chemical oxygen demand is considered one of the most problematic in oil refinery wastewater treatment. This study applies the response surface methodology to obtain a response model for industrial wastewater treatment. Operating parameters are optimized to enhance the treatment performance. The study, focusing on the effects of input variables for chemical oxygen demand removal, was experimentally carried out using dissolved air floatation jar tests. The experimental matrix incorporated the Box-Behnken design in the response surface methodology. In addition, the procedure evaluated the effect of the input variables and their interactions to obtain the optimum condition for the extent of efficiency. The results show that the chemical oxygen demand removal was sensitive to the effect of the input variables and their interactions. The statistical analysis established that the quadratic model was highly significant with a low probability (<  0.0001), indicating that the correlated regression scattering was unlikely random. The predicted model results corresponded well to the experimental results, with a coefficient of determination close to 1.0. The response surface of the model is presented in three-dimensional plots. These study results show that the addition of a coagulant to remove chemical oxygen demand is effective under acidic conditions when response surface methodology is applied.


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