Removal of color, COD, and turbidity from the effluents of turkey processing plant by ozonation: Box-Behnken approach and Monte Carlo simulation

The purpose of the present study includes of the advanced treatment of discharge waters of a local turkey processing plant located in Bolu, under the guidance of the experiments based on Box Behnken Design (BBD), empirical modeling, and quantification of the uncertainties in the derived models. Ozone dose (OD, mg/L), reaction time (RT, min) and initial pH (pH) were selected as experimental operating parameters. Color, COD, and turbidity removals were obtained as 96.77%, 96.07%, and 95.37%, respectively under the conditions of 20 mg/L of OD, 7.5 of initial pH, and 60 min of RT. Those results showed that ozonation was an efficient process for treatment of related effluents. Multiple (non-) linear regression models (MNLR) were derived for both identification of variation in response variables (R-adj(2)) and prediction of response variables (R-pred(2)) as a results of BBD-based empirical modeling studies. In this context, it was seen that values of R-adj(2) of derived MNLR models had ranged between 99.81% and 99.99%, whereas values of R-pred(2) had varied between 99.36% and 99.83%. This situation showed that derived MNLR models had a fairly high predictive power. Finally, the uncertainties in derived MNLR models were tried to be identified and quantified by Monte Carlo simulation (MC). Results of MC demonstrated that there were uncertainties of 0.1%, 0.09%, and 24% in color, COD, and turbidity removals, respectively.