title: Comparison of methods for estimating carbonaceous BOD parameters.
abstract: The performance of seven different methods (Differential, Fujimoto, Thomas, Graphical, Integral, Log-Difference, and Nonlinear Regression) for estimating first-stage, carbonaceous biochemical oxygen demand (CBOD), curve parameters, namely k and L0, were compared using synthetic data generated by Monte Carlo simulation technique. The comparison of the methods was made based on their efficiency in retrieving the original values of k and L0, which were selected to generate the synthetic data. In the first part of the study, five sets of "true" data (without error substitution) with different k and L0 value pairs, (k (d(-1))-L0 (mg l(-1)): 0.23-10,000; 0.23-250; 0.23-50; 0.10-250; and 0.50-250) were used to obtain information about the effect of different k-L0 combinations and of using 5-day and 20-day CBOD data on the performance of the methods. In the second part, the same methods were used to calculate k and L0 for ten sets of synthetic data with log-normally distributed random errors at the coefficient of variation (COV) levels of 0.1, 0.2, and 0.3 for a single k-L0 value pair, (0.23 d(-1); 250 mg l(-1)). The results indicated that: (1) different combinations of k-L0 values had no significant effect on the performance of CBOD curve parameter estimation methods with the "true" data; (2) use of CBOD20 data, i.e., CBOD data collected for 20 days, provided better estimates for k and L0; (3) the Integral and Nonlinear Regression techniques were found to be the most reliable methods for the estimation of CBOD curve parameters among the other methods considered in this study.
authors.affiliation: Department of Environmental Engineering, Anadolu University, Eskisehir, Turkey.
authors.forename: S
authors.initials: S
authors.lastname: Uludag-Demirer
authors.forename: G N
authors.initials: GN
authors.lastname: Demirer
authors.forename: A R
authors.initials: AR
authors.lastname: Bowers
mesh_terms: D002244:Carbon / Q000378:metabolism*; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting; D008962:Models, Theoretical; D010100:Oxygen / Q000032:analysis* / Q000378:metabolism; D014865:Waste Disposal, Fluid
chemical_list: D002244:Carbon; D010100:Oxygen