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 L 0 , 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 L 0 , 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 L 0 value pairs, (k (d −1 )-L 0 (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-L 0 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 L 0 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-L 0 value pair, (0.23 d −1 ; 250 mg l −1 ). The results indicated that: (1) different combinations of k-L 0 values had no significant effect on the performance of CBOD curve parameter estimation methods with the “true” data; (2) use of CBOD 20 data, i.e., CBOD data collected for 20 days, provided better estimates for k and L 0 ; (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. \textcopyright 2001 Taylor \& Francis Group, LLC.
|Journal||Environmental Technology (United Kingdom)|
|State||Published - 2001|