Bayesian model discrimination for BOD analyses

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Abstract
The Hsiang–Reilly method, a discrete Bayesian model discrimination technique, is used to select the best model of several alternative models to describe data relating observed BOD exertion as a function of time. Use of this approach avoids the need for linearization of equations and results in distributed probability estimates of the parameter magnitudes rather than simply point estimates. A case study application to the raw influent and primary effluent of the Waterloo Pollution Control Plant is included. The results indicate that although a three-parameter model often gives a better fit to observed BOD data than the two-parameter first-order model, differences between the BOD-progression curves for the models are often so small as to be insignificant. In view of these small differences, and considering that none of the proposed models could adequately describe observed deviations from the exponential BOD curves, the use of the first-order equation to describe BOD-progression relationships for the Waterloo Pollution Control Plant appears to be justified.

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