Impact of Air Flow Rate and Frequency of Influent Data on Full-Scale Dynamic Model Calibration
Abstract:The paper investigates the impact of the way oxygen transfer is modelled and the frequency of influent data on the dynamic calibration of a full-scale WWTP. Oxygen transfer was modelled in 2 ways: by means of a fast “virtual controller” tracking dissolved oxygen and by means of a linear correlation between KLa and air flow rate. Influent data was retrieved from correlations derived from either off-line data or on-line data. The correlations in the latter case were found to be better. With regard to model performance, it was found that the oxygen transfer model based on the linear relation between KLa and air flow rate was able to sufficiently capture DO dynamics. Using the off-line data based correlation for influent data resulted in a decent DO profile prediction, but the nitrification prediction was not accurate implying that a calibration effort is required. However, when using on-line influent data correlations, NH4 predictions were found accurate without any need for calibration. Using more influent data resulted in a small deterioration of NH4 predictions but resulted in better NO3 predictions.
Document Type: Research Article
Publication date: January 1, 2010
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