A black-box model for generation of site-specific WWTP influent quality data based on plant routine data

This paper presents a simple method for the generation of continuous influent quality datasets for wastewater treatment plants (WWTPs) that is based on incomplete available routine data, only, without referring to any further measurement. In the approach, Weibull-distributed random data are fitted to the available routine data, such that the resulting distribution of influent quality data shows the identical statistical characteristics. Beside the description of the method, this paper contains a comprehensive analysis of robustness and universality of the approach. It is shown that incomplete datasets with only 10% remaining influent quality data can be filled with this method with nearly the same statistical parameters as the original data. In addition, the use with datasets of different WWTP plants sizes results always in a good agreement between original and filled datasets.