Search

• A conceptualization procedure is proposed with easy calibration. • Software platform with flexible sewer model selection and linkable to WWTP models. • Sewer biochemical modelling results can be visualized into dynamic distribution maps. • Suggestion is given on selection of conceptual- and whole-network biological models. This paper proposes a new conceptual modelling approach and calibration method for sewer systems, driven by the interest in using the sewer as a bioreactor and long-term biochemical modelling of the integrated system consisting of the sewage collection system and the wastewater treatment plant (WWTP). The new approach consists of a method for simplifying a complex sewer network into a simpler, yet accurate, computationally efficient (conceptual) model able to capture the relevant sewer dynamics, both in terms of water quantity and water quality entering the wastewater treatment plant. Water quality predicted by the conceptual model was assessed using a detailed biochemical process model and compared against a whole-network biochemical model that covers all hydraulic segments and pipes of the sewer system. The conceptual biochemical model achieves very similar results as the whole-network model in terms of water quality, while providing much faster simulation speed (minutes rather than hours) and easy calibration. Given these advantages, the conceptual biochemical model of a collection system can be used in an integrated model with which sewer and WWTP can be simulated simultaneously for long-term evaluation of optimization scenarios. The model was applied to the prediction of the biochemical components, e.g. sulfide, organics and nutrients, which are essential for the control and management of sewer corrosion and pollutant removal. [ABSTRACT FROM AUTHOR]

In situ continuous monitoring at high frequency is used to collect water quality information about water bodies. However, it is crucial that the collected data be evaluated and validated for the appropriate interpretation of the data so as to ensure that the monitoring programme is effective. Software tools for data quality assessment with a practical orientation are proposed. As water quality data often contain redundant information, multivariate methods can be used to detect correlations, pertinent information among variables and to identify multiple sensor faults. While principal component analysis can be used to reduce the dimensionality of the original variable data set, monitoring of some statistical metrics and their violation of confidence limits can be used to detect faulty or abnormal data and can help the user apply corrective action(s). The developed algorithms are illustrated with automated monitoring systems installed in an urban river and at the inlet of a wastewater treatment plant [ABSTRACT FROM AUTHOR]