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Capacity Analysis of Urban Water Systems under Centralization: A Case Study in Denmark
F Sousa Braga1,2,3, C L Gargalo3, Ll Corominas4
1Skanderborg Forsyning, Døjsøvej 1, 8660 Skanderborg, Denmark.
None:
Water utilities across Europe face increasing challenges like aging infrastructure, which often lacks the capacity and flexibility to meet future demands. Centralization of wastewater treatment, redirecting flows from smaller, outdated plants to larger, more advanced facilities, offers opportunities to improve performance and optimize system-wide capacity. This study introduces a comprehensive, plant-wide simulation framework designed to operate under dynamic conditions, evaluating the technical feasibility and capacity impacts of centralization strategies, using a real case involving five wastewater treatment plants (WWTPs) (Døjsøvej, Skovby, Ry, Hørning, and Gl.Rye) in Skanderborg, Denmark. Mechanistic models were developed for existing and centralized configurations, integrating dynamic influent generators, data reconciliation, plant-wide process modeling, and key performance indicators to assess system behavior under realistic operational and environmental conditions. Monte Carlo simulations were employed to quantify uncertainty propagation and evaluate long-term infrastructure resilience. The calibrated models were evaluated using mean absolute percentage error (MAPE) and reproduced influent and effluent pollutant dynamics satisfactorily (average deviations <5% and <15%, respectively); they closely matched operational metrics, including aeration/pumping energy demand, chemical usage, and biosolids production (average deviation <5%). While centralization was found to be technically feasible under current operational conditions in the short term, it reduced the operational buffer of receiving facilities. Long-term scenario-based predictions revealed performance degradation and increased risk of noncompliance, particularly for Døjsøvej, which may require upgrades within approximately five years under the assumed growth scenario. The framework enables scenario-based proactive, data-driven planning by revealing system constraints and upgrade needs, offering a scalable solution to enhance efficiency, compliance, and infrastructure longevity.
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