Addressing Europe's current challenges of aging sewer networks, the presented research focuses on the uncertainties in service life and aging behavior of the most used renovation technique, Cured in Place Pipe (CIPP) lining. Examining its aging behavior, common defects and deficiencies were analyzed through literature review and expert interviews. The findings influenced the proposition of a calibration setting for a deterioration model using survival curves. Identified defects stress the need for precise installation and curing processes. The study recommends a thorough review of the initially specified 50-year service life, acknowledging uncertainties during the installation process.

Extending the AI-driven Software SEMAplus with a risk prioritization module.

Selection and analysis of risk criteria as preliminary part of decision making.

Analysis of multicriteria decision approaches and choice of ELECTRE TRI.

During the last decades, municipalities have increasingly invested in new approaches for rehabilitating sewerage networks. With the increasing number of rehabilitation techniques, objectives and constraints, the number of rehabilitation scenarios rises exponentially. This article proposes an asset management approach to create long-term rehabilitation plans where different budget allocations for rehabilitation techniques are considered every year depending on performance and cost indicators. It builds long-term strategies through multiobjective black-box optimization where the impact of the budget allocations over the network life cycle is part of the decision process. It employs a pipe deterioration model based on Markov chains whose transition matrices are estimated by survival curves for different pipe cohorts. The proposed approach seeks to determine the appropriate investment (CAPEX) and operational expenses (OPEX) levels in the coming decades. It was tested with real-world data from a sewerage network in Sofia, Bulgaria, and the results show that it provides efficient long-term rehabilitation plans.

Smart water management is acknowledged as a key component of the solutions to address climate change impact and secure water resources availabilities in the context of Sustainable Development Goals. Over the last decades, digital solutions have become an essential part of water management. Numerous initiatives have been developed to explore hybrid and new AI modeling with concrete approaches such as digital twins. The ambition is to provide water managers with tailored IT solutions that can be implemented in their current management system. These developments raise a wide range of questions in terms of sensors’ approach, interoperable open data models, reference architecture, and cybersecurity that are presented in this chapter. Additionally, IT innovation, as groundbreaking as it may be, requires additional dimensions such as governance, capacity building, and economics to ensure its adoption by water managers. These aspects are also presented in the latest sections of this chapter.

D7.4 describes the innovation and Intellectual Property Rights (IPR) management procedures within DWC. It introduces the concepts of Intellectual Property (IP), the types of protection rights as well as the IPR rules in the project. It summarizes the key procedures introduced in the Grant Agreement and Consortium Agreement documents. Finally, it explains the role of the innovation and IPR manager and the detailed activities that will be carried out to foster innovation and secure the protection of our key results. Compared to the previous versions, the IPR repository has been updated.

Urban wastewater management and the associated modelling has become indispensable today. Reliable calibration is essential for these models, and water level data is used as a standard. However, data collection can be limited due to high sensor costs and harsh conditions in the sewer. A novel solution is collecting data using low-cost temperature sensors, placing one in the stream, the other at the crest of the weir. In the case of dry weather, the sensor measures the air phase, whereas, in the case of Combined Sewer Overflow (CSO), the discharged storm and wastewater is measured. Autocalibration was performed using OSTRICH for a SWMM model in Berlin, with water level and fictional temperature data, and various number of measuring sites. Results showed that calibration using temperature data was as good as using water level data, with promising outcomes achieved by using one measuring site, offering a cost-effective alternative for water utilities.

The management of urban wastewater systems and the associated modelling of these systems has become indispensable in today's world. In order for these models to represent reality as accurately as possible, a reliable calibration is essential. Water level data is used as a standard, but due to expensive sensors and harsh conditions in the sewer, data can only be collected at a few key points of the system. One novel solution, that has experienced an upswing in recent years, is collecting data using low-cost temperature sensors. Two sensors are needed; one is placed in the stream; the other is placed at the crest of the weir. In the case of dry weather, the sensor measures the air phase, whereas, in the case of Combined Sewer Overflow (CSO), the discharged storm and wastewater is measured. The start and end of a CSO event can be determined via the merging of measured temperature values in both points of the overflow structure. Due to this method, the duration of CSO events in a sewer system can be detected.

In this work, the potential benefits of this novel method for model calibration are assessed. Therefore, autocalibration runs with water level data and fictional temperature data were carried out via OSTRICH for a SWMM model located in Berlin. Furthermore, calibration runs with a different number of measuring sites were performed, to evaluate the amount of necessary measuring sites for a reliable calibration. In order to be able to compare the different approaches, a calibration period of 19 events was first required for the respective datatype. Next, a validation period which consisted of 18 events was carried out and evaluated by the R² of three water level measuring sites for both approaches to ensure comparability. It was revealed that the calibration with duration data based on temperature sensors was able to achieve results as good as the conventional approach using water level data. Due to low spatial distribution of the measuring sites in the model, it could not be finally answered if more measuring sites would yield to even better results. However, already with one measuring site, promising calibration outcomes could be achieved and thus, offers an alternative for water utilities and practitioners.