Objective of this synthesis report is to summarise the main achievements of the OPTIWELLS-2 project. Based on a preparatory phase OPTIWELLS-1 (2011-2012), the main project phase OPTIWELLS-2 (2012-2015) included the development of two different optimisation modelling methodologies (data-driven, process-driven) for minimising a well field’s specific energy demand whilst satisfying both, water demand and water quality constraints. Chapter 2 gives a short overview on the technical background on pipe hydraulics and the general methodology used within the project. The general workflow of the testing and application for the three case study well fields investigated within OPTIWELLS-2 is summarised in Chapter 3. For the first two case studies (Chapter Fehler! Verweisquelle konnte nicht gefunden werden. and Fehler! Verweisquelle konnte nicht gefunden werden.), a process-driven modelling approach was used, which enabled the assessment of three different management strategies (smart well field management, pump renewal or a combination of both) on the specific energy demand. This approach was more time and data-demanding (Chapter 2.5) compared to the data-driven approach used for the third case study (Chapter Fehler! Verweisquelle konnte nicht gefunden werden.). The cross-case analysis (Chapter 4) showed, that the energetic prediction accuracy of process-driven modelling (Chapter 4.1.3) was improved significantly by using pump characteristics derived from audits instead of relying on manufacturer data, whilst including steady-state well drawdown compared to assuming a static water level in the production well was much less important. This can be explained by the fact, that well drawdown contributed to less than 3% of the required pump head (Chapter 4.1.1), whilst the offset between audit and manufacturer pump characteristics is much more relevant because of pump ageing during long usage periods (up to 40 years). The data-based modelling approach used for Site C has yielded energy consumption forecasts with a similar accuracy, but is more robust as it relies on operational data, thus requiring no calibration.

In this study, a method is proposed to activate the maximal in-sewer storage volume of a combined sewer system (CSS) with a limited number of flow regulators to reduce negative impacts of combined sewer overflows (CSO). Based on a detailed analysis of the CSS structure, it indicates suitable locations to install flow regulators. The method has been developed in the programming language R and tested on the Berlin’s biggest CSS. Flow regulators have been implemented in the CSS Infoworks model at the five most suitable locations found and tested for different rainfall conditions. It was found that significant additional in-sewer storage capacity can be activated (~50% of the already existing capacity) leading to CSO volume and pollutant load reductions up to 62% for a three-monthly rain event of 60 minutes duration.

Changes in rainfall patterns or land use require flexible adaptation strategies for urban drainage systems. However, finding effective measures to reduce combined sewer overflows (CSO) and flooding is not straight-forward. The presented study proposes a holistic assessment approach that combines CSO quantity and quality criteria with indicators for the spatial extent and severity of flood events. The approach is tested for three selected adaptation measures with a detailed calibrated model of Berlin’s largest combined sewer catchment in the software Infoworks CS. The results indicate that a detailed assessment based on multiple performance criteria is necessary to fully understand measure effects. The presented work is embedded in an integrated modelling study involving different elements of the drainage and the wastewater treatment system.

In Germany 35% of the total energy consumption in water utilities is due to well pumping (Plath et al., 2010). Therefore, a more efficient abstraction, besides the reduction of the carbon footprint, will lead to economic benefits for the operator. Different strategies exist for energy saving both in the operation of well fields as well as with the use of adapted, energy-efficient technical equipment (pumps, pipes, etc.) (Madsen et al., 2009). The objective of this study is the development and testing of a well field optimization tool, which is based on a hydraulic pipe network model (EPANET) but also takes steady-state well drawdown into account. The optimizer, based on coupling EPANET with the programing language R, simulates automatically the different optimization strategies (e.g. smart well field management, pump renewal) and evaluates their impact on the energy demand. The developed well field model was tested for a case study in France and predicted the measured energy demand with an error of less than 2%. The identified energy saving potential found by the optimizer reaches up to 17% in case of implementing only smart well field management and close to 50% combining the latter option with pump renewal.