Abteilung Verfahrenstechnik

Das Qualitätsziel einer 80%igen Elimination von Mikroverunreinigungen ist in der Schweiz für ausgewählte ARA auch bei Regenwetterverhältnissen stets einzuhalten. Hohe Niederschläge verursachen hohe Mischwasserzuläufe zur ARA, die in der Menge häufig das Doppelte der Trockenwettermenge übersteigt. Der Schmutzwasseranteil (Abwassermatrix) ist dann um das Vielfache verdünnt. Dieser Umstand verursacht hydraulische Effekte wie verkürzte hydraulische Aufenthaltszeiten in den Behandlungsstufen und führt zu verkürzten Kontakt- und Reaktionszeiten in den Reaktoren. U.a. können Kurzschlussströme entstehen, die die EMV erniedrigen können. Die EMVStufen in der Schweiz sind auf Mischwasserzulauf ausgelegt und es wurden über die letzten Jahre angepasste Betriebsstrategien für die verschiedenen
EMV-Verfahren erarbeitet, so dass in aller Regel das Qualitätsziel erreicht wird. Dennoch bleibt der sichere, wirtschaftliche sowie nachhaltige Betrieb der Anlagen anspruchsvoll mit hohen Herausforderungen.
Aktuelle Untersuchungen der Eawag zum Verhalten höher beladener GAK im Einzelfilter zeigten bei Kontakt mit stark verdünntem Abwasser trotz 20-minütiger Kontaktzeit verringerte Sorptionsleistung bis hin zur Desorption von Einzelsubstanzen. Ob dieser Umstand auch bei einem Parallelbetrieb zu beobachten ist, sollen weiterführende Untersuchungen im Praxisbetrieb von GAK-Filtrationen zeigen. Im Fall der GAK-Filtration der ARA Glarnerland mit langanhaltenden Regennachläufen und Schmelzwasserzulauf wurde eine PAK-Dosierung in die Biologie vorgeschaltet bzw. wird aktuell realisiert, um allenfalls Einbussen durch verdünnte Abwassermatrix abzufangen.

Industrial ecology (IE) methodologies, such as input/output or material flow analysis and life cycle assessment (LCA), are often used for the environmental evaluation of circular economy strategies. Up to now, an approach that utilizes these methods in a systematic, integrated framework for a holistic assessment of a geographic region's sustainable circular economy potential has been lacking. The approach developed in this study (IE4CE approach) combines IE methodologies to determine the environmental impact mitigation potential of circular economy strategies for a defined geographic region. The approach foresees five steps. First, input/output analysis helps identify sectors with high environmental impacts. Second, a refined analysis is conducted using material flow and LCA. In step 3, circular strategies are used for scenario design and evaluated in step 4. In step 5, the assessment results are compiled and compared across sectors. The approach was applied to a case study of Switzerland, analyzing 8 sectors and more than 30 scenarios in depth. Carbon capture and storage (CCS) from waste incineration, biogas and cement production, food waste prevention in households, hospitality and production, and the increased recycling of plastics had the highest mitigation potential. Most of the scenarios do not influence each other. One exception is the CCS scenarios: waste avoidance scenarios decrease the reduction potential of CCS. A combination of scenarios from different sectors, including their impact on the CCS scenario potential, led to an environmental impact mitigation potential of 11.9 Mt CO₂-eq for 2050, which equals 14% of Switzerland's current consumption-based impacts.

Artificial intelligence (AI) applications and digital technologies (DTs) are increasingly present in the daily lives of citizens, in cities and in industries. These developments generate large amounts of data and enhance analytical capabilities that could benefit the industrial ecology (IE) community and sustainability research in general. With this communication, we would like to address some of the opportunities, challenges, and next steps that could be undertaken by the industrial ecology community in this realm. This article is an adapted summary of the discussion held by experts in industrial ecology, AI, and sustainability during the 2021 Industrial Ecology Day conference session titled "The Future of Artificial Intelligence in the Context of Industrial Ecology." In brief, building on previous studies and communications, we advise the industrial ecology community to: (1) create internal committees and working groups to monitor and coordinate AI applications within and outside the community; (2) promote and ensure transdisciplinary efforts; (3) determine optimal infrastructure and governance of AI for IE to minimize undesired effects; and (4) act on effective representation and on reduction of digital divides.

In recent years many Life Cycle Assessment (LCA) studies have been conducted to quantify the environmental performance of products and services. Some of these studies propagated numerical uncertainties in underlying data to LCA results, and several applied Global Sensitivity Analysis (GSA) to some parts of the LCA model to determine its main uncertainty drivers. However, only a few studies have tackled the GSA of complete LCA models due to the high computational cost of such analysis and the lack of appropriate methods for very high-dimensional models. This study proposes a new GSA protocol suitable for large LCA problems that, unlike existing approaches, does not make assumptions on model linearity and complexity and includes extensive validation of GSA results. We illustrate the benefits of our protocol by comparing it with an existing method in terms of filtering of noninfluential and ranking of influential uncertainty drivers and include an application example of Swiss household food consumption. We note that our protocol obtains more accurate GSA results, which leads to better understanding of LCA models, and less data collection efforts to achieve more robust estimation of environmental impacts. Implementations supporting this work are available as free and open source Python packages.

Mathematical modelling is an indispensable tool to support water resource recovery facility (WRRF) operators and engineers with the ambition of creating a truly circular economy and assuring a sustainable future. Despite the successful application of mechanistic models in the water sector, they show some important limitations and do not fully profit from the increasing digitalisation of systems and processes. Recent advances in data-driven methods have provided options for harnessing the power of Industry 4.0, but they are often limited by the lack of interpretability and extrapolation capabilities. Hybrid modelling (HM) combines these two modelling paradigms and aims to leverage both the rapidly increasing volumes of data collected, as well as the continued pursuit of greater process understanding. Despite the potential of HM in a sector that is undergoing a significant digital and cultural transformation, the application of hybrid models remains vague. This article presents an overview of HM methodologies applied to WRRFs and aims to stimulate the wider adoption and development of HM. We also highlight challenges and research needs for HM design and architecture, good modelling practice, data assurance, and software compatibility. HM is a paradigm for WRRF modelling to transition towards a more resource-efficient, resilient, and sustainable future.