Staff

Andreas Frömelt

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Dr. Andreas Frömelt

Group Leader

Department Process Engineering

About Me

I am a tenure-track group leader at the Process Engineering (Eng) department of Eawag (Swiss Federal Institute of Aquatic Science and Technology). My group "Data & Information" specializes in mathematical modelling and advanced data analysis to support process control in water and wastewater treatment systems by transforming measurements into intelligent actions.

I received a Master's degree in Environmental Engineering from ETH Zurich in 2009. After gaining work experience as a process engineer in the field of wastewater treatment, I returned to ETH as a scientific assistant. In my doctoral thesis (defended in 2018 and honored with the ETH Medal), I combined data mining techniques and physically-based models to develop new approaches for modeling environmental impacts induced by individual household behavior on a large scale with high resolution. In 2019, I received a SNSF fellowship that allowed me to work as a visiting researcher in the Sustainability Assessment Program group at UNSW Australia for one year. Before and after this year, I continued working at ETH Zurich as a postdoctoral researcher and later on as a senior researcher (Oberassistent) in the Ecological Systems Design group until 2021.

In my position at Eawag, I combine my work experience as a wastewater process engineer with my data science and modeling skills to support plant operators to find optimal control strategies for water and wastewater treatment.

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Projects

The Swiss Federal Council set the ambitious goal to reach net-zero greenhouse gas emissions by 2050.

SCENE - Reducing N2O emissions from wastewater treatment
Treatment with powdered activated carbon (PAC) and ozonation are established methods for advanced wastewater treatment plants...

Granular activated carbon (GAC)
Systematic investigation of hybrid model approaches for the prediction of nitrous oxide emissions in biological wastewater treatment

MAD4WW
Open-Source Software for (Hybrid) Modelling of Water Systems

PeePyPoo / BioChemicalTreatment

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Curriculum Vitae

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Publications

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Wiprächtiger, M., Haupt, M., Froemelt, A., Klotz, M., Beretta, C., Osterwalder, D., … Hellweg, S. (2023). Combining industrial ecology tools to assess potential greenhouse gas reductions of a circular economy. Method development and application to Switzerland. Journal of Industrial Ecology, 27(1), 254-271. doi:10.1111/jiec.13364, Institutional Repository

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.
Donati, F., Dente, S. M. R., Li, C., Vilaysouk, X., Froemelt, A., Nishant, R., … Hashimoto, S. (2022). The future of artificial intelligence in the context of industrial ecology. Journal of Industrial Ecology, 26(4), 1175-1181. doi:10.1111/jiec.13313, Institutional Repository

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.
Kim, A., Mutel, C. L., Froemelt, A., & Hellweg, S. (2022). Global sensitivity analysis of background life cycle inventories. Environmental Science and Technology, 56(9), 5874-5885. doi:10.1021/acs.est.1c07438, Institutional Repository

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.
Schneider, M. Y., Quaghebeur, W., Borzooei, S., Froemelt, A., Li, F., Saagi, R., … Torfs, E. (2022). Hybrid modelling of water resource recovery facilities: status and opportunities. Water Science and Technology, 85(9), 2503-2524. doi:10.2166/wst.2022.115, Institutional Repository

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.
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Address

E-Mail: andreas.froemelt@eawag.ch
Phone: +41 58 765 6861
Fax: +41 58 765 5802
Address: Eawag
Überlandstrasse 133
8600 Dübendorf
Office: BU B13
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Expert on

wastewater, wastewater treatment, data science, machine learning, modeling

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Research Group

Data & Information

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