Department Process Engineering

Data & Information

The mission of the "data & information"-group is to explore the role of the increasing digitalization in the wastewater treatment sector. Our research focuses especially on the information content of the rapidly growing amount of data.

Every day, every minute, a huge volume of data is gathered at every wastewater treatment plant. However, the pace of data collection exceeds the capabilities of conventional data processing approaches; leaving the information content of the data under-utilized. By using advanced mathematical modelling and advanced data analysis, we investigate how (and to what extent) the ever-increasing amount of data can be used to support and improve the operation of wastewater treatment systems. Thereby, we aim to pursue new pathways by employing and combining mechanistic modelling approaches with techniques from the data mining and machine learning toolbox in order to unleash the full information potential of data.

It is of great importance to us to have a direct link to practice. Therefore, our findings shall effectively support engineers, operators and authorities in solving pressing issues such as meeting ever-tightening effluent criteria while simultaneously reducing greenhouse gas emissions.

Publications

Böhler, M. A., Carl, F., Zhu, M., McArdell, C. S., Frömelt, A., & Joss, A. (2023). Spurenstoffelimination bei stark verdünnter Abwassermatrix – Erfahrungen, Kenntnisstand und Herausforderungen. In M. Wessling & T. Wintgens (Eds.), 15. Aachener Tagung Wassertechnologie. Verfahren der Abwasserbehandlung und Wasseraufbereitung (pp. 14-23). Aachen: RWTH Aachen University. , Institutional Repository
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
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
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
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
Frömelt, A. (2007). Marketing compost in Nepal. Field testing of Sandec’s compost marketing handbook. Dübendorf: Eawag. , Institutional Repository