Auflistung nach:
Auflistung P328 - EnviroInfo 2022 nach Schlagwort "AI"
1 - 2 von 2
Treffer pro Seite
Sortieroptionen
- KonferenzbeitragCloud-based Processing of data from Non-Target-Analysis for Tracking Micropollutants in Surface Water(EnviroInfo 2022, 2022) Pauw, Viktoria; Hayek Mohamad; Shojaei, Elham; Hachinger, Stephan; Müller, Uwe; Bader, TobiasTens of thousands of chemicals used by consumers, agriculture and industry enter the aquatic environment as micropollutants every day. Using targeted analysis we are so far only able to detect a small subset of the chemicals that are present. Therefore so called non-target screening (NTS) using liquid chromatography in combination with high-resolution mass spectrometry (LCHRMS) is increasingly used by labs to perform more comprehensive monitoring. However, a high degree of variance in measurements and processing workflows results in low comparability of data from separate laboratories. On one hand this is caused by differences in processing techniques which are due to stationary laboratory equipment and on the other hand by differing priorities in the detection strategy and evaluation workflow. The K2I project funded by BMBF aims at fostering collaboration between laboratories by providing a joint platform for uploading and processing LCHRMS data. A cloud based datalake and processing pipeline is being developed. A standardized processing workflow can then be executed which is enhanced by data mining tools including machine learning techniques. An indexing and searching software is employed to create a web based access to the processed data for participants.
- KonferenzbeitragOptimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concept(EnviroInfo 2022, 2022) Schroth, Moritz; Hake, Felix; Merker, Konstantin; Becher, Alexander; Klaeger, Tilman; Huesmann, Robin; Eichhorn, Detlef; Oehm, LukasNowadays cross-industry ranging challenges include the reduction of greenhouse gas emission and enabling a circular economy. However, the production of paper from waste paper is still a highly resource intensive task, especially in terms of energy consumption. While paper machines produce a lot of data, we have identified a lack of utilization of it and implement a concept using an operator assistance system and state-of-the-art machine learning techniques, e.g., classification, forecasting and alarm flood handling algorithms, to support daily operator tasks. Our main objective is to provide situation-specific knowledge to machine operators utilizing available data. We expect this will result in better adjusted parameters and therefore a lower footprint of the paper machines. emission and enabling a circular economy. However, the production of paper from waste paper is still a highly resource intensive task, especially in terms of energy consumption. While paper machines produce a lot of data, we have identified a lack of utilization of it and implement a concept using an operator assistance system and state-of-the-art machine learning techniques, e.g., classification, forecasting and alarm flood handling algorithms, to support daily operator tasks. Our main objective is to provide situation-specific knowledge to machine operators utilizing available data. We expect this will result in better adjusted parameters and therefore a lower footprint of the