Real-time analytics to determine the quality of input in waste pre-treatment plants

Publikationen: KonferenzbeitragPaperForschung(peer-reviewed)

Abstract

In the framework of a larger project (ReWaste4.0), research work is carried out to char-acterise input into waste pre-treatment plants by means of real-time analysis. Commer-cial waste was selected as input material for the experiments. A number of samples were pre-processed (shredding, sieving) and sorted into a number of fractions. The main experiments will be carried out with individual waste objects, which are taken from nine sorting fractions. Regarding the real-time analysis, two approaches will be used, i.e. sensor-bases analysis (NIR-sensor/RGB-camera) and a deep learning ap-proach (image classification system). The produced data are related to data, which are generated by manual measuring (object size, weight) and laboratory analysis (heating value, water and chlorine content). By using regression analysis, the data of the real-time analysis are related to the data of standard laboratory analysis.

Details

Titel in ÜbersetzungEchtzeitanalytik zur Bestimmung der Qualität des Inputs in Abfallvorbehandlungsanlagen
OriginalspracheEnglisch
Seiten130 - 138
Seitenumfang9
StatusVeröffentlicht - 15 Mai 2019
VeranstaltungWaste to resources 2019 - Hotel Wienecke 11, Hannover, Deutschland
Dauer: 14 Mai 201916 Mai 2019

Konferenz

KonferenzWaste to resources 2019
LandDeutschland
OrtHannover
Zeitraum14/05/1916/05/19