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

Publikationen: KonferenzbeitragPaper(peer-reviewed)

Standard

Real-time analytics to determine the quality of input in waste pre-treatment plants. / Weißenbach, Thomas; Pomberger, Roland; Sarc, Renato.
2019. 130 - 138 Beitrag in Waste to resources 2019, Hannover, Deutschland.

Publikationen: KonferenzbeitragPaper(peer-reviewed)

Harvard

Weißenbach, T, Pomberger, R & Sarc, R 2019, 'Real-time analytics to determine the quality of input in waste pre-treatment plants', Beitrag in Waste to resources 2019, Hannover, Deutschland, 14/05/19 - 16/05/19 S. 130 - 138.

APA

Vancouver

Bibtex - Download

@conference{d80856fd392e43609d589432c53a97a1,
title = "Real-time analytics to determine the quality of input in waste pre-treatment plants",
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.",
keywords = "Waste characterisation, real-time analysis, sensor-based analysis, image classification, commercial waste",
author = "Thomas Wei{\ss}enbach and Roland Pomberger and Renato Sarc",
year = "2019",
month = may,
day = "15",
language = "English",
pages = "130 -- 138",
note = "Waste to resources 2019 ; Conference date: 14-05-2019 Through 16-05-2019",

}

RIS (suitable for import to EndNote) - Download

TY - CONF

T1 - Real-time analytics to determine the quality of input in waste pre-treatment plants

AU - Weißenbach, Thomas

AU - Pomberger, Roland

AU - Sarc, Renato

PY - 2019/5/15

Y1 - 2019/5/15

N2 - 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.

AB - 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.

KW - Waste characterisation

KW - real-time analysis

KW - sensor-based analysis

KW - image classification

KW - commercial waste

M3 - Paper

SP - 130

EP - 138

T2 - Waste to resources 2019

Y2 - 14 May 2019 through 16 May 2019

ER -