EnSort - "Increasing energy efficiency in the waste and recycling material sorting process by increasing the degree of automation"

General Information

• Project duration: 10/2022 - 09/2025
• Contact Person: Prof. Dr.-Ing. Leon Urbas
• Funding: BMWE
• Project partners:

       Sutco Recycling Technik GmbH

       TOMRA Sorting GmbH

       Universität Bremen

       Ruhr-Universität Bochum

Project description

Motivation

The material recycling of recyclable waste is becoming increasingly important in the course of the ecologically necessary and politically desired efforts to save energy and fossil fuels and to conserve geogenic resources. In this context, the specific energy consumption during the separation and sorting of waste materials of different types, which are primarily intended for material recycling, is to be significantly reduced.

Goal

The goal of the EnSort (Energieeffiziente Sortieranlage) project is to optimize energy-intensive aggregates in the highly complex sorting process from recyclable waste materials to recyclables with the help of AI programming via material recognition, as well as to increase the adaptability of the plant to constantly changing input and output qualities. This goal can be achieved by digitizing the process, which was previously operated manually.

ecoKI Platform

The EnSort project is an application project of the ecoKI project (further information on the ecoKI project ). In order to accelerate the project start and to provide a low-threshold entry for the use of AI, the ecoKI concepts are applied to EnSort, e.g. the AI methods used in the EnSort project to increase energy efficiency are provided by the ecoKI platform as reusable standard building blocks. In the course of the EnSort project, ecoKI's research gaps will be identified and the need for developing further building blocks will be determined in order to further develop the ecoKI-platform.

Contribution of TU Dresden

• Execution of simulation experiments on digital twin
• Dynamic combination of rigorous and data-driven simulation models
• Definition of necessary interfaces and model descriptions
• Creation of rigorous simulation models for single aggregates