ViscAssist – Production assistance systems for predicting the viscosity curve of polyolefin recyclates

Intelligentes Assistenzsystem optimiert Viskositätsvorhersage von Polyolefinen mit Rezyklaten im Produktionsprozess

Intelligent assistance system optimises viscosity prediction of polyolefins with recyclates in the production process

The digitisation of compounding can significantly improve design and process monitoring, which are particularly complex when processing recyclates, and increase the added value of the entire process chain. Outdated plant technology and peripheral equipment must be retrofitted to make them capable of digitalization. Good analysis of the process data enables a precise description of the actual state of production and can be used to control or train soft sensors for in-line prediction of viscosity. This requires a holistic recording of the process chain in a digital infrastructure.

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The aim of the project “ViscAssist – Production assistance systems for predicting and controlling the viscosity curve of polyolefins when using recyclate” is to develop a soft sensor for inline prediction of the material viscosity curves of compounds containing recyclate by fully recording the production data in the compounding process. For this purpose, a three-stage in-line rheometer flanged to a melt pump will initially be used to build up a broad data set for training neural networks to determine the Carreau parameters of the compound based on the process parameters. This soft sensor forms the basis for an assistance system that supports plant operators in process control.

The assistance system will continuously detect viscosity fluctuations in the compound and make recommendations for adjusting the recipe and/or process parameters. Overall, the project aims to improve compounding through digital transformation. This includes the integration of modern sensor technology, the development of intelligent assistance systems and the use of advanced data analysis methods. As a result, not only is process quality optimised, but the flexibility and adaptability of production is also increased. This leads to more efficient and sustainable production that meets the current requirements of the industry.

Project data and funding

We would like to thank the BMWK for funding the project (funding code 01IF23179N) and the project partners for their cooperation.

Project duration: 04.2024 – 04.2026

Project partner and funding

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