# Optimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concept

arXiv: https://arxiv.org/abs/2206.11581 · HTML: https://mlsanity.com/paper/2206.11581/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 1 of 1)

- [Using anomaly detection to support classification of fast running (packaging) processes](https://mlsanity.com/paper/1906.02473/) (2019)

## Abstract (arXiv)

Nowadays 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.