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

TL;DR

This paper proposes a digital assistance system utilizing machine learning to support paper machine operators, aiming to optimize production, improve decision-making, and reduce environmental impact through better data utilization.

Contribution

The paper introduces a novel operator assistance system that applies machine learning techniques to leverage existing machine data for improved process control and sustainability.

Findings

Enhanced decision support for operators

Potential reduction in energy consumption

Improved process efficiency

Abstract

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.