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Managing Staff Turnover and Knowledge Gaps: Which Training Solutions Should You Apply?

Managing Staff Turnover and Knowledge Gaps: Which Training Solutions Should You Apply? At a time when our industry is facing increasing technical staff turnover, an effective training solution for

Managing Staff Turnover and Knowledge Gaps: Which Training Solutions Should You Apply?

Sponsored Webinar by: IBC Paper Training

Surface Treatment – From History to Modern Customized Solutions and Their Environmental Footprint Webinar

Flexible Packaging Webinar

Beyond Corrugated Packaging Trends: Practices And Solutions To Prioritize In 2026

This article was originally published on the blog page of York Container, and is being excerpted here for Ahead of the Curve readers with an interest in Corrugated Packaging. Access the full article here.

Predictive advisory solutions for chemistry management, control, and optimization, TAPPI Journal March 2025

ABSTRACT: Process runnability and end-product quality in paper and board making are often connected to chemistry. Typically, monitoring of the chemistry status is based on a few laboratory measurements and a limited number of online specific chemistry-related measurements. Therefore, mill personnel do not have real-time transparency of the chemistry related phenomena, which can cause production instability, including deposition, higher chemical consumption, quality issues in the end-product and runnability problems. Machine learning techniques have been used to establish soft sensor models and to detect abnormalities. Furthermore, these soft sensors prove to be most useful when combined with expert-driven interpretation. This study is aimed at utilizing a hybrid solution comprising chemistry and physics models and machine learning models for stabilizing chemistry-related processes in paper and board production. The principal idea is to combine chemistry/physics models and machine learning models in a fashion close to white box modeling. A cornerstone in the approach is to formulate explanations of the findings from the models; that is, to explain in plain text what the findings mean and how operational changes can mitigate the identified risks. The approach has been demonstrated for several different applications, including deposit control in the wet end, both raw water treatment and usage, and wastewater treatment. This approach provides mill personnel with knowledge of identified phenomena and recommendations on how to stabilize chemistry-related processes. Instead of using close to black box machine learning models, a hybrid solution including chemistry/physics models can enhance the performance of artificial intelligence (AI) deployed systems. A successful way of gaining the trust from mill personnel is by creating a plain text explanation of the findings from the hybrid models. The correlation between the likelihood of a phenomena and disturbance and the explanations are derived and validated by application and chemistry and physics experts.

Flow rheology of light foams generated from aqueous solutions of polyvinyl alcohol, TAPPI Journal January 2023

WCP Solutions Promotes Amy Swanson to CEO; Tom Groves to Join WCP's Board

PulPac and LAKRIDS BY BÜLOW Collaborate to Develop Sustainable Packaging Solutions

Emtec Presents Eenergy-Efficient Solutions for Sustainable Paper Production at Paper & Biorefinery 2024

NPTA Recognizes Tom Groves of WCP Solutions with 2024 Stanley O. Styles Industry Excellence Award