ABLESS: AI-Based Low-Energy Strategic Systems for Enhanced (C)TMP Processes
| Reference number | |
| Coordinator | Chalmers Tekniska Högskola AB - Chalmers Tekniska Högskola Inst f Elektroteknik |
| Funding from Vinnova | SEK 6 600 000 |
| Project duration | September 2025 - September 2028 |
| Status | Ongoing |
| Venture | Advanced digitalization - Industrial needs-driven innovation |
| Call | Advanced digitalization - Industrial innovation 2025 |
Purpose and goal
The aim is to control (C)TMP processes effectively. The goal is an energy efficiency improvement of 65 GWh/year (based on an annual production of approximately 550,000 tons) and a 25% reduction in variation in pulp properties to be achieved at a given heat balance and controlled process, through the use of advanced system-based AI models.
Expected effects and result
Significant energy efficiency can be achieved by using innovative control in the production of mechanical pulp (C)TMP. This project proposal includes the use and further development of advanced “soft sensors” based on physical modeling and advanced ANN - empirical models. Data from three reference mills will be used. The goal is to achieve at least 65 GWh savings annually while at the same time achieving a 25% reduction in the variation in pulp properties.
Planned approach and implementation
Three specific work packages will be led by Chalmers and the respective mills. The algorithms that will be installed on-line will be developed autonomously through the technology that Chalmers has developed for ANN implementation. Dissemination of results between the actors will take place through joint workshops (every year during the project´s 3-year period), articles and conferences. The assessment is that the work at each mill will be distributed corresponding to 1/3 of the project time.