Improved delignification kinetics models through AI-handling of data from large scale processes

Purpose and goal

The purpose of the project is to develop improved experimental methods and kinetic models to study and design kraft pulping process by using large scale process data for AI-driven models. Specific aims are to: - Improve experimental design through AI-based identification of critical experimental relations - Develop machine learning methods for: black-box end-to-end generation of comparable kinetic models based on experimental data & gray-box extended kinetic models combing existing domain knowledge with data-driven learning, aiming at a white-box models directly from data.

Expected results and effects

The project is expected to deliver following results: - Identification of critical experimental conditions during kraft delignification through AI-treatment of large-scale process data - AI methods for development of data-driven kinetic model and those combing existing knowledge with data-driven learning Implementation of these results will directly contribute to improved process assessment, research approaches and new insights pertaining to process control & design, leading to increased resource efficiency, process flexibility and sustainability of the kraft pulping.

Planned approach and implementation

The project will be structured in two work packages (two post doc projects, 24 months each), addressing the problem from two different directions each: AI and delignification technology. The AI work package, WP1, (at Linköping University) will aim at developing a pure data driven model, while the delignification technology postdoc, WP2, (at Chalmers) will focus on gradually improving the existing analytical models with data-driven components. These two efforts will towards the end of the project converge into joint efforts towards developing an improved white-box model.