Autonomous control of a process chain for CO2 mineralization by use of alkaline waste materials

• Applicants: Professor Dr.-Ing. Naim Bajcinca; Dr.-Ing. Marco Gleiß; Professor Dr.-Ing. Kai Sundmacher
• Subject Area: Mechanical Process Engineering ; Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 504852622

Avoiding catastrophic climate change requires a drastic reduction of greenhouse gas emissions, the removal of emitted CO2 from the atmosphere paired with permanent CO2 storage. Reaction with minerals allows CO2 to be converted into carbonates, which are environmentally friendly and stable, making CO2 mineralization a permanent and leak-free CO2 storage method. There are numerous industrial wastes containing Ca and Mg that are suitable as starting materials for CO2 carbonation, including waste cement (1 Gt/a), fly ash from coal combustion (600 Mt/a) and slag from steel production (400 Mt/a). Due to the widely varying raw material qualities (composition, particle sizes) of the mineral waste materials, there is a knowledge gap with regard to the optimal process operation for efficient CO2 mineralization, which would allow the recovery of high-purity target products (CaCO3, MgCO3) that can be used in other industrial sectors (including the paper industry). The present project addresses this issue by investigating the fundamentals for the development of an autonomous control system for the entire process chain of CO2 mineralization consisting of (1) mineral extraction, (2) filtration, (3) selective carbonate precipitation and (4) centrifugal separation. The project is based on a close cooperation between the Institute for Mechanical Process Engineering and Mechanics (KIT, Dr. Marco Gleiß), the Institute for Process Engineering (OVGU, Prof. Dr. Kai Sundmacher) and the Chair of Mechatronics in Mechanical and Automotive Engineering (RPTU, Prof. Dr. Naim Bajcinca). The autonomous control to be investigated should have self-learning properties and thus be able to recognize time-dependent external changes (in particular in the quality of the raw materials) on the basis of the observable state variables, to control the process chain to a new state of maximum productivity without human intervention, and at the same time to keep to the desired specifications (particle sizes, purities) of the carbonate products. In the second funding period, the KIT is working on continuous, autonomous mineral extraction and its hybrid modeling to predict kinetic parameters. The OVGU is investigating a periodically operated semi-batch reactor for the selective precipitation of carbonates by optimally controlling CO2 dosing and pH to achieve defined particle size distributions with the highest product purity. RPTU is developing the model-based control concept for the entire process based on a hierarchical architecture with the aim of ensuring a rapid adaptation of the process conditions to dynamically changing raw material qualities. Towards the end of the second funding period, the four process steps will be combined into a single process chain on a laboratory scale to experimentally evaluate the autonomous control concept for real alkaline-rich waste materials.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2364: Autonomous processes in particle technology - Research and testing of concepts for model-based control of particulate processes
• Co-Investigator: Dr. Mohammad Al Khatib