Autonomous control of a process chain for CO2 carbonation by use of mine waste
Contact
Dr. Sandesh Hiremath
Gottlieb-Daimler-Str. 42
67663, Kaiserslautern
Phone: +49 (0)631/205-3455
Fax: +49 (0)631/205-4201
sandesh.hiremath(at)mv.uni-kl.de
Description
Avoiding catastrophic climate change requires dramatically decreasing greenhouse gas emissions and removing already-emitted CO2 from the atmosphere paired with permanent CO2 storage. Through carbon mineralization, CO2 can be stored as carbonates which are environmentally benign and stable, and thus make mineral carbonation a permanent and leakage free CO2 disposal method. Calcium and magnesium are the most common alkaline earth metals in nature, indicating that these are the most suitable feedstocks for carbonate formation. In natural minerals, the two metals usually appear in the form of silicates, mostly Antigorite (Mg3Si2O5(OH)4), Lizardite (Mg6[(OH)8|Si4O10), Forsterite (Mg2SiO4), Augite (CaMgSi2O6+Fe,Al) and Wollastonite (CaSiO3). In addition, there are many industrial wastes rich in calcium and magnesium, which can be used as feedstocks for mineral carbonation, waste cement (1 Gt/yr), coal fly ash (600 Mt/yr), steelmaking slag (400 Mt/yr), platinum group mineral (PGM) mine tailings (77 Mt/yr), and red gypsum (1.25 Mt/yr). Ex-situ mineral carbonation takes place above ground using mined rocks and wastes. It can proceed through direct and indirect processes. In general, direct mineral carbonation can be performed through direct gas solid mineral carbonation or aqueous mineral carbonation. Indirect mineral carbonation takes place in more than one stage, and can be accomplished via gas-solid mineral carbonation, the pH swing process, the molten salt process, acid extractions, ammonia extraction, caustic extraction, and bioleaching. From a technical point of view, mineral carbonation is more favored through the indirect route because higher purity products can be obtained. Furthermore, the calcium and magnesium conversion rates to carbonates are significantly higher in indirect processes. Indirect gas–solid mineral carbonation requires large amounts of heat input, which inherently increases the energy consumption of the process. Therefore, indirect liquid phase mineral carbonation processes are the much better choice. They consist of a process chain with four essential steps: 1) liquid-solid extraction of metal ions (Mg2+,Ca2+), 2) solid-liquid separation by filtration, 3) carbonation of metal ions contained in the filtrate by use of CO2, 4) separation of solid carbonate particles from the post-reaction mixture obtained in the carbonation step.
Goals
- Autonomous control of filtration and selective precipitation:
One of the core objectives of this project is to design and implement a self-learning and robust controller (SLARC) to enable autonomous functioning of a complex process in general and specifically the filtration and precipitation processes to be developed. - Development of an observable and controllable vacuum belt filter to apply maximum filtrate flow:
The temporal monitoring and control of the filtrate flow with simultaneous maximum ion concentration of Mg2+ and Ca2+ is important for the subsequent selective precipitation. Thus, we integrate measurement technology to observe conductivity, cake height and filtrate flow online for the processed slurry on the lab-scale vacuum belt filter. - Development of an observable and controllable selective precipitation process for high purity carbonates:
A dynamic particle population balance (PBE) model of the selective precipitation of MgCO3 and CaCO3 particles is to be formulated in WP 2.3 as a basis for the design of SLARC (WP 3). Supported by inline sensors (FTIR probe, pH probe, spectral extinction probe for the mean particle size) and online measurement technology (ion chromatography, flow-through microscopy), the PBE model shall allow the prediction of the temporal evolution of the particle size distribution as well as the chemical composition of the particle population.
Keywords
CO2 carbonation from mine waste
Belt filtration
Stochastic controller
Reinforcement learning
Autonomous control
SLARC
Funding
Deutsche Forschungsgemeinschaft (DFG)
Time span
Jan 2023 - Dec 2025