Autonomous control of a process chain for CO2 carbonation by use of mine waste

避免灾难性的气候变化需要大幅减少温室气体排放，并在永久储存二氧化碳的同时，从大气中消除已经排放的二氧化碳。通过碳矿化，二氧化碳可以以环境友好、稳定的碳酸盐形式储存，从而使矿物碳化成为一种永久性的、无泄漏的二氧化碳处置方法。工业废渣中富含钙、镁，可作为矿物碳化废水泥(1gt/a)、煤粉(600m t/a)和炼钢废渣(400m t/a)的原料。由于矿物废物的原料质量(成分和粒度)不同，在有效的工艺操作方面存在知识差距，同时在回收有价值的高纯度目标产品(CaCO3，MgCO3)的同时最大限度地固定二氧化碳。本项目通过开发一个自主、自学习的矿山废物二氧化碳碳化过程链来解决这个问题，该过程链考虑了四个步骤：矿物提取、过滤、选择性沉淀和离心分类。该项目是卡尔斯鲁厄理工学院机械过程工程研究所(IMVM)(Marco Gleiü博士)、马格德堡俄勒冈州立大学(OvGU Magdeburg)过程工程研究所(IVT)(凯·桑德马赫教授)和德州大学凯泽斯劳滕大学机械与汽车工程机电一体化主席(Nim Bajcinca教授)之间的密切合作。自动过程链将能够识别上述影响过程的动态变化和干扰，并相应地引导过程达到最大可能生产率的状态，同时确保碳酸盐产品所需的纯度。在不可预见的情况下，导致非最优或不希望看到的行为，控制器的自学习功能仍然应该使过程能够基于可观察到的状态变量而自主行为。在第一个项目期间，我们研究了过滤(步骤2)和选择性沉淀(步骤3)与过程动力学有关的问题，并开发了动态模型，这些模型集成在自学习稳健自主控制器(SLARC)中，用于真空带式过滤和选择性沉淀。第二个资金期涵盖工艺链的扩展，包括矿物提取(步骤1)和离心机分级(步骤4)。我们在这里讨论了这两个流程步骤以及整个流程链的自学习自主系统。此外，我们通过整合可显著改变整个过程的过程动态的再循环流动来关闭材料循环。

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. There are many industrial wastes rich in calcium and magnesium, which are usable as feedstocks for mineral carbonation waste cement (1 Gt/yr), coal fly ash (600 Mt/yr) and steelmaking slag (400 Mt/yr). Due to the different raw material qualities (composition and particle size) of the mineral waste streams, there is a knowledge gap in terms of efficient process operation while maximizing CO2 sequestration while recovering valuable, high-purity target products (CaCO3, MgCO3).This project addresses this issue by developing an autonomous, self-learning process chain for the CO2 carbonation of mine waste considering the four step of: mineral extraction, filtration, selective precipitation, and centrifugal classification. The project is a close collaboration between the Institute of Mechanical Process Engineering (IMVM) of the Karlsruhe Institute of Technology (Dr. Marco Gleiß), the Institute of Process Engineering (IVT) of the OVGU Magdeburg (Prof. Dr. Kai Sundmacher) and Chair of Mechatronics in Mechanical and Automotive Engineering at TU Kaiserslautern (Prof. Dr. Naim Bajcinca). The autonomous process chain will be able to recognize the mentioned dynamic variations and disturbances affecting the process and accordingly steer it towards a state of maximum possible productivity, while ensuring desired purities for the carbonate products. In cases of unforeseen scenarios leading to non-optimal or undesired behavior, the self-learning feature of the controller should nonetheless enable the process, based on the observable state variables, to behave autonomously. During the first project period we investigate the filtration (step 2) and selective precipitation (step 3) in relation to the process dynamics and develop dynamic models which are integrated within the Self-Learning Robust Autonomous Controller (SLARC) for vacuum belt filtration and selective precipitation.The second funding period covers the extension of the process chain to include mineral extraction (step 1) and centrifugal classification (step 4). We address here the self-learning autonomous systems for these two process steps as well as for the entire process chain. Furthermore, we close material cycles by integrating recirculation flows that can significantly change the process dynamics of the overall process.