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，并从大气中清除已经排放的二氧化碳，同时永久储存二氧化碳。通过碳矿化，CO2可以以环境友好和稳定的碳酸盐形式储存，从而使矿物碳酸化成为永久和无泄漏的CO2处置方法。有许多富含钙和镁的工业废料，它们可用作矿物碳酸化废水泥（1Gt/yr）、粉煤灰（600 Mt/yr）和炼钢炉渣（400 Mt/yr）的原料。由于不同的原材料质量由于矿物废物流的组成和颗粒大小不同，在有效的工艺操作方面存在知识空白，同时最大限度地封存CO2，同时回收有价值的高纯度目标产品（CaCO 3，MgCO 3）。该项目通过开发一个自主的、自学习的工艺链来解决这个问题，该工艺链用于矿山废物的CO2碳酸化，考虑到以下四个步骤：矿物提取、过滤、选择性沉淀和离心分级。该项目是卡尔斯鲁厄理工学院机械工艺工程研究所（IMVM）（Marco Gleiben博士）、马格德堡OVGU工艺工程研究所（IVT）（Kai Sundmacher教授博士）和凯瑟斯特恩理工大学机械与汽车工程机电一体化系主任（Naim Bajcinca教授博士）之间的密切合作。自主工艺链将能够识别影响工艺的上述动态变化和干扰，并相应地将其转向最大可能生产率的状态，同时确保碳酸盐产品的所需纯度。在不可预见的情况下，导致非最佳或不期望的行为，控制器的自学习功能，仍然应该使过程，根据可观察的状态变量，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）与过程动态的关系，并开发了动态模型，这些模型集成在用于真空带式过滤和选择性沉淀的自学习鲁棒自主控制器（SLARC）中。第二个资助期涵盖了工艺链的扩展，包括矿物提取（步骤1）和离心分级（步骤4）。我们在这里解决这两个过程步骤以及整个过程链的自学习自主系统。此外，我们通过整合再循环流来关闭材料循环，这可以显着改变整个过程的过程动态。

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.