Process control sensor systems for copper production, ProCuPro

铜生产中缺乏传感器和过程控制技术，实际上在大多数有色金属生产过程中也是如此，因为其中许多过程是火法冶金过程，即它们发生在高温和腐蚀性环境中。光学探头和光谱分析系统等独立式传感器可以为这一测量挑战提供完美的解决方案。部分由NSERC-Engage和OCE资助，女王大学和KPM已经朝着全光学温度探测器迈出了第一步，该探测器于2018年5月在汉堡的奥鲁比斯由一名女王的研究生进行了测试。 根据目前的提议，我们打算扩大用于高温工业过程在线探测的光学技术套件。我们将使用颗粒散射来获得尘埃颗粒的粒度分布。我们将使用光纤探测技术来测量原子发射，并使用吸收光谱进行化学分析。这些技术将通过脱机分析进行验证，例如通过气相色谱、X射线光电子能谱(XPS)和X射线衍射仪(X射线衍射仪)。 通过将光学在线测量和产品产率与可调整的输入参数(进料组成、SO2和O2浓度、燃料浓度)相关联，我们将创建一个操作条件数据库，使操作员能够针对所需的产品组成和产率来确定工艺目标。 拟议的工作非常符合“先进制造业--工业4.0”倡议的目标，因为它需要通过分布式和多参数传感器系统、深层数据库和机械操作员之间的互动，实现金属制造的数字化转型。通过将在线和离线信息与期望的产品产量相关联，人们可以预期提高可靠性、产品质量，同时降低能源成本和减少环境足迹。 研究生和博士后研究员将在女王大学和波茨坦大学的研究实验室、KPM的试点工厂以及最终在德国一家工业冶炼厂的生产车间接受培训。与德国项目合作伙伴UPPC和Loptek的互动将需要访问其设施。

Sensors and process control technology are lacking in copper production, and indeed in most non-ferrous production processes, because many of these processes are pyrometallurgical, i.e. they occur at high temperatures and in corrosive environments. Stand-off sensors such as optical probes and spectrometric systems can provide an elegant solution to this measurement challenge. Funded in part by NSERC-Engage and OCE, first steps have been taken by Queen's University and KPM towards an all-optical temperature probe, which had been tested by Aurubis, Hamburg in May 2018 by a queen's graduate student. With the current proposal, we intend to expand the suite of optical technologies for in-line probing of high-temperature industrial processes. We will use particle scattering to obtain dust particle size distributions. We will use fiber probe technologies to measure atomic emission, and absorption spectroscopy for chemical analysis. These techniques will be validated using off-line analysis through, e.g. by gas chromatography, X-ray Photoelectron Spectroscopy (XPS) and X-ray Diffraction (XRD). By correlating optical online measurements and product yields to the adjustable input parameters (feed composition, SO2 and O2 concentration, fuel concentration) we will create a database of operating conditions that will allow the operator to target the process towards a desired product composition and yield. The proposed work aligns very well with the objectives of the initiative "Advanced Manufacturing-Industry 4.0" as it entails the digital transformation of metal manufacturing, through the interactions between a distributed and multi-parameter sensor system, a deep database and the machinery operators. By correlating online and off-line information with desired product yields one may expect to increase reliability, product quality while reducing energy costs and reducing the environmental footprint. Graduate students and postdoctoral fellows will be trained in research lab at Queen's and Potsdam University, at a pilot plant at KPM and eventually on the production floor of an industrial smelter in Germany. Interactions with German project partners UPPC and Loptek will require visits to their facilities.