Quality control of baked carbon anodes and assessment of their performance in aluminium reduction cells

本研究计划的目的是改善原铝冶炼厂和阳极制造商的决策，以减轻阳极原材料（焦炭和沥青）质量下降和变异性增加对铝还原电池性能的影响。为了实现这一目标，新的过程分析传感器将被开发和测试，以快速和无损地评估绿色和烘烤阳极的质量。这些将用于设计自动控制策略，以最小化阳极电阻率，并开发烘烤阳极检测方案，以检测其内部结构（大孔隙和裂纹）中是否存在缺陷并进行诊断。研究计划的这一部分预计将有助于生产更高质量和更一致的阳极，并在出现异常情况时能够及时反馈纠正措施，以便在阳极制造过程中实施。在该计划的第二部分，还将提出建模工具，结合分析传感器提供的信息和从阳极制造和铝还原过程中常规收集的数据，以便更好地了解填充阳极如何影响电池的性能以及单个阳极如何被消耗。这些新知识随后将用于建立烘烤阳极电阻率和检查结果的多变量规范区域，以便在烘烤前对缺陷绿阳极和在电池中设置之前对缺陷烘烤阳极进行分类。该模型还可以支持决策制定哪些电池将是接受下一组新阳极的最佳候选人，以及在电池中的哪个位置可以最大限度地降低其性能恶化的风险。使用拟议的传感器和建模工具改进决策的预期好处包括节省成本、减少废物、降低碳消耗和提高能源效率，此外还有助于保持加拿大铝业在全球的竞争力和领导地位。

The aim of this research program is to improve the decision making of primary aluminum smelters and anode manufacturers to mitigate the impact of the declining quality and increasing variability of the anode raw materials (coke and pitch) on aluminum reduction cell performance. To achieve this, new process analytical sensors will be developed and tested for rapid and non-destructive assessment of green and baked anode quality. These will be used to design an automatic control strategy to minimize the anode electrical resistivity, and to develop a baked anode inspection scheme to detect the presence of defects within their internal structure (large pores and cracks) and diagnose them. This part of the research program is expected to contribute to producing higher quality anodes and more consistently, and to enable timely feedback corrective actions to be implemented on the anode manufacturing process when abnormal situations occur. In the second part of the program, modelling tools combining the information provided by the analytical sensors and the routinely collected data from the anode manufacturing and aluminum reduction processes will also be proposed in order to better understand how the anodes populating a cell affects its performance and how individual anodes are consumed. These new learnings will subsequently be utilized to establish multivariate specifications regions for baked anode electrical resistivity and inspection results in order to sort defect green anodes before baking, and defect baked anodes before being set in the cells. The models could also support decision making on which cells would be the best candidates to receive the next set of fresh anodes and in which position in the cell to minimize the risk of deteriorating its performance. Expected benefits from improved decision making using the proposed sensors and modelling tools includes costs savings, waste reduction, lower carbon consumption, and higher energy efficiency, in addition to help maintaining the competitiveness and leadership position of the Canadian aluminum industry Worldwide.