Development of advanced monitoring and control schemes for the primary aluminum industry

为原铝行业开发先进的监测和控制方案

• 批准号: 557042-2020
• 负责人: Duchesne, Carl
• 金额: $ 5.19万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721647
• 关键词: Development; advanced; monitoring; control; schemes

This research program continues a 12-years ongoing partnership with Alcoa Corporation. It aims at improving process intelligence and agility of primary aluminum smelters to cope with the declining quality and increasing variability in raw materials (i.e., petroleum coke, coal tar pitch) affecting the industry since the past several years. The program led by Prof. C. Duchesne contributes to addressing these issues by using process systems engineering (PSE) tools, such as multivariate data analysis, process monitoring and quality control, to mitigate raw material variations and improve real-time decision making. Data-driven methods are used to extract and valorize the relevant information contained in the large industrial databases (Big Data) routinely collected by the smelters, and provide low-cost solutions to support process operations, aligned on the Industry 4.0 concepts. Past research in partnership with Alcoa focused on developing rapid and non-destructive sensors to measure and track the impact of raw material variability in various materials along the carbon anode manufacturing chain. The new research directions explored in this program are based 1) on sensors and monitoring schemes developed in the group in the last 5 years, and 2) on new technologies recently implemented at Alcoa smelters. More specifically, advanced monitoring and control schemes will be proposed based on a 4-points probe device (Alcoa technology) measuring the electrical resistivity of green anodes in multiple points, and individual anode electrical current sensing systems installed on several aluminum reduction cells. A simplified 1-D electrical circuit model of a reduction cell will also be built to estimate anode-cathode distances in real-time. The outcomes this program consist of systems and technologies to help Alcoa and the Canadian aluminum industry increase energy efficiency and reduce environmental footprint. Combined with the training of highly qualified personnel in the field, it will contribute to maintaining competitiveness and leadership of the Canadian aluminum industry Worldwide.

该研究项目延续了与美国铝业公司 (Alcoa Corporation) 长达 12 年的合作关系。它旨在提高原铝冶炼厂的流程智能和敏捷性，以应对过去几年以来影响该行业的原材料（即石油焦、煤焦油沥青）质量下降和变异性增加的情况。由 C. Duchesne 教授领导的项目致力于通过使用过程系统工程 (PSE) 工具（例如多元数据分析、过程监控和质量控制）来解决这些问题，以减少原材料变化并改进实时决策。数据驱动方法用于提取和评估冶炼厂定期收集的大型工业数据库（大数据）中包含的相关信息，并提供低成本解决方案来支持流程操作，符合工业 4.0 概念。过去与美铝合作的研究重点是开发快速、无损的传感器，以测量和跟踪碳阳极制造链中各种材料的原材料变化的影响。该项目探索的新研究方向基于 1) 该小组过去 5 年开发的传感器和监测方案，2) 美铝冶炼厂最近实施的新技术。更具体地说，将提出先进的监测和控制方案，该方案基于测量多点绿色阳极电阻率的 4 点探针装置（美铝技术），以及安装在多个铝电解槽上的单独阳极电流传感系统。还将建立还原电池的简化一维电路模型，以实时估计阳极-阴极距离。该计划的成果包括帮助美国铝业公司和加拿大铝业提高能源效率并减少环境足迹的系统和技术。结合该领域高素质人才的培训，它将有助于保持加拿大铝业在全球的竞争力和领导地位。