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.

该研究计划持续了与Alcoa Corporation进行的12年持续合作伙伴关系。它旨在提高主要铝冶炼厂的过程智能和敏捷性，以应对以来几年以来，原材料的质量下降和不断增加的原材料（即石油焦炭，煤焦油沥青）的变化。由C. Duchesne教授领导的计划通过使用流程系统工程（PSE）工具（例如多元数据分析，过程监视和质量控制）来解决这些问题，以减轻原材料的变化并改善实时决策。数据驱动的方法用于提取和重视由冶炼厂常规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以支持流程操作，并在行业4.0概念上对齐。过去与ALCOA合作的研究致力于开发快速和非破坏性传感器，以测量和跟踪沿碳阳极制造链中各种材料中原材料可变性的影响。该计划中探索的新研究方向是1）基于过去5年中该小组中开发的传感器和监视方案，以及2）最近在Alcoa冶炼厂实施的新技术。更具体地说，将根据4点探针设备（ALCOA技术）提出高级监控和控制方案，该设备测量了多个点绿色阳极的电阻率，以及安装在几个铝还原细胞上的单个阳极电流传感系统。还将构建简化的1D电路模型，以实时估算阳极 - 阴极距离。该计划的结果包括系统和技术，以帮助铝业和加拿大铝业行业提高能源效率并减少环境足迹。结合对该领域高素质人员的培训，它将有助于维持全球加拿大铝制行业的竞争力和领导能力。