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
基本信息
- 批准号:557042-2020
- 负责人:
- 金额:$ 5.19万
- 依托单位:
- 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:
- 关键词:
项目摘要
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12年的合作关系。它旨在提高原铝冶炼厂的工艺智能和灵活性,以科普原材料质量下降和变化增加(即,石油焦、煤焦油沥青),影响了行业的发展。由C. Duchesne通过使用过程系统工程(PSE)工具(如多变量数据分析、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善实时决策。数据驱动的方法用于提取和评估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,并提供低成本的解决方案来支持工艺操作,与工业4.0概念保持一致。过去与Alcoa合作的研究重点是开发快速和非破坏性的传感器,以测量和跟踪碳阳极制造链沿着各种材料中原材料变化的影响。该计划中探索的新研究方向基于1)过去5年集团开发的传感器和监测方案,以及2)最近在美铝冶炼厂实施的新技术。更具体地说,先进的监测和控制方案将基于4点探针装置(Alcoa技术)和安装在几个铝电解槽上的单个阳极电流感测系统提出,所述4点探针装置测量多个点中的绿色阳极的电阻率。一个简化的一维电路模型的还原槽也将建立实时估计阳极-阴极距离。该计划的成果包括系统和技术,以帮助美铝和加拿大铝业提高能源效率和减少环境足迹。与该领域高素质人才的培训相结合,它将有助于保持加拿大铝工业在全球的竞争力和领导地位。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Duchesne, Carl其他文献
A Bootstrap-VIP approach for selecting wavelength intervals in spectral imaging applications
- DOI:
10.1016/j.chemolab.2009.09.005 - 发表时间:
2010-01-15 - 期刊:
- 影响因子:3.9
- 作者:
Gosselin, Ryan;Rodrigue, Denis;Duchesne, Carl - 通讯作者:
Duchesne, Carl
A machine vision approach to on-line estimation of run-of-mine ore composition on conveyor belts
- DOI:
10.1016/j.mineng.2007.04.009 - 发表时间:
2007-10-01 - 期刊:
- 影响因子:4.8
- 作者:
Tessier, Jayson;Duchesne, Carl;Bartolacci, Gianni - 通讯作者:
Bartolacci, Gianni
Mechanical, water absorption, and aging properties of polypropylene/flax/glass fiber hybrid composites
- DOI:
10.1177/0021998314568576 - 发表时间:
2015-12-01 - 期刊:
- 影响因子:2.9
- 作者:
Ghasemzadeh-Barvarz, Massoud;Duchesne, Carl;Rodrigue, Denis - 通讯作者:
Rodrigue, Denis
Selection and Tuning of a Fast and Simple Phase-Contrast Microscopy Image Segmentation Algorithm for Measuring Myoblast Growth Kinetics in an Automated Manner
- DOI:
10.1017/s143192761300161x - 发表时间:
2013-08-01 - 期刊:
- 影响因子:2.8
- 作者:
Juneau, Pierre-Marc;Garnier, Alain;Duchesne, Carl - 通讯作者:
Duchesne, Carl
Single-cell level analysis of megakaryocyte growth and development
- DOI:
10.1016/j.diff.2011.12.003 - 发表时间:
2012-04-01 - 期刊:
- 影响因子:2.9
- 作者:
Leysi-Derilou, Younes;Duchesne, Carl;Pineault, Nicolas - 通讯作者:
Pineault, Nicolas
Duchesne, Carl的其他文献
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{{ truncateString('Duchesne, Carl', 18)}}的其他基金
New Latent Variable Methods for selection of raw materials, process monitoring and product quality control
用于原材料选择、过程监控和产品质量控制的新潜变量方法
- 批准号:
RGPIN-2019-04800 - 财政年份:2022
- 资助金额:
$ 5.19万 - 项目类别:
Discovery Grants Program - Individual
New Latent Variable Methods for selection of raw materials, process monitoring and product quality control
用于原材料选择、过程监控和产品质量控制的新潜变量方法
- 批准号:
RGPIN-2019-04800 - 财政年份:2021
- 资助金额:
$ 5.19万 - 项目类别:
Discovery Grants Program - Individual
New Latent Variable Methods for selection of raw materials, process monitoring and product quality control
用于原材料选择、过程监控和产品质量控制的新潜变量方法
- 批准号:
RGPAS-2019-00118 - 财政年份:2020
- 资助金额:
$ 5.19万 - 项目类别:
Discovery Grants Program - Accelerator Supplements
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 - 财政年份:2020
- 资助金额:
$ 5.19万 - 项目类别:
Alliance Grants
New Latent Variable Methods for selection of raw materials, process monitoring and product quality control
用于原材料选择、过程监控和产品质量控制的新潜变量方法
- 批准号:
RGPIN-2019-04800 - 财政年份:2020
- 资助金额:
$ 5.19万 - 项目类别:
Discovery Grants Program - Individual
New Latent Variable Methods for selection of raw materials, process monitoring and product quality control
用于原材料选择、过程监控和产品质量控制的新潜变量方法
- 批准号:
RGPAS-2019-00118 - 财政年份:2019
- 资助金额:
$ 5.19万 - 项目类别:
Discovery Grants Program - Accelerator Supplements
A hyperspectral Raman imaging systems for process analytical technology developments
用于过程分析技术开发的高光谱拉曼成像系统
- 批准号:
RTI-2020-00218 - 财政年份:2019
- 资助金额:
$ 5.19万 - 项目类别:
Research Tools and Instruments
Quality control of baked carbon anodes and assessment of their performance in aluminium reduction cells
铝电解槽中烘烤碳阳极的质量控制及其性能评估
- 批准号:
509004-2017 - 财政年份:2019
- 资助金额:
$ 5.19万 - 项目类别:
Collaborative Research and Development Grants
New Latent Variable Methods for selection of raw materials, process monitoring and product quality control
用于原材料选择、过程监控和产品质量控制的新潜变量方法
- 批准号:
RGPIN-2019-04800 - 财政年份:2019
- 资助金额:
$ 5.19万 - 项目类别:
Discovery Grants Program - Individual
Quality control of baked carbon anodes and assessment of their performance in aluminium reduction cells
铝电解槽中烘烤碳阳极的质量控制及其性能评估
- 批准号:
509004-2017 - 财政年份:2018
- 资助金额:
$ 5.19万 - 项目类别:
Collaborative Research and Development Grants
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