Increasing Potlife of Hall–Héroult Reduction Cells Through Multivariate On-Line Monitoring of Preheating, Start-Up, and Early Operation

Tessier, Jayson; Duchesne, Carl; Tarcy, Gary P.; Gauthier, Claude; Dufour, Gilles

doi:10.1007/s11663-010-9359-3

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…These variables can be aggregated to the medium resolution (e.g., every 5 minutes) and the low resolution (e.g., every 1 hour), denoted by the squares in dash line. [17,19,18,16] based on daily or weekly data. Neural networks are developed to forecast weekly bath leve [15], resistance [9] and, temperature [14].…”

Section: Aluminum Smelting Processmentioning

confidence: 99%

Learning from Multi-Modality Multi-Resolution Data: an Optimization Approach

Zhu¹,

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Many complex real applications involve the collection of time series data with multiple modalities and of multiple resolutions. For example, in aluminum smelting processes, the recorded process variables typically reflect various aspects of these processes, such as pressure and temperature, and they are often obtained with different time resolutions, such as every 5 minutes and every day. How can we effectively leverage both the multi-modality property and the multi-resolution property of the data for the sake of more accurate prediction of key process indicators (e.g., the cell temperature of the aluminum smelting processes)?Different from existing techniques, which can only model the multi-modality property or the multi-resolution property, in this paper, for the first time, we propose to jointly model the two properties such that the prediction results are consistent across multiple modalities and multiple resolutions. To this end, we construct an optimization framework, which is based on a novel regularizer imposing such consistency. Then, we design an effective and efficient optimization algorithm based on randomized block coordinate descent. Its performance is evaluated on both synthetic and real data sets, outperforming state-of-the-art techniques.

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Section: Aluminum Smelting Processmentioning

confidence: 99%

Learning from Multi-Modality Multi-Resolution Data: an Optimization Approach

Zhu¹,

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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“…(f) multivariate online monitoring of preheating, startup, and early operation of aluminium reduction cells was investigated using PLS regression [25].…”

Section: Fault Detection and Diagnostic Techniquesmentioning

confidence: 99%

Aluminium Process Fault Detection and Diagnosis

Majid

Taylor

Chen

et al. 2015

Advances in Materials Science and Engineering

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The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting. However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system. This paper reviews aluminium process fault detection and diagnosis systems and proposes a taxonomy that includes four key elements: knowledge, techniques, usage frequency, and results presentation. Each element is explained together with examples of existing systems. A fault detection and diagnosis system developed based on the proposed taxonomy is demonstrated using aluminium smelting data. A potential new strategy for improving fault diagnosis is discussed based on the ability of the new technology, augmented reality, to augment operators’ view of an industrial plant, so that it permits a situation-oriented action in real working environments.

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“…Early operation variables were averaged over the first 2 months of pot operation. This period was shown to be the most critical for pot material integrity . These 69 variables are grouped into the PSE data block.…”

Section: The Aluminum Smelting Process and Available Datamentioning

confidence: 99%

“…This period was shown to be the most critical for pot material integrity. 33 These 69 variables are grouped into the PSE data block.…”

Section: The Aluminum Smelting Process Andmentioning

confidence: 99%

Multivariate Analysis and Monitoring of the Performance of Aluminum Reduction Cells

Tessier

Duchesne

Tarcy

et al. 2012

Ind. Eng. Chem. Res.

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A multiblock partial least squares (PLS) modeling approach is proposed in this Article for multivariate analysis and monitoring of aluminum reduction smelters and other electrochemical processes. These industries commonly operate from hundreds to a thousand of metallurgical reactors in parallel, which makes is difficult to build and maintain separate models for each unit. To cope with this problem, the proposed approach is based on the assumption that reactors sharing the same design (i.e., technology) and fed with the same lots of raw materials should also share a similar latent variable space. This allows reducing the number of latent variable models to build for process monitoring. The approach is illustrated on the basis of data collected from 31 reactors used for aluminum reduction. It was shown that well-defined regions in the raw material property and process operation spaces were associated with higher smelter performance. These could be used to establish joint multivariate specification regions for raw materials as well as for plant-wide process monitoring.

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Increasing Potlife of Hall–Héroult Reduction Cells Through Multivariate On-Line Monitoring of Preheating, Start-Up, and Early Operation

Cited by 10 publications

References 20 publications

Learning from Multi-Modality Multi-Resolution Data: an Optimization Approach

Learning from Multi-Modality Multi-Resolution Data: an Optimization Approach

Aluminium Process Fault Detection and Diagnosis

Multivariate Analysis and Monitoring of the Performance of Aluminum Reduction Cells

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