Noise Classification in the Aluminum Reduction Process

Banta, Larry E.; Dai, Congxia; Biedler, Philip L.

doi:10.1007/978-3-319-48156-2_121

Cited by 4 publications

(4 citation statements)

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“…Therefore, the NCV contains alumina concentration information and can track the concentration change. Unlike the theoretical cell voltage, the NCV collected from industrial cells is a complex, nonstationary signal containing multiple frequency components [7], [18]- [20], which cannot be directly used for alumina concentration estimation.…”

Section: A Process Mechanism Analysismentioning

confidence: 99%

“…Equations (17) and (18) show that the frequency band of the alumina concentration component in the NCV is related to I ands f eed , which means that the SFB depends on the current intensity and the feed scheme. Therefore, different low-pass filters for feed control should be designed for different SFBs to obtain the components related to the alumina concentration in the NCV.…”

Section: Identification Of the Alumina Concentration Sfbmentioning

confidence: 99%

“…Let J 0 ∈ N + be the transform level of the partial TIDWT for the NCV. J 0 is then obtained by Equations (10), (17), (18) This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.…”

Section: ) Pass-band Determination For the Tidwt Scaling Filter Basementioning

confidence: 99%

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Estimating the Alumina Concentration During Aluminum Electrolysis by Identifying the Sensitive Frequency Band Using Marginal Kurtosis

et al. 2021

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Section: A Process Mechanism Analysismentioning

confidence: 99%

Section: Identification Of the Alumina Concentration Sfbmentioning

confidence: 99%

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Estimating the Alumina Concentration During Aluminum Electrolysis by Identifying the Sensitive Frequency Band Using Marginal Kurtosis

et al. 2021

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“…Three different patterns of noise were recognized, to assist in fault diagnosis. These are bubble noise [2], shortcircuiting noise, and metal pad roll noise [2,3].…”

Section: Fault Detection and Diagnostic Knowledgementioning

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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