Aluminium Process Fault Detection and Diagnosis

Majid, Noriza; Taylor, Marilyn; Chen, John J. J.; Young, Brent R.

doi:10.1155/2015/682786

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…Based on potline operation procedures, if the surface temperature of the potshell exceeds approximately 500 • C, the system operators consider that hotspot as an abnormal measurement. This detected thermal anomaly has to be monitored for any future excessive temperatures which could be caused by faults associated with the aluminum production process [28]. Regular potshell surface temperature measurements are performed at different locations to help in studying and analyzing potshell thermal balance.…”

Section: Environment and Requirementsmentioning

confidence: 99%

Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things

Ghazal

Basmaji

Yaghi

et al. 2020

Sensors

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Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.

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Section: Environment and Requirementsmentioning

confidence: 99%

Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things

Ghazal

Basmaji

Yaghi

et al. 2020

Sensors

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“…The use of neural networks, however, lacks the ability of generalize/explain behavior. Kalman filter [8] is used to estimate alumina concentration. While the issue of robustness may limit the effectiveness of using Kalman filter based methods.…”

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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“…One of the subsequent influences is that the heatexchanging system is able to control the formation and melting of the frozen ledge by adjusting the heat flow rate through the heat exchanger. is effect is very important because the shape of the frozen ledge in the cell is very much related to the cell stability of the electrolysis process [10] and further on the current efficiency [11]. Obviously, there are several key topics that need to be considered: (1) the compatibility between the heat-exchanging system and cell structures, (2) the maintenance of the thermal balance of the cell, (3) the heat medium material and heat reapplication, and (4) equipment reliability and failure diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Numerical Approach on Waste Heat Recovery through Sidewall Heat‐Exchanging in an Aluminum Electrolysis Cell

Yang

Tao

Wang

et al. 2021

Advances in Materials Science and Engineering

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This paper proposes a technical viewpoint for the recovery of waste heat in aluminum electrolysis. The idea of combining heat-generating electrolysis process and the heat-consuming alumina tube digestion process is discussed in detail. The structural design of the heat-exchanging system as well as the matching problems between the heat exchanger and cell design are also mentioned. Several major concerns including the automatic temperature regulation of the cell sidewall and the preferred selection principles for the heat medium are introduced. A 2 kA heat exchangeable cell is developed and a series of tests are carried out in the laboratory. It is found that approximate 80% of the sidewall waste heat can be recovered while the cell behaves steadily. It is also proved possible to control the thickness of the frozen ledge through adjusting the heat convection rate of the heat exchanger. The heat-exchanging system is also suitable for aluminum cells when the fluctuating wind power is applied as a major energy supply.

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Aluminium Process Fault Detection and Diagnosis

Cited by 6 publications

References 38 publications

Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things

Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things

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

A Numerical Approach on Waste Heat Recovery through Sidewall Heat‐Exchanging in an Aluminum Electrolysis Cell

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