Representation learning and predictive classification: Application with an electric arc furnace

Rippon, Lee D.; Yousef, Ibrahim; Hosseini, Behrooz Koohmareh; Bouchoucha, A.; Beaulieu, Julie; Prévost, C; Ruel, M.; Shah, S.L.; Gopaluni, R. Bhushan

doi:10.1016/j.compchemeng.2021.107304

Cited by 10 publications

(2 citation statements)

References 30 publications

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“…Li et al [11] developed a nonparallel hyperplanebased fuzzy classifier model to coordinate their model's accuracy and interpretability based on the closed smelting and hysteresis features of a blast furnace system and tested the classification impact utilizing blast furnace data. On the topic of industrial failure detection, Rippon et al [12] created a new industrial prediction classification problem and designed a machine learning methodology. They proposed a complete representation learning prediction classification framework by comparing conventional and contemporary representation learning approaches with data from an electric arc furnace.…”

Section: Related Literaturementioning

confidence: 99%

Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process

Li¹,

Liu

Guo

2022

Mathematics

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In this paper, we present a novel support vector machine learning method for multi-label classification in the steelmaking process. The steelmaking process involves complicated physicochemical reactions. The end-point temperature is the key to the steelmaking process. According to the initial furnace condition information, the end-point temperature can be predicted using a data-driven method. Based on the setting value of the temperature before tapping, multi-scale predicted errors of the end-point temperature can be calculated and divided into different ranges. The quality evaluation problem can be attributed to the multi-label classification problem of molten steel quality. To solve the classification problem, considering that it is difficult to capture nonlinear relationships between the input and output in linear models, we propose a novel support vector machine with robust low-rank learning, which has the characteristics of class imbalance without label correlations; a low-rank constraint is used to deal with high-order label correlations in low-dimensional space. Furthermore, we derive an accelerated proximal gradient algorithm and then extend it to handle the nonlinear multi-label classifiers. To validate the proposed model, experiments are conducted with real data from a practical steelmaking problem. The results show that the proposed model can effectively solve the multi-label classification problem in industrial production. To evaluate the proposed approach as a general classification approach, we test it on multi-label classification benchmark datasets. The results illustrate that the proposed approach performs better than other state-of-the-art approaches across different scenarios.

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Section: Related Literaturementioning

confidence: 99%

Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process

Li¹,

Liu

Guo

2022

Mathematics

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“…Regarding recent studies involving offline analysis of real historical data, there are some noteworthy works that deserve highlighting, such as: the development of a methodology for detecting the decomposition of reagents in a copolymerization autoclave using PCA [645]; the application of machine learning methods for decentralized monitoring of an effluent treatment plant [646]; the application of PCA to monitor the physicochemical properties of crude oil blends [647]; the application of CCA to historical process data for detection and diagnosis of a sensor fault in a fiscal metering station of an oil processing plant, highlighting the potential for economic gains which may result in such a real-time application [648]; the application of different latent variable models to develop a strategy aimed at an increasing understanding of biorefineries processes [649]; the extension of recursive PCA with big data methodologies for monitoring a fluorochemical plant [650]; the development of a virtual sensor based on machine learning to predict faults in a metallurgical process [651]; the development of a system that combines various techniques such as recurrent neural networks and PCA to predict abnormal conditions in catalytic cracking processes [652]; the application of neural network-based nonlinear PCA for fault detection in gas turbines [653]; and the application of autoencoders for unsupervised monitoring of blast furnaces [405].…”

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confidence: 99%

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Melo,

Câmara,

Pinto

2024

Processes

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This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different perspectives they provide. Data-driven modeling techniques are surveyed and categorized into two main groups: multivariate statistics and machine learning. Representative models, namely principal component analysis, partial least squares and artificial neural networks, are detailed in a didactic manner. Topics not typically covered by other reviews, such as process data exploration and treatment, software and benchmarks availability, and real-world industrial implementations, are thoroughly analyzed. Finally, future research perspectives are discussed, covering aspects related to system performance, the significance and usefulness of the approaches, and the development environment. This work aims to be a reference for practitioners and researchers navigating the extensive literature on data-driven industrial process monitoring.

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