Control and Communication Coordination for Industrial Digital Twins of Sintering Process

Chen, Cailian; Wen, Xiaojing; Bai, Xuehan; Xu, Lei; Ren, Cheng; Ye, Jiale; Ma, Yehan; Guan, Xinping

doi:10.1007/978-3-030-98064-1_15

Cited by 3 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Maged et al [95] proposed a variation-based autoencoder-long shortterm memory deep learning T 2 graph, which was applied to intelligent fault diagnosis of sintering process. Chen et al [96] employed autoencoders for intelligent modelling of industrial processes, validated their approach with sintering process cases, and developed digital twins to enable accurate production quality prediction. Wang et al [97] used the raw process data to train the autoencoder neural network to accurately capture defective products and achieve automatic prediction of product quality in sintering process.…”

Section: Autoencoder Model Structure and Its Applications In Sinteringmentioning

confidence: 99%

Application of deep learning in iron ore sintering process: a review

Gong,

Wang,

et al. 2024

J. Iron Steel Res. Int.

View full text Add to dashboard Cite

In the wake of the era of big data, the techniques of deep learning have become an essential research direction in the machine learning field and are beginning to be applied in the steel industry. The sintering process is an extremely complex industrial scene. As the main process of the blast furnace ironmaking industry, it has great economic value and environmental protection significance for iron and steel enterprises. It is also one of the fields where deep learning is still in the exploration stage. In order to explore the application prospects of deep learning techniques in iron ore sintering, a comprehensive summary and conclusion of deep learning models for intelligent sintering were presented after reviewing the sintering process and deep learning models in a large number of research literatures. Firstly, the mechanisms and characteristics of parameters in sintering processes were introduced and analysed in detail, and then, the development of iron ore sintering simulation techniques was introduced. Secondly, deep learning techniques were introduced, including commonly used models of deep learning and their applications. Thirdly, the current status of applications of various types of deep learning models in sintering processes was elaborated in detail from the aspects of prediction, controlling, and optimisation of key parameters. Generally speaking, deep learning models that could be more effectively implemented in more situations of the sintering and even steel industry chain will promote the intelligent development of the metallurgical industry.

show abstract

Section: Autoencoder Model Structure and Its Applications In Sinteringmentioning

confidence: 99%

Application of deep learning in iron ore sintering process: a review

Gong,

Wang,

et al. 2024

J. Iron Steel Res. Int.

View full text Add to dashboard Cite

show abstract

“…Therefore, it is beneficial to apply the DT technique for TS modelling and updating. Moreover, DT has a strong potential to improve productivity for the complex process industry since all kinds of models are applied to estimate and predict the system dynamics [20][21][22]. Recently, Zhou et al [23] built a cloud platform-based application of the iron-making DT.…”

Section: Introductionmentioning

confidence: 99%

Online Dynamic Modelling for Digital Twin Enabled Sintering Systems: An Iterative Update Data-Driven Method

Ding,

Liu,

et al. 2023

IET Signal Processing

Self Cite

View full text Add to dashboard Cite

The sintering process is a crucial thermochemical process in the blast furnace iron-making system. Tumble strength (TS), as a vital performance to assess sinter quality, is difficult to monitor due to the lack of timely measurement. Constructing a data-driven model for TS is an alternative for monitoring TS. However, the time-varying dynamic sintering process makes the task of modelling challenging. And the data are incomplete and insufficient in practice for modelling since there are unknown time delays in the system and lack actual TS value. The digital twin (DT) technique is a powerful tool to simulate the system dynamics with the real-time interaction between physical processes and virtual agents in cyberspace. This paper introduces a DT-enabled equivalent of the sintering system and proposes online data-driven modelling for TS monitoring. The time delay in the system is estimated for variable sequence alignment based on a modified maximum information coefficient method. The data used for modelling is enriched based on a multi-source information fusion technique. An adaptive update method is proposed to deal with the time-varying dynamics. The iterative forgetting factor-based algorithm is designed for the support vector regression method and guarantees a fast computational speed. Implementation and validation of the model on a DT-enabled sintering system show the efficiency of the proposed method. The accuracy of TS monitoring reaches 99.6% by analysis of 3 months’ data.

show abstract