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Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology

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International Journal of Minerals,Metallurgy and Materials 2023年第9期30卷 1651-1666页

作者： Quan Shi Jue Tang Mansheng Chu School of Metallurgy Northeastern UniversityShenyang 110819China Institute for Frontier Technologies of Low-carbon Steelmaking Northeastern UniversityShenyang 110819China Engineering Research Center of Frontier Technologies for Low-carbon Steelmaking(Ministry of Education) Shenyang 110819China

Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models i

关键词： BF ironmaking intelligent BF industrial big data machine learning integrated mechanism and data

Incremental QR-based tensor-train decomposition for industrial big data

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The Journal of China Universities of Posts and Telecommunications 2021年第1期28卷 10-23页

作者： Chen Yanping Jin Xiaodong Xia Hong Wang Zhongmin School of Computer Technology Xi'an University of Posts and TecomniunicationsXi'an 710121China Shaanxi Key Laboratory of Network Dala Analysis anti Intelligent Processing Xi'an L nivarsity of Posts and TelecommunicationsXi'an 710121China

industrial big data was usually multi-source, heterogeneous, and deeply intertwined. It had a wide range of data sources, high data dimensions, and strong data correlation. In order to effectively analyze and process streaming industrial big data generated by edge computing, it was very important to provide an effective real-time incremental data method. However, in the process of incremental processing, industrial big data incremental computing faced the challenges of dimensional disaster, repeated calculations, and the explosion of intermediate results. Therefore, in order to solve the above problems effectively, a QR-based tensor-train(TT) decomposition(TTD) method and a QR-based incremental TTD(QRITTD) method were proposed. This algorithm combined the incremental QR-based decomposition algorithm with an approximate singular value decomposition(SVD) algorithm and had good scalability. In addition, the computational complexity, space complexity, and approximation error analysis were analyzed in detail. The effectiveness of the three algorithms of QRITTD, non-incremental TTD(NITTD), and TT rank-1(TTr1) SVD(TTr1 SVD)were verified by comparison. Experimental results show that the SVD QRITTD method has better performance under the premise of ensuring the same tensor size.

关键词： tensor-train decomposition incremental processing edge computing industrial big data

On Fault Prediction Based on industrial big data

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On Fault Prediction Based on Industrial Big Data

Preliminary build and application of a data analysis platform for coiled tubing steel strips

第36届中国控制会议

作者： Qingsong Han Huifang Li Wei Dong Yafei Luo Yuanqing Xia School of Automation Beijing Institute of Technology

ISBN: (纸本)9781538629185

Fault-free production is a basic characteristic of intelligent workshop.The existing model-based fault prediction methods depend much more on the precise models of the equipment,while the data-driven ones can only use some basic state data with very limited volume.These features make them not only impractical,but also not able to meet the real-time requirement of analyzing industrial big data under the environment of industrial Internet of Things.This paper presents a fault prediction method based on industrial big data,which directly excavates the relationship between the data such as the sound and status data,and the equipment faults by machine learning methods.What is more,the equipment state can be monitored in real time leading to the failure would be checked out timely.The simulation result shows that our method has high accuracy and real-time features compared with the existing ones.

关键词： Intelligent workshop industrial big data fault prediction neural network

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Baosteel Technical Research 2022年第3期16卷 19-26页

作者： ZHANG Haozhen ZHANG Chuanguo WANG Pengjian Research Institute Baoshan Iron&Steel Co.Ltd.Shanghai 201999China Products&Technique Management Department Baoshan Iron&Steel Co.Ltd.Shanghai 201900China

To solve the problems in the quality control and improvement of coiled tubing steel strips production, such as scattered and inefficient production data, difficult performance fluctuation factor analysis, complex multivariate statistical analysis, and low accuracy and difficulty in mechanical property prediction, an industrial data analysis platform for coiled tubing steel strips production has been preliminarily developed.As the premise and foundation of analysis, industrial data collection, storage, and utilization are realized by using multiple big data technologies.With Django as the agile development framework, data visualization and comprehensive analyses are achieved.The platform has functions including overview survey, stability analysis, comprehensive analysis(such as exploratory data analysis, correlation analysis, and multivariate statistics),precise steel strength prediction, and skin-passing process recommendation.The platform is helpful for production overviewing and prompt responding, laying a foundation for an in-depth understanding of product characteristics and improving product performance stability.

关键词： coiled tubing steel strips industrial big data data analysis platform prediction

Application of industrial Internet Identifier in Optical Fiber industrial Chain

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ZTE Communications 2020年第1期18卷 66-72页

作者： SHI Zongsheng JIANG Jian JING Sizhe LI Qiyuan MA Xiaoran Jiangsu ZTT LINK Limited Company NantongJiangsu 226000China

The industrial Internet has germinated with the integration of the traditional industry and information technologies.An identifier is the identification of an object in the industrial Internet.The identifier technology is a method to validate the identification of an object and trace it.The identifier is a bridge to connect information islands in the industry,as well as the data basis for building a technology application ecosystem based on identifier resolution.We propose three practical applications and application scenarios of the industrial Internet identifier in this paper.Future applications of identifier resolution in the industrial Internet field are also presented.

关键词： industrial Internet application of identifier ecology of information application industrial big data identifier resolution

Nonlinear Successive Compensation Method for Plant-Wide Production Index Based on industrial data

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Nonlinear Successive Compensation Method for Plant-Wide Prod...

Monitoring and path optimization of catalytic reformer in a refinery:principal component analysis and big data application

第31届中国过程控制会议（CPCC2020）

作者： Kang Li Fuli Wang Dakuo He Yan Liu Hualu Zhang School of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Due to the complexities of high-dimensionality, nonlinearity, many constraints, and process model mismatch,the plant-wide production index needs to be compensated when the production index is applied to the actual production process. This paper proposes a nonlinear successive compensation method for the plant-wide production index based on industrial data. Based on the set point of the production index obtained from the plant-wide optimization, the method uses the large amount of industrial data stored in the industrial process to establish the relationship between production index compensation and the increase of economic benefit, optimizing the solution value of the production index compensation that maximizes the economic benefit increment. Through iterative compensation, the method gradually improve the economic benefit of the production process. The proposed method was applied to the hydrometallurgical production process of a refinery. The results verified the effectiveness of the proposed method.

关键词： hydrometallurgical industrial big data optimal compensate plant-wide production index

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Monitoring and path optimization of catalytic reformer in a ...