An Intelligent Control Strategy for Iron Ore Sintering Ignition Process Based on the Prediction of Ignition Temperature

Du, Sheng; Wu, Min; Chen, Xin; Cao, Weihua

doi:10.1109/tie.2019.2898581

Cited by 25 publications

(10 citation statements)

References 27 publications

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“…The other studies are in these references. [86][87][88] For the ignition temperature control modelling in the sintering process, Du et al [89] proposed an intelligent control strategy for the iron ore sintering ignition process based on the prediction of ignition temperature. Ying et al [40] developed a proportional-integral-derivative (PID) neural network to control the ignition temperature according to its non-linear, large delay, and slow timevarying characteristics.…”

Section: Control Modelling For the Sintering Processmentioning

confidence: 99%

Data‐driven modelling methods in sintering process: Current research status and perspectives

et al. 2022

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The sintering process, as a primary modus of the blast furnace ironmaking industry, has enormous economic value and environmental protection significance for the iron and steel enterprises. Recently, with the emergence of artificial intelligence and big data, data‐driven modelling methods in the sintering process have increasingly received the researchers' attention. But now, there is still no systematic review of the data‐driven modelling approaches in the sintering process. Therefore, in this article, we conduct a comprehensive overview and prospects on the data‐driven models for the purpose of intelligent sintering. First, the mechanism and characteristics of the sintering process are introduced and analyzed elaborately. Second, the detailed research status of the sintering process is illustrated from four aspects: key parameters prediction, control, optimization, and others. Finally, several challenges and promising modelling methods such as deep learning in the sintering process are outlined and discussed for future research.

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Section: Control Modelling For the Sintering Processmentioning

confidence: 99%

Data‐driven modelling methods in sintering process: Current research status and perspectives

et al. 2022

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“…In the case of unstable air pressure, stabilizing the ignition temperature of sintering has significant economic and scientific value. Du et al [ 11 ] proposed an intelligent control strategy based on the ignition temperature prediction for the ignition process of iron ore sintering. Combining the mechanism analysis method and the data-driven method, they established an ignition temperature prediction model.…”

Section: Related Workmentioning

confidence: 99%

“…In the metallurgical industry, powder metallurgy materials have always been indispensable industrial materials in medicine, railways, and military fields [ 8 ]. The sintering furnace is the central equipment in the powder metallurgy industry, a piece of high-energy-consuming equipment that makes the powder compact to obtain the required mechanical properties and microstructure via sintering [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Design and fault diagnosis of DCS sintering furnace’s temperature control system for edge computing

Wen

2021

PLoS ONE

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Under the background of modern industrial processing and production, the sintering furnace’s temperature control system is researched to achieve intelligent smelting and reduce energy consumption. First, the specific application and implementation of edge computing in industrial processing and production are analyzed. The industrial processing and production intelligent equipment based on edge computing includes the equipment layer, the edge layer, and the cloud platform layer. This architecture improves the operating efficiency of the intelligent control system. Then, the sintering furnace in the metallurgical industry is taken as an example. The sintering furnace connects powder material particles at high temperatures; thus, the core temperature control system is investigated. Under the actual sintering furnace engineering design, the Distributed Control System (DCS) is used as the basis of sintering furnace temperature control, and the Programmable Logic Controller (PLC) is adopted to reduce the electrical wiring and switch contacts. The hardware circuit of DCS is designed; on this basis, an embedded operating system with excellent performance is transplanted according to functional requirements. The final DCS-based temperature control system is applied to actual monitoring. The real-time temperature of the upper, middle, and lower currents of 1# sintering furnace at a particular point is measured to be 56.95°C, 56.58°C, and 57.2°C, respectively. The real-time temperature of the upper, middle, and lower currents of 2# sintering furnaces at a particular point is measured to be 144.7°C, 143.8°C, and 144.0°C, respectively. Overall, the temperature control deviation of the three currents of the two sintering furnaces stays in the controllable range. An expert system based on fuzzy logic in the fault diagnosis system can comprehensively predict the situation of the sintering furnaces. The prediction results of the sintering furnace’s faults are closer to the actual situation compared with the fault diagnosis method based on the Backpropagation (BP) neural network. The designed system makes up for the shortcomings of the sintering furnace’s traditional temperature control systems and can control the temperature of the sintering furnace intelligently and scientifically. Besides, it can diagnose equipment faults timely and efficiently, thereby improving the sintering efficiency.

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“…Different from the mechanistic model, the data-driven model is based on the actual sintering process data, which does not need to consider multiple coupled physical and chemical reactions that are difficult to describe, and focuses on exploring the correlation between the data [6]. Many studies have successfully applied machine learning algorithms to the prediction of process parameters in the steel industry [7][8][9][10][11][12], which shows the feasibility of applying data-driven models to the steel industry. For the prediction of sinter drum index based on datadriven model, some research progress has been made at home and abroad.…”

Section: Introductionmentioning

confidence: 99%

Prediction interval estimation of sinter drum index based on light gradient boosting machine and kernel density estimation

Xia

Liu

et al. 2023

Ironmaking & Steelmaking

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Owing to the uncertainty operation in the sintering process, it is easy to produce uncertain prediction errors in the single drum index prediction model, which makes the prediction results lack certain reliability. Accurate and reliable prediction of the drum index can help improve the drum index. In this paper, a prediction interval estimation method of drum index based on a light gradient boosting machine (LightGBM) and kernel density estimation (KDE) is proposed. LightGBM can obtain accurate points prediction of drum index, and then use the KDE method to obtain the estimated prediction interval of drum index. The comparison results of different methods show that LightGBM has high prediction performance, and KDE can well quantify the prediction error of drum index, which verifies the effectiveness of the prediction interval estimation method combined with LightGBM and KDE, and provides more reliable decision-making information for the optimisation of sintering process parameters.

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An Intelligent Control Strategy for Iron Ore Sintering Ignition Process Based on the Prediction of Ignition Temperature

Cited by 25 publications

References 27 publications

Data‐driven modelling methods in sintering process: Current research status and perspectives

Data‐driven modelling methods in sintering process: Current research status and perspectives

Design and fault diagnosis of DCS sintering furnace’s temperature control system for edge computing

Prediction interval estimation of sinter drum index based on light gradient boosting machine and kernel density estimation

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