利用多元数据分析和机器学习方法预测高炉炼铁过程中的铁水温度

IF 0.9 4区 材料科学 Q3 METALLURGY & METALLURGICAL ENGINEERING Metallurgical Research & Technology Pub Date : 2023-01-01 DOI:10.1051/metal/2023073
Arun Kumar, Ashish Agrawal, Ashok Kumar, Sunil Kumar
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引用次数: 0

摘要

采用前馈-反传播神经网络(FFBPN)方法和多元数据分析,提出了一种以铁水温度(HMT)形式预测高炉健康状况的新方法。铁水温度是控制铁水生产稳定流程、避免炼铁过程中发生重大危险事件的关键参数。健康状态似乎也可以在过早的时候预测高炉的性能水平,允许操作员采取必要的步骤来避免高炉恶化。高炉的健康状态是指高炉在热熔铁水生产过程中可能出现的稳定或不稳定状态,用于故障的查找。本文利用FFBPN和相关矩阵来确定高炉的健康状态。这是用Matlab (Version 2018Rb)软件完成的,该软件使用数据预处理、变量约简和数据集的选择属性。对该模型进行了训练、测试和验证,所有数据集组合的HMT预测相关系数达到96%。利用多个实际过程数据集预测的HMT有助于高炉过程不均匀性的识别。
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Prediction of hot metal temperature in a blast furnace iron making process using multivariate data analysis and machine learning methodology
The feed-forward back propagation neural (FFBPN) network method and multivariate data analysis are used to present a new approach for predicting the health of a blast furnace in the form of hot metal temperature (HMT), which is a crucial parameter to control the stable flow of hot metal production while avoiding major danger incidents during the ironmaking process. The health status also appears to predict the performance level of BF at a premature time, allowing the operator to take necessary steps to avoid BF deterioration. The BF’s health status designates the stability or instability of the BF, which may arise during the manufacturing process of hot molten iron, and is used to find the fault. In this paper, the health status of BF was determined with the help of a FFBPN and correlation matrix. This was done with Matlab (Version 2018Rb) software that uses data pre-processing, variable reduction, and a selective attribute of a data set. The FFBPN model has been trained, tested, and validated, and it has got 96% correlation coefficient of HMT prediction of combination of all data sets. The predicted HMT using several actual process data sets has been helpful in identifying the process irregularity in BF.
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来源期刊
Metallurgical Research & Technology
Metallurgical Research & Technology METALLURGY & METALLURGICAL ENGINEERING-
CiteScore
1.70
自引率
9.10%
发文量
65
审稿时长
4.4 months
期刊介绍: Metallurgical Research and Technology (MRT) is a peer-reviewed bi-monthly journal publishing original high-quality research papers in areas ranging from process metallurgy to metal product properties and applications of ferrous and non-ferrous metals and alloys, including light-metals. It covers also the materials involved in the metal processing as ores, refractories and slags. The journal is listed in the citation index Web of Science and has an Impact Factor. It is highly concerned by the technological innovation as a support of the metallurgical industry at a time when it has to tackle severe challenges like energy, raw materials, sustainability, environment... Strengthening and enhancing the dialogue between science and industry is at the heart of the scope of MRT. This is why it welcomes manuscripts focusing on industrial practice, as well as basic metallurgical knowledge or review articles.
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