Deep weighted joint distribution adaption network for fault diagnosis of blast furnace ironmaking process

Gao, Dali; Zhu, Xiong zhuo; Yang, Chunjie; Huang, Xiaoke; Wang, Wenhai

doi:10.1016/j.compchemeng.2022.107797

Cited by 13 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Blast furnace ironmaking process involves complex physical and chemical changes, the purpose is to smelt iron ore into qualified pig iron 11) . Smooth furnace condition is a prerequisite to ensure the stable production of the blast furnace.…”

Section: Analysis Of Blast Furnace Smelting and Causes Of Hangingmentioning

confidence: 99%

Blast Furnace Hanging Diagnosis Model Based On ReliefF-Decision Tree

Li,

Meng,

Liu

et al. 2024

ISIJ Int.

View full text Add to dashboard Cite

Blast furnace hanging is an abnormal furnace condition with the highest frequency.The judgment of hanging mainly relies on manual analysis, but this approach has strong subjectivity and time lag. In order to solve the above problems, this paper proposes a ReliefF -Decision Tree based anomaly diagnosis model to identify the hanging. Firstly, 10 relevant parameters are extracted based on expert experience, and each characteristic parameter is weighted using ReliefF algorithm after preprocessing. Secondly, the calculated weights of the characteristic parameters are sorted from the largest to the smallest, and the seven most effective characteristic parameter sets are selected as the decision nodes of the decision tree, which is constructed according to the expert rules of hanging diagnosis to complete the training and testing of the classifier. Simulation results show that the accuracy of the ReliefF -Decision Tree model reaches 96.5%, and the recognition rate of the two anomalies of "burden stop" and "sudden rise of differential pressure" exceeds 80%, which is 83.1% and 87.5%, respectively, indicating that the performance of the model is good. Finally, the expert diagnosis system is built, which effectively improves the diagnosis efficiency of hanging.

show abstract

Section: Analysis Of Blast Furnace Smelting and Causes Of Hangingmentioning

confidence: 99%

Blast Furnace Hanging Diagnosis Model Based On ReliefF-Decision Tree

Li,

Meng,

Liu

et al. 2024

ISIJ Int.

View full text Add to dashboard Cite

show abstract

“…Deng et al [9] employed FactSage technology to construct a dynamic model of different oxides in the oxidation process of ultra-low carbon steel and carried out a detailed analysis of slag impurity oxides, which provided a significant contribution to the control of the steelmaking process. In light of the challenges of fewer data and large data fluctuation in blast furnace ironmaking, Gao et al [10] developed a migration learning approach for robust fault identification and demonstrated that the algorithm can solve the abnormal diagnosis of blast furnace ironmaking through tests. 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.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…The smooth operation of a BF is the key to obtaining a high quality, high yield, low consumption, and long life. Due to the dynamic time-varying and strong coupling in BF production, there is no specific index that can directly characterize the operation status of a BF [4]. Whether by using the direct observation method, such as by observing iron notch, slag notch, tuyere, and the movement state of a probe, or by using the indirect observation method, such as in analyzing CO 2 curve or judging hot air pressure, blast pressure, blast volume, and shaft temperatures, all of these have the disadvantage of one-sided localization and subjective experience.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Prediction Models for Blast Furnace Operating Status Based on Big Data Mining

Liu

et al. 2023

Metals

View full text Add to dashboard Cite

Based on the historical data of a commercial blast furnace (BF), the evaluation and prediction models for the BF comprehensive operating status were established by big data mining methods. Firstly, based on the data resources of the data warehouse of BF ironmaking, clean data were obtained by processing the original data with the problem of null values, outlier data, and blowing-down operations data. Then, the AHP_EWM_TPOSIS evaluation model was built with the combined weight of AHP and EWM and the improved TOPSIS algorithm. Finally, the model evaluation results were verified with the actual production situation, and the comprehensive matching rate reached 94.49%, indicating that the model can accurately judge the comprehensive operating status of BF. The evaluation result was the target parameter for building the BF comprehensive operating status prediction model. The results showed that the stacking model achieved better results than the base models in all indicators. The accuracy index of the deviation between the predicted value and the actual value within ±0.05 reached 94.50%, which meets the practical needs of BF production. The evaluation and prediction models provided timely and accurate furnace condition information to the operators in the BF smelting process, which promoted the long-term stable operation of the BF condition.

show abstract

Deep weighted joint distribution adaption network for fault diagnosis of blast furnace ironmaking process

Cited by 13 publications

References 16 publications

Blast Furnace Hanging Diagnosis Model Based On ReliefF-Decision Tree

Blast Furnace Hanging Diagnosis Model Based On ReliefF-Decision Tree

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

Evaluation and Prediction Models for Blast Furnace Operating Status Based on Big Data Mining

Contact Info

Product

Resources

About