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Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

International Journal of Minerals,Metallurgy and Materials 2024年第1期31卷 106-117页

作者： Xin Shao Qing Liu Zicheng Xin Jiangshan Zhang Tao Zhou Shaoshuai Li State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China Engineering Research Center of MES Technology for Iron&Steel Production Ministry of EducationBeijing 100083China Laiwu Iron and Steel Group Yinshan Section Steel Co. Ltd.Jinan 271104China

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

关键词： basic oxygen furnace oxygen consumption oxygen blowing time oxygen balance mechanism deep neural network hybrid model

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Feature selection of BOF steelmaking process data by using an improved grey wolf optimizer

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Journal of Iron and Steel Research(International) 2022年第8期29卷 1205-1223页

作者： Zong-xin Chen Hui Liu Long Qi Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500YunnanChina Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500YunnanChina

basic oxygen furnace(BOF)steelmaking end-point control using soft measurement models has essential value for economy and ***,the high-dimensional and redundant data of the BOF collected by the sensors will hinder the performance of *** traditional feature selection results based on meta-heuristic algorithms cannot meet the stability of actual industrial *** order to eliminate the negative impact of feature selection application in the BOF steelmaking,an improved grey wolf optimizer(IGWO)for feature selection was proposed,and it was applied to the BOF data ***,the proposed algorithm preset the size of the feature subset based on the new encoding scheme,rather than the traditional uncertain number ***,opposition-based learning was used to initialize the grey wolf population so that the initial population was closer to the potential optimal *** addition,a novel population update method retained the features closely related to the best three grey wolves and probabilistically updated irrelevant features through measurement or random *** methods were used to search feature subsets to maximize search capability and stability of algorithm on BOF steelmaking ***,the proposed algorithm was compared with other feature selection algorithms on the BOF data *** results show that the proposed IGWO can stably select the feature subsets that are conductive to the end-point regression accuracy control of BOF temperature and carbon content,which can improve the performance of the BOF steelmaking.

关键词： basic oxygen furnace Feature selection Grey wolf optimization Regression Opposition-based learning

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An online BOF terminal temperature control model based on big data learning

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Journal of Iron and Steel Research(International) 2023年第5期30卷 875-886页

作者： Jia-wei Guo Dong-ping Zhan Guo-cai Xu Nai-hui Yang Bo Wang Ming-xin Wang Geng-wei You School of Metallurgy Northeastern UniversityShenyang 110819LiaoningChina Jianlong Acheng Iron&Steel Co. Ltd.Harbin 150000HeilongjiangChina

The development of basic oxygen furnace(BOF)intelligent steelmaking model based on artificial intelligence and big data is the focus of international research and *** the view of the current situation that the BOF cannot continuously detect the composition and molten steel temperature,combined with the monitoring results of the high-definition and high-brightness camera at the converter mouth,an online BOF terminal temperature control model is established based on big data learning case-based reasoning model and expert system *** on-site online operation shows that the model can effectively improve the"flying lance"phenomenon and the splashing condition,the stability and safety of smelting process are better than that of artificial smelting,the"flying lance"rate decreases from 39.2% to 0,the early splashing rate decreases from 21.4% to 13.3% and the late splashing rate decreases from 81.25% to 56.7%.When the temperature fluctuation is controlled at±15 oC,the hit rate of the terminal temperature under the automatic control of the model is 90.91%.

关键词： basic oxygen furnace Case-based reasoning Expert system Steelmaking Temperature

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Control Model of Multifunctional Hot Metal Ladles

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Journal of Iron and Steel Research(International) 2016年第12期23卷 1262-1267页

作者： Bang-fu HUANG Nai-yuan TIAN Zhi wei MA Zhe SHI Xiao-lei ZHOU Faculty of Metallurgical and Energy Engineering Kunming University of Science and TechnologyKunming 650093YunnanChina Clean Metallurgy Key Laboratory of Complex Iron Resources Kunming 650093YunnanChina School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing 100083China

For further research on the control model of muhifunctional hot metal ladles between the ironmaking and steelmaking interface, the hot metal ladies of K steel plant were taken as the object to analyze the operation process. The factors of blast furnace supply and basic oxygen furnace demand were proposed. According to the principle of supply and demand balance, the control model of hot metal was researched under the following factor conditions： equal to, greater than, and less than 1, respectively. The distribution model of the blast furnace, sleelmaking works, and online buffering was proposed. When the supply and demand factor is equal to ） , the turnover number of hot metal ladles equals 16 and the turnover cycle of hot metal ladles equals 512 min. When the factor is greater than 1, the total number of hot metal ladles is equal to the normal turnover number plus the turnover number of the cast iron machine. When the factor is less than 1, the total number of hot metal ladles is equal tO the normal turnover number plus the accumulating number. Satisfactory effects were obtained by applying the control model in produc tion. The numbers of turnover ladles and accumulating ladles were reduced.

关键词： ironmaking steelmaking interface muhifunctional hot metal ladle control model blast furnace basic oxygen furnace

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Dynamic Load Characterization of basic oxygen furnaces Using Random Vibration Analysis

Dynamic Load Characterization of Basic Oxygen Furnaces Using...

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第十届中国钢铁年会暨第六届宝钢学术年会

作者： Zangeneh Pouya Al-Dojayli Maher Maleki Majid Ghorbani Hamid Choey Mervyn Hatch Ltd.

basic oxygen furnaces（BOF） suffer from damages due to excessive dynamic loading and fatigue during their life time as a result of significant vibrations of the furnace during oxygen injection process. Typical BOF vibration responses can be classified as an indeterministic non-stationary vibration, in which the regime of the vibration changes with the continuous operation of oxygen blowing. In order to assess the structural integrity of BOFs, dynamic loads are key design variable and should be determined. In this paper, a methodology is presented to evaluate the dynamic loads generated by the BOF’s chemical reactions through random vibration investigation using Finite Element Analysis（FEA）. The actual time domain vibration of the system is measured and transformed using Fourier Transformation to obtain the response defined in the frequency domain. Harmonic analysis is conducted to capture the Frequency Response Functions（FRF） of the furnace over a sweep of frequencies covering a wide range of the system natural frequencies, up to 90% of the system mass participation factor. The frequency domain response is used for comparison with FEA dynamic responses to characterize the vibrating load. The calculated loads can be used effectively to better design and/or retrofit BOF systems and their supporting structures to avoid major losses in structural assets.

关键词： basic oxygen furnace random vibration load characterization PSD signal processing

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Prediction of Endpoint Phosphorus Content of Molten Steel in BOF Using Weighted K-Means and GMDH Neural Network

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Journal of Iron and Steel Research(International) 2012年第1期19卷 11-16页

作者： WANG Hong-bing XU An-jun AI Li-xiang TIAN Nai-yuan School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Key Laboratory of Advanced Control of Iron and Steel Process of Ministry of Education Beijing 100083China School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing 100083China

The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phos- phorus content of molten steel in BOF （basic oxygen furnace）. At the clustering stage, the weighted K-means is performed to generate some clusters with homogeneous data. The weights of factors influencing the target are calcu- lated using EWM （Entropy Weight Method）. At the predicting stage, one GMDH （Group Method of Data Handling） polynomial neural network is built for each cluster. And the predictive results from all the GMDH polynomial neural networks are integrated into a whole to be the result for the hybrid method. The hybrid method, GMDH polnomial neural network and BP neural network are employed for a comparison. The results show that the proposed hybrid method is effective in predicting the endpoint phosphorus content of molten steel in BOF. Furthermore, the hybrid method outperforms BP neural network and GMDH polynomial neural network.

关键词： basic oxygen furnace endpoint phosphorus content K-means neural network GMDH

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A Novel RBF Neural Network Based on Data Dispersion Level and Its Application in BOF Endpoint Prediction 25th

A Novel RBF Neural Network Based on Data Dispersion Level an...

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第25届中国控制与决策会议

作者： Zhang Yu-xian Liu Tong Wang Jian-hui School of Electrical Engineering Shenyang University of Technology College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781467355339

The basic oxygen furnace （BOF） process is a primary method of steel-making. The endpoint targets must be strictly control. However, it is difficult to accurately predict endpoint targets in BOF. In this paper, a clustering method is proposed in which data dispersion level and new metric are introduced respectively. And the proposed clustering method is applied to obtain accurate neural network centers in order to improve accuracy of Radial Basis Function （RBF） neural network. Then a novel RBF neural network is built for the endpoint prediction in BOF process. Finally, an example of endpoint prediction is shown, the simulation results indicate that the influence of disperse and noisy data is decreased, clustering accuracy is increased and the accuracy of endpoint prediction based on RBF neural networks is improved.

关键词： basic oxygen furnace Endpoint Prediction RBF Neural Networks Clustering Data Dispersion Level

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End-point Prediction of BOF Steelmaking Based on Wavelet Transform based Weighted TSVR

End-point Prediction of BOF Steelmaking Based on Wavelet Tra...

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第37届中国控制会议

作者： Chuang Gao Minggang Shen Lidong Wang University of Science and Technology Liaoning

In this paper, a novel static prediction model is proposed based on wavelet transform based weighted TSVR（WTWTSVR）. New weighted matrix and coefficient vector are added into the objective functions of TSVR to improve the performance of the algorithm. The performance test illustrates the effectiveness of the proposed method. The results of proposed BOF model show that the prediction error bound with 0.005% in carbon content and 10℃ in temperature can achieve a hit rate of 90% and 94%, respectively. In addition, the double hit rate of 84% is the best result by comparing with other three existing methods. Therefore, the proposed method can provide a significant reference for real BOF applications.

关键词： basic oxygen furnace Twin Support Vector Regression Wavelet Transform Prediction model

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