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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 co.verter is one of the key parameters for the co.trol of the co.verter blowing process,which directly affects the tap-to-tap time of co.verter. 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 co.verter. A three-step method was utilized in the hybrid model. First, the oxygen co.sumption volume was predicted by the OBM model and DNN model, respectively. Seco.d, a more accurate oxygen co.sumption volume was obtained by integrating the OBM model and DNN model. Finally, the co.verter oxygen blowing time was calculated acco.ding to the oxygen co.sumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data co.lected from an integrated steel plant in China, and co.pared 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 co.pared with other models. The predicted hit ratio of oxygen co.sumption volume within the error±300 m^(3)is 96.67%;determination co.fficient (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 co.sumption volume and oxygen blowing time in the co.verter.

关键词： basic oxygen furnace oxygen co.sumption oxygen blowing time oxygen balance mechanism deep neural network hybrid model

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Genetic optimization of ladle scheduling in empty-ladle operation stage based on temperature drop co.trol

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Journal of iron and steel Research(International) 2022年第4期29卷 563-574页

作者： Yu-jie Hong Qing Liu Jian-ping Yang Jian Wang Shan Gao Hong-hui Li State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China laiwu iron and steel group Yinshan Section Steel Co.Ltd.Laiwu 271100ShandongChina

To optimize ladle scheduling in the empty-ladle operation stage of steel plants,a mathematical scheduling model was established for the empty-ladle operation stage,taking the minimum total waiting time in the empty-ladle operation stage as the optimization goal and setting the equipment assignment uniqueness as the key *** improved genetic algorithm was designed to calculate the mathematical scheduling *** the operation of the genetic algorithm,the strategy of"ladle temperature drop co.trol"was adopted to solve the problem of equipment co.flicts and reduce unreasonable ladle temperature drops to enhance"red-ladle"*** main production modes operating at 95%capacity in a Chinese steel plant were simulated using the genetic optimization *** results showed that the genetic optimization model co.ld improve the efficiency of ladle operation and reduce the total waiting time in the empty-ladle operation stage by 868–1147 min.

关键词： Ladle scheduling Empty-ladle operation Waiting time Genetic algorithm Temperature drop

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pH-dependent co.rosion initiation behavior induced by inclusions of low alloy steel in simulated marine environments

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Journal of iron and steel Research(International) 2023年第10期30卷 2067-2079页

作者： Heng Ma Zhong-xue Wang Yue Liu Yue-xiang Wang Teng-fei Wang Qing-pu Zhang Zhong-yu Cui yinshan section steel co.poration of laiwu steel group Ltd. Jinan 271104ShandongChina School of Materials Science and Engineering Ocean University of ChinaQingdao 266100ShandongChina

The initial pitting co.rosion behavior triggered by inclusions in S420 low alloy steel in acidic artificial seawater(ASW)and weak-alkaline ASW was *** geometric data of the co.rosion pits formed in the ASW with different pH were *** pH of the ASW has an obvious influence on the pit *** pits in acidic ASW exhibit a funnel shape,while those in weak-alkaline ASW present a co.e shape with a larger pit *** reason for the difference in the morphology and size of the co.rosion pits induced by inclusion is that it is easier to trigger micro-galvanic co.rosion in different structures and different areas of the pits in the acidic environment.

关键词： Low alloy steel Inclusion Pitting co.rosion Micro-galvanic co.rosion

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Quantitative evaluation of multi-process co.laborative operation in steelmaking–co.tinuous casting sections

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International Journal of Minerals,Metallurgy and Materials 2021年第8期28卷 1353-1366页

作者： Jian-ping Yang Qing Liu Wei-da Guo Jun-guo Zhang 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 Hesteel group Tangsteel co.pany Tangshan 063000China

The quantitative evaluation of multi-process co.laborative operation is of great significance for the improvement of production planning and scheduling in steelmaking–co.tinuous casting sections(SCCSs). However, this evaluation is difficult since it relies on an in-depth understanding of the operating mechanism of SCCSs, and few existing methods can be used to co.duct the evaluation, due to the lack of full-scale co.sideration of the multiple factors related to the production operation. In this study, three quantitative models were developed, and the multiprocess co.laborative operation level was evaluated through the laminar-flow operation degree, the process matching degree, and the scheduling strategy availability degree. Based on the evaluation models for the laminar-flow operation and process matching levels, this study investigated the production status of two steelmaking plants, plants A and B, based on actual production data. The average laminar-flow operation(process matching) degrees of SCCSs were obtained as 0.638(0.610) and 1.000(0.759) for plants A and B, respectively, for the period of April to July 2019. Then, a scheduling strategy based on the optimization of the furnace-caster co.rdinating mode was suggested for plant A. Simulation experiments showed higher availability than the greedy-based and manual strategies. After the proposed scheduling strategy was applied,the average process matching degree of the SCCS of plant A increased by 4.6% for the period of September to November 2019. The multi-process co.laborative operation level was improved with fewer adjustments and interruptions in casting.

关键词： steelmaking–co.tinuous casting multi-process co.laborative operation quantitative evaluation model laminar-flow operation process matching scheduling strategy

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