Development and Application of Prediction Model for End-Point Manganese Content in Converter Based on Data from Sub-Lance

Zhang, Bo; Xue, Zheng; Liu, Ke; Xiao, Wen

doi:10.4028/www.scientific.net/amr.683.497

Cited by 3 publications

(2 citation statements)

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“…At present, the oxygen prediction models for BOFs mainly include static prediction and dynamic prediction. Dynamic prediction mainly depends on auxiliary monitoring technology and gas analysis technology to revise a BOF's terminal point in real time [17][18][19]. However, most of the dynamic prediction models aim at a BOF's endpoint to optimize the process operation, which results in oxygen demand prediction only serving as the optimization means of composition and temperature rather than providing the scheduling of the oxygen pipe network balance.…”

Section: Introductionmentioning

confidence: 99%

Method for Dynamic Prediction of Oxygen Demand in Steelmaking Process Based on BOF Technology

et al. 2023

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Oxygen is an important energy medium in the steelmaking process. The accurate dynamic prediction of oxygen demand is needed to guarantee molten steel quality, improve the production rhythm, and promote the collaborative optimization of production and energy. In this work, a analysis of the mechanism and of industrial big data was undertaken, and we found that the characteristic factors of Basic Oxygen Furnace (BOF) oxygen consumption were different in different modes, such as duplex dephosphorization, duplex decarbonization, and the traditional mode. Based on this, a dynamic-prediction modeling method for BOF oxygen demand considering mode classification is proposed. According to the characteristics of BOF production organization, a control module based on dynamic adaptions of the production plan was researched to realize the recalculation of the model predictions. A simulation test on industrial data revealed that the average relative error of the model in each BOF mode was less than 5% and the mean absolute error was about 450 m3. Moreover, an accurate 30-minute-in-advance prediction of dynamic oxygen demand was realized. This paper provides the method support and basis for the long-term demand planning of the static balance and the short-term real-time scheduling of the dynamic balance of oxygen.

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Section: Introductionmentioning

confidence: 99%

Method for Dynamic Prediction of Oxygen Demand in Steelmaking Process Based on BOF Technology

et al. 2023

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“…In terms of oxygen demand forecasting, most forecasting models are based on predicting the production rhythm of basic oxygen furnaces (BOFs) [6][7][8][9]. Ruuska et al [10] presented a new model that describes the relationship between input variables such as blast temperature and blast composition and the output variable, namely, the tapped temperature of the steel.…”

Section: Introductionmentioning

confidence: 99%

Oxygen Demand Forecasting and Optimal Scheduling of the Oxygen Gas Systems in Iron- and Steel-Making Enterprises

Cheng,

Zhang,

Wang

2023

Applied Sciences

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Due to the imbalance between the supply and demand of oxygen, the oxygen systems of iron- and steel-making enterprises in China have problems with high oxygen emissions and high pressure in the pipelines, resulting in the energy consumption of oxygen production being high. To reduce the energy consumption of oxygen systems, this study took a large-scale iron- and steel-making enterprise as a case study and developed a two-stage forecasting and scheduling model. The novel aspect and progressiveness of this work are as follows: First, an oxygen demand forecasting model was developed based on the backpropagation neural network with genetic algorithm optimization (GABP) and is driven only by historical data. Compared with some complex models in the literature, although the accuracy of this model has been reduced, the model does not need to consider production plans for other process steps, making it more practical and feasible. Second, different from the existing literature, an oxygen production scheduling model was developed for load-variable ASUs with an internal compression process, and both the oxygen emissions and pipeline pressure are included in the objective function. The case study showed that based on the oxygen demand forecast and optimal scheduling, the oxygen emissions and pipeline pressure in the studied iron- and steel-making enterprise can be significantly reduced, thereby achieving considerable energy-saving effects and economic benefits. Specifically, the following conclusions were obtained: (1) For the oxygen demand forecast, the prediction accuracy of the GABP model was better than that of the ARIMA model. The average MAPE of the 12 sets of data of the ARIMA and GABP models was 23.8% and 20.2%, respectively. (2) By comparing the scheduling results and the field data, it was found that after scheduling, the amount of oxygen emissions decreased by 6.32%, the pipeline pressure decreased by 0.61%, and the energy consumption of oxygen compression decreased by 1.6%. Considering both the oxygen emission loss and the energy consumption of oxygen compression, the total power consumption of the studied oxygen system was reduced by 1.38%, resulting in electricity cost savings of approximately 9.03 million RMB per year.

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State of the art in applications of machine learning in steelmaking process modeling

Zhang,

Yang

2023

Int J Miner Metall Mater

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Development and Application of Prediction Model for End-Point Manganese Content in Converter Based on Data from Sub-Lance

Cited by 3 publications

References 1 publication

Method for Dynamic Prediction of Oxygen Demand in Steelmaking Process Based on BOF Technology

Method for Dynamic Prediction of Oxygen Demand in Steelmaking Process Based on BOF Technology

Oxygen Demand Forecasting and Optimal Scheduling of the Oxygen Gas Systems in Iron- and Steel-Making Enterprises

State of the art in applications of machine learning in steelmaking process modeling

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