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

Zhang, Kaitian; Zheng, Zhong; Zhang, Liu; Yu, Lei; Chen, Sujun

doi:10.3390/pr11082404

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Fang [16], Xin [17] 2014-2023 Prediction of molten steel temperature Zhou [18], Wang [19], Zang [20] 2022-2023 Prediction of oxygen demand Wang [21] 2017 Prediction of ladle furnace temperature Takalo-Mattila [22], Chen [23], Li [24], Wu [25], Zhao [26], Xie [27], He [28], Boto [29], Chen [30], Xu [31], Orta [32],…”

Section: Review Of Dynamic Problems In Complex Industrial Processesmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

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As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields.

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Section: Review Of Dynamic Problems In Complex Industrial Processesmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

View full text Add to dashboard Cite

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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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“…Steel is one of the most important metals, owing to its wide range of uses from structural applications to automotive, aerospace, and shipbuilding industries [1][2][3][4]. The reason why steel is an important metal is because of its mechanical properties such as high strength, high toughness, and relatively easy production and processing [5].…”

Section: Introductionmentioning

confidence: 99%

Effect of the Molybdenum Content on Wear and Corrosion Behavior of Fe-B-Based Surface-Alloyed Layer

Kocaman

2023

Coatings

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In this study, Fe-Mo-B-based hardfacing electrodes containing different amounts of Mo were coated on an AISI 1020 steel substrate using the electric arc welding method. The findings show that molybdenum is highly effective on the microstructure and minor changes in the coating composition affect the phases and morphological properties. In the hardness tests, an increase of 73% was achieved in the Fe14Mo2B4-based hardfacing coating, compared to the base material, and a 30% increase was achieved, compared to the Fe16B4-based coating. The highest hardness value was measured as 56.4 HRC and the highest phase hardness was measured as 3228 HV in the FeMo2B4 phase. The lowest wear rate was measured in the Fe14Mo2B4-based coating. The wear rate of the Fe14Mo2B4-based coating was 8.1 times lower than that of the substrate material and 4.7 times lower than that of the Fe16B4-based coating. According to corrosion test results, the highest corrosion resistance was obtained in the Fe16B4-based coating. The current density value of the Fe16B4-based coating was measured to be 13.6 times lower than that of the substrate material.

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

Cited by 3 publications

References 20 publications

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

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

Effect of the Molybdenum Content on Wear and Corrosion Behavior of Fe-B-Based Surface-Alloyed Layer

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