Case-based reasoning method based on mechanistic model correction for predicting endpoint sulphur content of molten iron in KR desulphurization

Feng, Kai; Xu, Anjun; He, Dingyong; Yang, Lingzhi

doi:10.1080/03019233.2019.1615307

Cited by 15 publications

(5 citation statements)

References 17 publications

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“…Instead, it would be better to use cross-validation as it usually performs better in the model selection. [142] An interesting attempt to combine parametrized modeling with a case-based reasoning model (CBRM) in the modeling of a KR was carried out in the study by Feng et al [94] The approach in the study was referred to as the corrected model. In the approach, the end sulfur content was predicted separately with the CBRM and a parametrized model (PRM).…”

Section: Data-driven Modelsmentioning

confidence: 99%

“…In the approach, the end sulfur content was predicted separately with the CBRM and a parametrized model (PRM). The overall prediction result was corrected based on the following expression [94] ½S t,corr:…”

Section: Data-driven Modelsmentioning

confidence: 99%

“…It was found that the corrected model performed slightly better, determined with the hit rate of the prediction, than ANN and PMR individually. [94]…”

Section: Data-driven Modelsmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Modeling Hot Metal Desulfurization

Visuri

Vuolio

Haas

et al. 2020

steel research int.

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Hot metal desulfurization serves as the main unit process for removing sulfur in blast‐furnace based steelmaking. The available body of literature on modeling hot metal desulfurization is reviewed to provide an in‐depth analysis of the approaches used and results obtained. The mathematical models for reaction kinetics have evolved from simplistic rate equations to more complex phenomenon‐based models that provide useful information on the effect of physico‐chemical properties and operating parameters on desulfurization efficiency. Data‐driven approaches with varying levels of phenomenological basis have also been proposed with the aim of achieving better predictive performance in industrial scale applications. Bath mixing has been studied using physical and numerical modeling to optimize mixing conditions in ladles and torpedo cars. The coupling of gas‐particle jets and their penetration into the liquid have been a focal point of physical and numerical modeling. In recent years, the fluid flow phenomena in mechanically stirred ladles has been studied extensively using physical and numerical modeling. These studies have focused on the fluid flow field, reagent dispersion, and bubble dispersion.

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Section: Data-driven Modelsmentioning

confidence: 99%

Section: Data-driven Modelsmentioning

confidence: 99%

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A Review of Modeling Hot Metal Desulfurization

Visuri

Vuolio

Haas

et al. 2020

steel research int.

View full text Add to dashboard Cite

show abstract

“…Most studies [4][5][6][7][8] are from the perspective of metallurgical thermodynamics, studying the variation of Gibbs free energy of C, Si, Mn, P and other elements in molten steel, revealing the theory of selective oxidation in the steelmaking process, in order to control decarbonization and dephosphorization. The Static mechanism models cannot provide data feedback effectively and require long-term analysis, and more importantly, it can 't adapt to the changing conditions of the steel mill [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Influence Factor Analysis and Prediction Model of End-Point Carbon Content Based on Artificial Neural Network in Electric Arc Furnace Steelmaking Process

Yang

Guo

et al. 2022

Coatings

Self Cite

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In this manuscript, we consider the accuracy of end-point carbon content prediction affected by oxygen injection in multiple stages of electric arc furnace (EAF) melting process. Such a prediction would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. Principal component analysis (PCA) was used to normalize the 13 input variables affecting the endpoint carbon content. log-sigmoid and tan-sigmoid functions were used to verify the same sample, and it was found that the Mean squared error(MSE) of the model under logsig + logsig function was smaller, indicating that the model was more stable. At the same time, different hidden layer nodes were tried, and finally the structure of the model was determined as 13 × 10 × 8 × 1, and the activation function was logsig + logsig. Using historical smelting data to train and test the neural network model, the correlation coefficient (R) of the verified model is 0.7632, the model prediction is in the range of ±0.03%, the hit rate of the model is 64.5%, and the hit rate of the model is 42% in the range of ±0.02%. Combining the verification basis of the model with the metallurgical reaction principle of the EAF steelmaking process, a pretreatment method of phased input of total oxygen is proposed. The oxygen is divided into three stages, which are the oxygen consumption volume of 0–5 min, 5–30 min and more than 30 min and other variables are kept unchanged. The same neural network is used to train and verify the same data. After verification, the R of the oxygen staged model is 0.8274. The model prediction is in the range of ±0.03%, the hit rate of the model is 78.5%, and the hit rate of the model is 58% in the range of ±0.02%. Finally, an on-line carbon content prediction system based on artificial neural network model is developed and applied to actual production. Running results illustrated that the hit rate of end-point carbon content is 96.67%, 93.33% and 86.67%, respectively when the prediction errors are within ±0.05%, ±0.03% and ±0.01%, the improved neural network model can effectively predict the end-point carbon content, which provides a good basis for the carbon content at the end point of EAF steelmaking process.

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“…The Kanbara Reactor (KR) desulphurization process is widely used in various steel plants as an efficient pre-desulphurization process before the converter process [1,2]. The KR desulphurization process is that an impeller lined with refractory material is immersed in the ladle to rotate and stir the hot metal to generate a vortex.…”

Section: Introductionmentioning

confidence: 99%

Water modelling on particle dispersion during KR desulphurization process

Chen

Zhao

Cai

et al. 2022

Ironmaking & Steelmaking

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In the current study, a 1:7 scaled water model was established to investigate the dispersion of desulphurizer particles during the Kanbara Reactor (KR) desulphurization process. The concentration, movement, and dispersion of the particle in the liquid were visualized and analysed. The influence of the impeller rotation speed, immersion depth, particle density and addition amount, and ladle wall baffle on the particle dispersion were systematically studied. The minimum critical rotation speed at which particles began to disperse into the liquid under different parameters was obtained. A regression equation for the influence of the impeller immersion depth and particle mass proportion on the critical rotation speed was proposed. Particles were easier to be driven into the liquid by the stirring impeller due to the higher particle additions. The area proportion of dispersed particles using the baffle was increased by three times compared with the case without the baffle.

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Case-based reasoning method based on mechanistic model correction for predicting endpoint sulphur content of molten iron in KR desulphurization

Cited by 15 publications

References 17 publications

A Review of Modeling Hot Metal Desulfurization

A Review of Modeling Hot Metal Desulfurization

Influence Factor Analysis and Prediction Model of End-Point Carbon Content Based on Artificial Neural Network in Electric Arc Furnace Steelmaking Process

Water modelling on particle dispersion during KR desulphurization process

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