Feed forward neural network for prediction of end blow oxygen in LD converter steel making

Rajesh, N.; Khare, Malaya Ranjan; Pabi, S.K.

doi:10.1590/s1516-14392010000100005

Cited by 16 publications

(4 citation statements)

References 10 publications

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“…There are many variants of using ANNs for the modeling of the BOF process. [4,8,9,[14][15][16][17][18][19][20][21][22][23][24][25] For all of these, only between 6 and 18 features are used, and the number of used samples varies from 17 to 2500 with a majority at the lower end. The prediction accuracy typically reaches around 90 pct, but the data used are typically selectively chosen, and it is therefore likely that the result is biased in a positive direction.…”

Section: Related Work In Machine-learn-ing-based Prediction Modelsmentioning

confidence: 99%

Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System

Bae

Ståhl

et al. 2020

Metall Mater Trans B

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The steel-making process in a Basic Oxygen Furnace (BOF) must meet a combination of target values such as the final melt temperature and upper limits of the carbon and phosphorus content of the final melt with minimum material loss. An optimal blow end time (cut-off point), where these targets are met, often relies on the experience and skill of the operators who control the process, using both collected sensor readings and an implicit understanding of how the process develops. If the precision of hitting the optimal cut-off point can be improved, this immediately increases productivity as well as material and energy efficiency, thus decreasing environmental impact and cost. We examine the usage of standard machine learning models to predict the end-point targets using a full production dataset. Various causes of prediction uncertainty are explored and isolated using a combination of raw data and engineered features. In this study, we reach robust temperature, carbon, and phosphorus prediction hit rates of 88, 92, and 89 pct, respectively, using a large production dataset.

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Section: Related Work In Machine-learn-ing-based Prediction Modelsmentioning

confidence: 99%

Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System

Bae

Ståhl

et al. 2020

Metall Mater Trans B

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“…Temperature prediction models [5,6] for EAF were established using the neural networks. Rajesh et al [7] employed feedforward neural networks to predict the intermediate stopping temperature and end blow oxygen in the LD converter steel making process. Wang et al [8] constructed a molten steel temperature prediction model in a ladle furnace by taking the general regression neural networks as a predictor in their ensemble method.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making

Wang

Zhang

et al. 2019

Energies

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The present work proposes an integrated methodology for rule extraction in a vacuum tank degasser (VTD) for decision-making purposes. An extreme learning machine (ELM) algorithm is established for a three-class classification problem according to an end temperature of liquid steel that is higher than its operating restriction, within the operation restriction and lower than the operating restriction. Based on these black-box model results, an integrated three-step approach for rule extraction is constructed to interpret the understandability of the proposed ELM classifier. First, the irrelevant attributes are pruned without decreasing the classification accuracy. Second, fuzzy rules are generated in the form of discrete input attributes and the target classification. Last but not the least, the rules are refined by generating rules with continuous attributes. The novelty of the proposed rule extraction approach lies in the generation of rules using the discrete and continuous attributes at different stages. The proposed method is analyzed and validated on actual production data derived from a No.2 steelmaking workshop in Baosteel. The experimental results revealed that the extracted rules are effective for the VTD system in classifying the end temperature of liquid steel into high, normal, and low ranges. In addition, much fewer input attributes are needed to implement the rules for the manufacturing process of VTD. The extracted rules serve explicit instructions for decision-making for the VTD operators.

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“…Due to this feature, statistical models and artificial neural networks (ANN) have been studied to model a variety of complex processes. Typical steelmaking operations, such as Ladle Furnace (LF), Tian (2008) and Sampaio (2006); BOF blowing end dynamic control of carbon and/or temperature were performed by Wei (2016), Liu (2014), Liu (2014), Wang (2012), Bing-Yao (2011), Rajesh (2010), and Meradi (2008); phosphorus at endpoint control . Different strategies were applied according to the operational data used for the training process, sub-lance measurements, visual characteristics of flame, gas analysis, etc.…”

Section: Introductionmentioning

confidence: 99%

Hybrid model associating thermodynamic calculations and artificial neural network in order to predict molten steel temperature evolution from blowing end of a BOF for secondary metallurgy

Júnior

Silva

2018

REM, Int. Eng. J.

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The developed model is an association of thermodynamic calculations for dissolution of alloys, slag formers and the deoxidation reaction in the molten steel with two artificial neural network (ANN) models trained with industrial data, to predict the molten steel temperature drop from the blowing end of the BOF until the first measurement at secondary metallurgy. To calculate the associated energy for deoxidation, an experiment was designed to set up the parameters for oxygen partitioning among deoxidants, with timed aluminum addition during teeming being the main parameter. The temperature control in the teeming stage presented a standard deviation for the error of prediction of 5.46 o C, for transportation from the rinsing station to the secondary metallurgy of 2.79 o C. The association of all calculations presented an error standard deviation of 7.49 o C. The operational validation presented superior accuracy compared with the current method for controlling the temperature, resulting in a reduction in the aluminum consumption for heating at secondary metallurgy with a potential economy of U$ 4.07 million per year for a steel shop producing 5 million tons of steel yearly. The artificial neural network model confirmed its capacity for modeling a complex multivariable process and the separation of thermodynamic calculation provides a better adaptability to different steel grades with different teeming strategies.

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Feed forward neural network for prediction of end blow oxygen in LD converter steel making

Cited by 16 publications

References 10 publications

Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System

Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System

An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making

Hybrid model associating thermodynamic calculations and artificial neural network in order to predict molten steel temperature evolution from blowing end of a BOF for secondary metallurgy

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