Thermo-chemical model for blast furnace process control with the prediction of carbon consumption

Gasparini, Vitor Maggioni; Castro, Luiz Fernando Andrade de; Quintas, Alfredo Carlos Bitarães; Moreira, Victor Eric de Souza; Viana, Arthur Oliveira; Andrade, Dimas Henrique Barros

doi:10.1016/j.jmrt.2016.12.001

Cited by 14 publications

(5 citation statements)

References 6 publications

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“…Faleiro et al [16] propuseram um método estatístico para calculo do consumo de carvão vegetal em altos-fornos com base em Modelos de Superfície de Resposta e Modelos de Regressão Linear, utilizando os dados históricos de consumo de matérias-primas e produção, obtendo modelos em que o R 2 varia entre 70%, para o alto-forno 2 do estudo no período de seca (abril a outubro), e 88,3% para o alto-forno 2 no período chuvoso (novembro a março).…”

Section: Discussão Dos Resultadosunclassified

Redes Neurais Artificiais Para Predição Do Consumo Total De Combustível De Um Alto-Forno

Gois¹,

Carvalho²,

Andretti³

et al. 2019

TMM

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Este artigo propõe o uso de redes neurais artificiais para a previsão do consumo total de combustível no alto-forno. Para tanto, foi considerado um conjunto de dados contendo 270 registros, com 19 variáveis de entrada, com base nos dados históricos médios de operação de um alto-forno no período de janeiro/2016 a junho/2017, e verificou-se que o modelo apresentou bons resultados com coeficiente de correlação de 0,837, rmse de 11,8 (treino) e 12.7 (teste). O modelo final apresenta uma camada de entrada com 19 neurônios, camada intermediária com 19 neurônios e camada de saída com 1 neurônio. Palavras-chave: Redes neurais artificiais; Consumo de combustível; Alto-forno.

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Section: Discussão Dos Resultadosunclassified

Redes Neurais Artificiais Para Predição Do Consumo Total De Combustível De Um Alto-Forno

Gois¹,

Carvalho²,

Andretti³

et al. 2019

TMM

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“…As shown in Figure 6, the carbon oxidation heat (Q g,1 ), which accounted for 77.51% of the total heat consumption, is the main energy source in the BF body. Coke and pulverized coal injection are the main carriers of carbon oxidation heat [33][34][35]. Therefore, these two parameters can reflect the energy consumption for BFIMP.…”

Section: Pcamentioning

confidence: 99%

An All-Factors Analysis Approach on Energy Consumption for the Blast Furnace Iron Making Process in Iron and Steel industry

et al. 2019

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The blast furnace iron making process (BFIMP) is the key of the integrated steel enterprise for energy saving due to its largest energy consumption proportion. In this paper, an all-factors analysis approach on energy consumption was proposed in BFIMP. Firstly, the BFIMP composition and production data should be collected. Secondly, the material flows and energy flows analysis models could be established based on material balance and the thermal equilibrium. Then, the all influence factors (mainly including material flows, energy flows and operation parameters) on energy consumption were obtained. Thirdly, the main influence factors, which influenced the coke ratio (CR) and the pulverized coal injection ratio (PCIR), were obtained by using the partial correlation analysis (PCA) method, because CR and PCIR were the key energy consumption performance in BFIMP. Furthermore, anall-factors analysis result could be achieved by a multivariate linear model (MLR), which was established through these main influence factors. The case study showed that the PCIR was the most effective parameter on CR; when it was increased by 1% (0.84 kg/t), the CR would reduce by 0.507 kg/t. Therefore, the increase in PCIR consumption is the key measure to realize energy saving for BFIMP. The results showed that the improvement of some material flows, energy flows and operation parameters could increase the amount of PCIR, such as sinter size, ore grade, sinter grade, M10, blast volume, blast temperature and especially for sinter alkalinity. Moreover, theall-factors analysis approach on energy consumption can widely be used in various BFIMPs, too.

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“…Such computer systems are commonly referred to as decision support systems (DSS). An analysis of the literature data reveals a variety of both the use of mathematical models, model systems, expert modules, and monitoring systems [1][2][3][4][5][6][7] for decision support, as well as complex DSS in BF production [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Development and Implementation of Decision Support Systems for Blast Smelting Control in the Conditions of PrJSC “Kamet-Steel”

Semenov

Shumelchyk

Horupakha

et al. 2022

Metals

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This article presents a description of three decision support systems (DSS) in the mode of an adviser to the technological personnel of blast furnaces (BF), which were implemented by the Iron and Steel Institute of Z.I. Nekrasov (Dnipro, Ukraine) or underwent pilot testing as part of the automated control system of the BF shop of PrJSC “Kamet-steel” (Kamianske, Ukraine). The first DSS for managing the thermal state was implemented in 2021; it includes the entire list of information necessary for personnel in a convenient and compact form, generates recommendations in case of technology deviations, and, in the case of incorrect actions by the personnel, signals the need for correct actions. The main recommendations from the DSS are to correct the raceway adiabatic flame temperature, coke consumption when its characteristics are specified in (indicators of strength and abrasion, fractional composition, humidity, ash and sulfur), and ore load change. Using the system allows both reducing the specific coke consumption and preventing unplanned downtime. The second DSS for controlling the distribution of fuel additives over air tuyeres is based on information on thermal loads determined on water-cooled elements of tuyere tools. The main recommendations from the DSS are to adjust the amount of injected pulverized coal fuel on individual tuyeres in order to ensure a uniform distribution of the raceway adiabatic flame temperature around the circumference of the BF and, as a result, the energy efficiency of BF smelting. The third DSS for adjusting the parameters of the charging mode is based on information from the means of controlling the temperatures of the gas flow above the surface of the charge in the BF. The functioning of this DSS is based on determining the reference curves for the distribution of the gas flow along the BF radii, corresponding to the minimum consumption of coke and maximum productivity, and on the search for solutions by direct and iterative optimization methods, which allow one, by adjusting the charging parameters, to ensure a rational distribution of charge materials and gas flow in the BF.

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Thermo-chemical model for blast furnace process control with the prediction of carbon consumption

Cited by 14 publications

References 6 publications

Redes Neurais Artificiais Para Predição Do Consumo Total De Combustível De Um Alto-Forno

Redes Neurais Artificiais Para Predição Do Consumo Total De Combustível De Um Alto-Forno

An All-Factors Analysis Approach on Energy Consumption for the Blast Furnace Iron Making Process in Iron and Steel industry

Development and Implementation of Decision Support Systems for Blast Smelting Control in the Conditions of PrJSC “Kamet-Steel”

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