Blast Furnace Flame Temperature Calculation Improvements

On-site atmospheric experiments using a one-fifth-scale model combustor of the ABB gas turbine type 11N2-LBtu have been recently carried out at the Kawasaki Steel Works in Mizushima, Japan. A diffusion type burner of special design was used to match the extremely low heating value (2360 kJ/kg) and the high stoichiometric fuel/air ratio (1.6 kg/kg) of the Blast-Furnace Gas (BFG). Except for pressure, all burner inlet conditions were simulated as in the actual gas turbine. The burner demonstrated an excellent burning stability behaviour over the entire operation range and stably burned pure BFG down to an equivalence ratio of 0.25, without any supplementary fuel. Due to the low adiabatic flame temperature and slow kinetics, approximately 1 ppm NOx was measured in the exhaust gas. The chemical kinetics of NOx production and CO burnout were also calculated using a chemical kinetics code and reasonable agreement with the experimental results was obtained. In dual-fuel operation (BFG with oil, propane, or coke-oven gas) the burner also demonstrated a wide flame stability range.

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Blast furnace operation with natural gas injection and minimum theoretical flame temperature

Ironmaking & Steelmaking ◽

10.1179/174328107x155240 ◽

2009 ◽

Vol 36 (1) ◽

pp. 12-18 ◽

Cited By ~ 19

Author(s):

K. S. Abdel Halim ◽

V. N. Andronov ◽

M. I. Nasr

Keyword(s):

Blast Furnace ◽

Natural Gas ◽

Gas Injection ◽

Flame Temperature ◽

Furnace Operation ◽

Blast Furnace Operation ◽

Theoretical Flame Temperature

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Performance analysis of hot metal temperature prediction in a blast furnace and expert suggestion system proposal using neural, statistical and fuzzy models

Metallurgical Research & Technology ◽

10.1051/metal/2021043 ◽

2021 ◽

Vol 118 (3) ◽

pp. 321

Author(s):

Erdoğan Bozkurt ◽

İlhami M. Orak ◽

Yasin Tunçkaya

Keyword(s):

Blast Furnace ◽

Mean Squared Error ◽

Arima Model ◽

Flame Temperature ◽

Metal Temperature ◽

Raw Material ◽

Coefficient Of Determination ◽

Percentage Error ◽

Chemical Compositions ◽

Hot Metal

Blast Furnace (BF) production methodology is one of the most complex process of iron & steel plants as it is dependent on multi-variable process inputs and disturbances to be modelled properly. Due to expensive investment costs, it is critical to operate a BF by reducing operational expenses, increasing the performance of raw material and fuel consumptions to optimize overall furnace efficiency and stability, also to maximize the lifetime. The chemical compositions and temperature of hot metal are important indicators while evaluating the operation, therefore, if the future values of hot metal temperature can be predicted in advance instead of subsequent measuring, then the BF staff can take earlier counteractions on several operational parameters such as coke to ore ratio, distribution matrix, oxygen enrichment rate, blast moisture rate, permeability, flame temperature, cold blast temperature, cold blast flow and pulverized coal injection rate, etc. to control the furnace optimally. In this study, Artificial Neural Networks (ANN) model is proposed combined with NARX (Nonlinear autoregressive exogenous model) time series approach to track and predict furnace hot metal temperature by selecting the most suitable process inputs and past values of hot metal temperatures using the real data which is collected from the BF operated in Turkey during 2 months of operation. Various data mining techniques are applied due to requirements of charge cycling and operating speed of the furnace which secures novelty and effectiveness of this study comparing previous articles. Furthermore, a statistical tool, Autoregressive Integrated Moving Average (ARIMA) model, is also executed for comparison. ANN prediction results of 0.92, 8.59 and 0.41 are found very satisfactory comparing ARIMA (1,1,1) model outputs of 0.73, 97.4 and 9.32 for R2 (Coefficient of determination), RMSE (Root mean squared error) and MAPE (Mean absolute percentage error) respectively. Consequently, an expert suggestion system is proposed using fuzzy if-then rules with 5 × 5 probability matrix design using the last predicted HMT value and the average of the last 5 HMT values to decide furnace’s warming or cooling movements state in mid-term and maintain the operational actions interactively in advance.

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Artificial Neural Network Model for Predict of Silicon Content in Hot Metal Blast Furnace

Materials Science Forum ◽

10.4028/www.scientific.net/msf.869.572 ◽

2016 ◽

Vol 869 ◽

pp. 572-577 ◽

Cited By ~ 9

Author(s):

Sayd Farage David ◽

Felipe Farage David ◽

M.L.P. Machado

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Blast Furnace ◽

Silicon Content ◽

Flame Temperature ◽

Reduction Process ◽

Added Value ◽

Hot Metal ◽

Operational Parameter ◽

Artificial Neural

The growing focus on the efficiency of the reduction process in blast furnace generates an alteration in the way they operate. This modifies the conditions of transfer of silicon for the hot metal and can cause problems in the added value of your product. To evaluate the changes of the operational parameters of the reduction on the conditions of transfer of silicon process a mathematical model based on artificial neural networks has been implemented. Through this model it was possible to predict the silicon content to determine the influence of each operational parameter. Artificial neural networks were able to predict the silicon content through parameters of the reduction in blast furnace process, and this was verified by the precision of this model. The ANN showed that Theoretical flame temperature, Pressure blow and Coke rate have a positive influence on the silicon content in hot metal, and the Hot metal rate is inversely proportional to the silicon content of the hot metal.

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