Burden distribution estimation in the blast furnace from stockrod and probe signals

Saxén, Henrik; Nikus, Mats; Hinnelä, Jan

doi:10.1002/srin.199805572

Cited by 16 publications

(10 citation statements)

References 3 publications

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“…Another reason is that the signals should be available at short sampling time, and such high-frequency data are seldom interesting in the analysis of the blast furnace process because of its inertia. In the present work 15) it was found that the stockrod signals should be sampled at least every two seconds in order to be able to provide accurate and useful information, especially in abnormal situations (such as slips).…”

Section: )mentioning

confidence: 98%

Neural Network Model of Burden Layer Formation Dynamics in the Blast Furnace.

Hinnelä¹,

Saxén

2001

ISIJ International

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A model of the thickness of burden layers in the ironmaking blast furnace is presented. Local layer thickness estimates are calculated on the basis of signals from stockrods that measure the burden (stock) level in the furnace. These estimates are used in developing a model for the relation between the layer thickness and variables such as stock level and movable armor settings. Because of the nonlinear dependence of the variables, the models are based on feedforward or recurrent neural networks. The network size is carefully selected based on a cross-validation procedure. The resulting neural model is first studied by analyzing its predictions for different inputs. By further introducing a simplified scheme for considering the practical constraints of the charging process, an autonomous model, where the neural network plays an important role, is formed. This hybrid model is applied to yield insight into the dynamics of the layer formation process; the effect of movable armor settings, stock level and burden descent rate are analyzed and compared with practical experience.KEY WORDS: blast furnace; burden distribution; neural network; hybrid model. signals from the stockrods (see Fig. 1), which measure the stock level close to the wall. Stockrod signals have been logged at high frequency and processed to provide burden layer thickness estimates. The relation between the layer thickness and variables affecting the burden distribution has been modeled by feedforward and recurrent neural networks. The study shows that the variation in the burden layer thickness can be described by the networks, given information about the stock level at the instant of charging and the movable armor position. However, the neural model requires information about the stock level, which is a variable that is affected by the layer thickness of the dumps, i.e., the output of the model. In order to resolve this dilemma, the network has been implemented in a hybrid model, which considers the effects of the descent of burden and practical constraints of the charging process. By this procedure, the dependence between the stock level prior to charging and the resulting layer thickness can be considered. The hybrid model can be used to simulate the effect of stock level set-point, movable armor positions and burden descent rate on the layer thicknesses. The results obtained conform well with observations from the Finnish blast furnace studied in this work and are also in general agreement with practical experience. MeasurementsThe stockrods in the blast furnace are sounding devices (as illustrated schematically in Fig. 1) that sense the burden level after each dump and are elevated before a new dump of burden is charged into the furnace. The main function of the rods is to measure the vertical position of the bed surface (stock level) in order to trigger the charging of the burden at an appropriate moment to maintain a stable stock level. In practice, charging is triggered when the rods have descended below a given set-point. From the stockrod ...

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Section: )mentioning

confidence: 98%

Neural Network Model of Burden Layer Formation Dynamics in the Blast Furnace.

Hinnelä¹,

Saxén

2001

ISIJ International

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“…There are basically only two regions where information can be obtained from inside the BF—the burden fill level at the top of the BF and the visual information of the raceway areas through the tuyere inspection windows. Information about the burden height can be derived from pointwise measurements via mechanical stockrod probes or microwave sensors, [ 12 ] 3D radar systems, [ 13,14 ] or, more recently, also the 3D reconstruction of the burden from optical sensors. [ 15,16 ] If the burden measurement system has a sufficient time resolution also the downward movement of the burden can be tracked.…”

Section: Introductionmentioning

confidence: 99%

Toward a Better Understanding of Blast Furnace Raceway Blockages

Puttinger

Stocker

2020

steel research int.

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Operational data of two small size blast furnaces (BFs) with respect to the occurrence of raceway blockages are analyzed. These blockages mainly occur due to erratic movements in the burden. In a previous project, the authors have investigated the various types of raceway blockages and tested different approaches to implement a reliable way to detect such blockages at a very early stage. Early detection of severe blockage events is important to avoid tuyere damages and trigger operational reactions like, e.g., the shutdown of pulverized coal injection (PCI) branches. The most promising algorithm of the previous project has been implemented in the process control system of the BFs and enhanced with the additional calculation of a strength factor serving as an indicator for the severity of a blockage event. The system is now collecting data on raceway blockages since the beginning of 2019. As there is now a good data basis available, herein, the latest findings on blockage event statistics and the correlation with other operational data of the BF are presented.

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“…To gain a better understanding of the gas distribution, horizontal or inclined probes have been applied above or below the burden surface. [3,11,12] These typically measure the temperatures and sometimes take gas samples to determine the gas composition at a number of discrete points along one radius. The in-burden probes also sample gas, while the above-burden probes usually only measure temperatures.…”

Section: Introductionmentioning

confidence: 99%

Numerical Study of Gas Flow and Temperature Patterns in the Blast Furnace Throat

Mondal

Liu

Bartusch

et al. 2022

Metall Mater Trans B

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The gas distribution in the shaft is of key importance for the performance of the blast furnace process, since it affects the way in which the burden is heated and reduced, and also the pressure drop in the shaft and cohesive zone. Traditionally, the gas distribution is followed by gas temperature measurements at several points over the radius or diagonal by the above-burden probes, but novel acoustic techniques can estimate the gas temperature over the full cross section of the throat. A fundamental problem is that the gas is redistributed in the upper bed and above it, so the measured profile may no longer reflect the conditions in the shaft. This paper studies the gas redistribution by a CFD model of the throat region, neglecting heat transfer to the bed and walls. It is demonstrated that strong redistribution and downward-swirling flows may occur, which affect the measurements. The arising conditions under different assumptions of the bed state are studied, and the dynamics of the changes are also briefly analyzed. A comparison of the findings of the computational model with acoustic measurements of the gas temperatures in the throat of two industrial blast furnaces reveals a good resemblance. The results of the study sheds light on the complex flow conditions of the gas in the blast furnace throat region and can be used to interpret information from measuring devices and to study how their positioning affects the measurements.

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Burden distribution estimation in the blast furnace from stockrod and probe signals

Cited by 16 publications

References 3 publications

Neural Network Model of Burden Layer Formation Dynamics in the Blast Furnace.

Neural Network Model of Burden Layer Formation Dynamics in the Blast Furnace.

Toward a Better Understanding of Blast Furnace Raceway Blockages

Numerical Study of Gas Flow and Temperature Patterns in the Blast Furnace Throat

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