Microwave Burden Sensor for Blast Furnaces

Ohno, Jiro; Yashiro, Hirokatsu; Shirakawa, Yoshiyuki; Tsuda, and Akihiko; Watanabe, Satoshi; Hirata, Takeyuki; Higuchi, Muneyuki; Nakagome, Michiru

doi:10.1016/s1474-6670(17)59118-1

Cited by 2 publications

(2 citation statements)

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“…2) A variety of methods for measuring the burden distribution in operating blast furnaces have been proposed. The burden surface (stockline) can be detected by mechanical profile meters, 3) or by non-contact methods based on radar, microwaves 4) or laser, 5) infra-red and gamma radiation techniques. The thicknesses of burden layers (at the wall) can also be measured by magnetometer.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

Hinnelä¹,

Saxén

2001

ISIJ International

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“…In spite of the fact that a number of measuring devices have been developed for detecting the burden surface profile, [1][2][3][4][5] it is still difficult to obtain reasonable estimates of the realized burden distribution in operating blast furnaces. Especially in furnaces with bell-type charging equipment, the push effect exerted by heavier materials, such as sinter or pellets, makes the "transformation" of an observed burden surface profile into layer thicknesses unreliable.…”

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On-line Estimation of the Ore-to-coke Ratio in the Blast Furnace Center.

Saxén

Nikus

2002

ISIJ International

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The burden distribution in the blast furnace is of central importance for achieving an energy-efficient operation of the process. In spite of the fact that a number of measuring devices have been developed for detecting the burden surface profile, [1][2][3][4][5] it is still difficult to obtain reasonable estimates of the realized burden distribution in operating blast furnaces. Especially in furnaces with bell-type charging equipment, the push effect exerted by heavier materials, such as sinter or pellets, makes the "transformation" of an observed burden surface profile into layer thicknesses unreliable. Another problem is the possible penetration of pellets into previously charged coke layers. These facts are reasons why a number of models for indirect estimation of the burden distribution using "standard" measurements have been proposed. [6][7][8] This note briefly presents a method for on-line estimation of the ore-to-coke ratio in the center of the blast furnace. As the main source of information, frequent temperature measurements from an above-burden probe are used. The method is illustrated on data from a Finnish blast furnace.The proposed method bases its estimation on observations of temperature transients recorded by an above-burden probe, using an approximate equation for the thermal conditions in the upper shaft of the blast furnace as a function of the vertical coordinate, z. It is assumed that there is a region in the furnace shaft where the gas and burden take the same temperature, T R . The occurrence of a "thermal reserve zone" is generally accepted and is also supported by result of direct measurements. 9) For the sake of simplicity, the model is also based on the assumption that no radial transport of mass or heat, and no major chemical reactions occur in the furnace part considered, and the gas is assumed not to mix between the stockline and the probe. Finally, to further simplify the treatment, the heat capacities of the gas and the burden, c g and c s , are assumed to be constant. These assumptions, naturally, introduce some inaccuracies into the model, but they are used to make the model complexity manageable for on-line use.From an energy balance for the region between a position z and the thermal reserve zone, an expression for the ther-

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Microwave Burden Sensor for Blast Furnaces

Cited by 2 publications

References 1 publication

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

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

On-line Estimation of the Ore-to-coke Ratio in the Blast Furnace Center.

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