Blast furnace hot metal temperature prediction, by mean of mathematical models, plays an interesting role in blast furnace control, helping plant operators to give a faster and more accurate answer to changes in blast furnace state. In this work, the development of parametric models based on neural networks is shown. Time has been included as an implicit variable to improve consistency. The model has been developed departing from actual plant data supplied by Aceralia from its steel works located in Gijón.KEY WORDS: ironmaking; blast furnace; neural networks; forecasting; simulation; hot metal temperature.coke rate, was the variables selected as input. The control effort exerted over some of these input variables by means of step changes is clearly observable. For example, the correction applied to blast moisture during the period comprised between the 16 and 32 h it is significant, probably with the objective of restraining the steady decrease of hot metal temperature. Clearly visible as well is the strong correlation between pulverised coal injection (PCI) and oxygen enrichment in the blast. Furthermore, it is possible to check the increase of ore/coke rate in the burden at the same time that PCI increases until it reaches its set point. Figure 2 shows a more detailed view for hot metal temperature during the same period. In it, the vertical lines and circles represent the beginning of tapping and the time when hot metal temperature was measured, respectively. It is important to emphasise some features of this variable, which are will determine the way in which the problem of its prediction will be addressed.First of all, it is obvious that neither the tapping rate nor the time when the tapping hole is open is regular. In addition, the number of temperature measurements taken during tapping times and when they are taken are irregular too. Both depend on the plant operator's decision according to their estimation of the state of the blast furnace.In general, between two and three hot metal temperature measurements are taken by cast. The first is taken shortly after piercing the tap hole, the second when slag starts to flow out and the third near the end of the cast. The process computer records the data when a new measurement is carried out, and repeats this value until the next measurement. This is the reason why the signal obtained for hot metal temperature evolves stepwise.These specific features must be taken into account for dynamic modelling purposes. The fact that the value of the variable that is intended to forecast can not be measured with a sampling rate equal to the inputs variables sampling rate, makes it necessary to pre-treat it before being introduced in the model, in order to obtain regularly distributed values. This can be done by means of interpolation among the data available.7) Another possibility is to include the time explicitly in the model. This latter was the approach employed in this work. Model DevelopmentThe structure chosen for the model can be considered as belonging to a class of...
During the realigning of Aceralia's blast furnace B in Gijon, a 1/10 scale half-section, three-dimensional cold model of the BF shaft was built to test charging patterns and the effect of gas flow in burden distribution. The front side of this half model is closed by a methacrylate sheet that allows images of the burden distribution inside the model to be obtained, during and after the charging process. Image processing techniques were applied to obtain useful information from burden profile images. A complete study of a charging pattern is presented in this paper.The model has allowed study of some local effects of gas flow in burden distribution, such as changes in the final coke layer during ore charging, and the formation of a narrow window in the centre of the model that connects the successive coke layers.
The mutual influence between gas flow and burden distribution in the upper part of a blast furnace has been studied employing a simplified mathematical model to estimate burden distribution and a gas flow model based on Ergun's equation.Experiments carried out on a 1/10 scale cold model of Aceralia's blast furnace B were compared with results obtained from these mathematical models. Results corroborate that changes in burden distribution during the charging process have a strong influence on gas distribution, which also affects the burden layer profiles. These interrelations should be taken into account when the blast furnace charging patterns are designed KEY WORDS: ironmaking; blast furnace; mathematical modelling; burden distribution; gas flow.obtained, a pair of second degree polynomials is calculated to describe the shape of the ring. Both of them have a maximum located at the contact point and are symmetrical with respect to this point, the first derivative at their turning point fits the tangent of the repose angle of the discharged material, and the revolution volume between them and the previous burden surface should fit the volume of discharged material.Successive rings define the layers of ore and coke. An example can be found in Fig. 1 showing the results obtained from this model for the charging pattern described in Tables 5 [r] and 6 [r].The descent of the burden along the blast furnace shaft was simulated employing a plug flow model.13) This allows ISIJ International, Vol. 44 (2004) Comparison between physical properties of coke employed in the actual blast furnace and coke employed in the scaled model of this study. Table 2. Comparison between physical properties of sinter employed in the actual blast furnace and sinter employed in the scaled model of this study. Table 3. Comparison between physical properties of pellets employed in the actual blast furnace and pellets employed in the scaled model of this study. Table 4. Relationship among gas flow condition for the actual blast furnace and the scaled model. the estimation of the burden layer after simulating its loading. The main features which taken into account by the descent model are the slope decrease and the progressive thickening of burden layers as they descend through the shaft.A version of the model just described is at present used in Aceralia to estimate the burden distribution inside the shafts of blast furnaces A and B on-line.A special version was developed to estimate the burden distribution in the 1/10 scale shaft model mentioned above. Tables 5 and 6 reproduce the charging pattern used at Aceralia (r) and the scaled charging pattern for the model (m).By mean of image acquisition and image processing techniques, it was possible to obtain the ring profiles once loaded into the physical scaled model. 10) Coke and ore were charged ring by ring taking pictures with a CCD camera of the layer profile after each dump. The images were stored in a hard disk in standard 'bmp' format. Quantitative information may be obtained from the ima...
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