The burden distribution in the blast furnace is estimated on the basis of measurements provided by the stockrods in combination with information about changes in the gas temperatures measured by an above‐burden probe after each dump of burden. The former measurements yield an estimate of the local layer thickness close to the wall while the latter ones are used to evaluate the layer thickness in the center of the furnace. The layer thickness values estimated by the method and values computed on the basis of geometrical considerations under simplifying assumptions were found to show good agreement. The results hold promise for a successful on‐line estimation of the burden distribution in an operating blast furnace.
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-
In this paper a model for on‐line estimation of the radial gas distribution in blast furnaces is presented. The model is based on molar and energy flow balances for the blast furnace top, using the top gas temperature and gas temperature measurements from a fixed above‐burden probe. The radial distribution of the gas flux in the upper shaft is estimated by means of a Kaiman filter. Using measurement data from a Finnish blast furnace, the method is illustrated to capture short‐term dynamics and to detect sudden (larger) changes in the gas distribution.
The objective of this work is to study the capabilities of support vector machines for approximating the complex control law arising from model predictive control of a hybrid MIMO-system. By approximating the control law, an explicit formulation can be obtained, which is computationally less intensive for on-line use. The explicit model predictive control approach is applied to a simulated hybrid system consisting of two tanks in series. The system has real-valued, integer-valued, and binary-valued control inputs. A model predictive controller is first designed to control the entire system. This controller is then approximated using support vector machines, with separate approximators for each control input. Reasonable control results were achieved with the approximators.
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