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

Abstract

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 ...

Evolutionary Neural Network Modeling of Blast Furnace Burden Distribution

“…[23] Such a hybrid model can consider practical constraints of the charging process (e.g., the minimum time between the dumps). The stock level, in turn, can be simulated by solving a differential equation for the descent of the stock, with the effect of intermittent charging of burden layers predicted by the network.…”

Model-Based Analysis of Factors Affecting the Burden Layer Structure in the Blast Furnace Shaft

“…Compared with physical experiments, mathematical modeling [3] has gained popularity due to its lower cost and higher flexibility. The mathematical modeling approaches [4,5] regarding the BF burden distribution (corresponding to the bell-less charging system) can basically be categorized as volume-based simplified models or classical (continuum) force models [6][7][8][9][10][11][12][13][14], data-driven models [15,16], or hybrid models [17,18], as well as the more computationally expensive models based of the discrete element method (DEM) [19][20][21][22][23]. Among these models, the classical force model has advantages in terms of its simple model formulation and fast computation, which are readily suitable for online implementation.…”

Radar Detection-based Modeling in a Blast Furnace: a Prediction Model of Burden Surface Shape after Charging

“…To date, existing prediction models can be classified into empirical models, 3) classical force models, [4][5][6][7][8][9] neural network models 10) or models using the discrete element method (DEM). 11) Among these types of models, classical force models outperform the others in real-time online calculation scenarios, because they offer higher prediction accuracy than do empirical models; the ability to predict the entire geometric shape, unlike neural network models; and moderate computational complexities for real-time online prediction comparing with those associated with DEM models.…”