Blast Furnace Analysis with Neural Networks

Transcription

1 Blast Furnace Analysis with Neural Networks Joachim Angstenberger MIT - Management Intelligenter Technologien GmbH Promenade Aachen, Germany Phone: Fax: jang@mitgmbh.de Abstract: Nowadays blast furnace operation is supervised by extensive measurements and controlled accordingly. Characteristic indications concerning process quality are given by the analysis of the radial temperature profile in the upper part of the furnace. Optimising this temperature distribution would lead to considerable savings of input material. To achieve an optimisation, quantitative relations between furnace parameters are needed. As those relationships are unknown, a process model can be provided using neural networks and fuzzy methods. In this paper we show the application of fuzzy clustering and neural networks to classify temperature profiles and to build a model of the interdependence between process operation parameters and the resulting temperature profiles. These investigations have been carried out in a plant of a German steel producer. Keywords: blast furnace, process analysis, fuzzy clustering, multilayer perceptron, identification of temperature profiles 1 Introduction During the blast furnace process, ferric oxides contained in the input materials ore and sinter are reduced and melted. The output material is liquid pig-iron. Figure 1 shows a schematic overview of this process. The furnace is charged from its top. The input materials are sinking downwards while gas is flowing upwards through the furnace. The melting heat is supplied by this gas stream and by coke (which is also charged from above). Although studies have been made about the internal operating conditions of the furnace [Omori, 1987], the blast furnace operating personnel relies mainly upon measurements at the inputs and outputs of the process. A wide range of measuring data is acquired at the blast furnace. Blast furnace engineers check Vertical probe Profile meter Layer thickness meter Lumpy zone Horizontal probe Fig. 1: Schematic view of the blast furnace Cohesive zone Dripping zone Belly probe Raceway Hearth

2 these data in order to find out if the change of a controllable furnace parameter is necessary. This inspection is done by expert knowledge, i.e. control of the blast furnace is based on the experience of the operating personnel. Quantitative knowledge of functional dependencies between furnace parameters is not absolutely required for that purpose.however, experience has shown that missing knowledge of quantitative functional interdependencies makes an optimised furnace operation almost impossible.the operation is considered to be optimal, if the fuel consumption (coke and coal) is minimised (cost savings) and the furnace shows a steady cycle without greater fluctuations of some parameters. One of these essential parameters is the radial temperature distribution in the upper part of the blast furnace. According to the experts' knowledge, this magnitude is the most important parameter to judge the condition of the furnace. The temperature profile should show a determined characteristic shape: Maximal value in the centre of the furnace, fall off reaching the wall with a minimum briefly after the half radius and again rising reaching the wall. Past has shown that at the considered blast furnace among all of these profiles one specific shape is always connected with the best furnace operation (in the above-mentioned sense). The question indeed is how to receive such a shape and how to maintain it in the long run? For that purpose the shape influencing quantities have to be known as well as the order of magnitude of their quantitative influence on the furnace process. Due to the large number of furnace parameters and their highly non-linear coupling the identification of these influencing factors is difficult, which thus also holds for the modelling of the process itself. As there is no possibility to generate an adequate physical model of the blast furnace, the relationship between these influencing parameters on one hand and the resulting temperature profile on the other hand were modelled using a neural network By interviewing blast furnace experts the characteristics of the input materials sinter (iron supply) and coke (energy supply) as well as the charging program were identified as the main influence factors on the temperature profile. The charging program determines the order and the amount of the input materials brought into the furnace, thereby producing a series of sinter and coke layers. The thickness and the structure of these layers (shown at the upper left side of figure 1 as different hatchings) have great influence on the rising gas streams in the furnace and therefore determine indirectly the measured temperature profile. The two objectives of this analysis were: 1. A classification of the temperature profiles with the objective to assess how much the current state of the furnace corresponds to determined quality criteria. 2. The determination of the quantitative dependency of the cross-sectional temperature profile on the decisive magnitudes that constitute this profile (i.e. modelling the blast furnace). These two parts of the analysis can later on be combined into a complete blast furnace control system. In a first step the classification of the temperature profile gives

3 information about the current state of the blast furnace. A suitable influencing of the profile can then be carried out using the furnace model. All examinations have been carried out with the help of the data analysis software DataEngine, which includes methods for fuzzy clustering as well as neural networks. Together with its data pre-processing, statistics and visualisation facilities this tool enables a complete solution of the whole task. 2 Classification of Temperature Profiles in the Blast Furnace by Fuzzy Clustering For splitting the temperature profiles into classes 143 data records, each containing a profile of 8 temperature values recorded previously have been used. At the beginning, the number of different classes of profiles included in the data material was unknown, so the optimum number of classes had to be determined first. For the purpose of classification the fuzzy c-means algorithm was used, a fuzzy clustering procedure that allows gradual association of the considered objects (here: temperature profiles) to the respective classes [Bezdek 1981]. The essential information this algorithm produces are the centres of the clusters found during clustering. Each cluster centre represents a (fictitious) sample that characterises the respective class best. Here, fuzzy clustering results in typical temperature profiles that can be interpreted as prototypes of the respective classes. To determine the optimal number of classes, cluster validity measures can be used [Windham 1981]. The analysis of the given data records leads to an optimal partition of five clusters, whose centres are displayed in the following diagrams (Figure 2). In each diagram the temperature is plotted against the measuring position (seen from the centre of the furnace). 8 Cla ss 1 8 Cla ss Te m p 4 Te m p Te m p Cla ss Te m p 4 2 Cla ss

4 Temp Class 5 Position 41 Fig. 2: Types of temperature profiles (classification into 5 classes) Using the criteria mentioned in the introduction above, it can be recognised that the profiles of classes 1, 2 and, with certain reservations, class 4 represent good furnace states, whereas classes 3 and 5 characterise undesired states of the furnace. The received class partition was validated by blast furnace experts. It turned out to represent a good characterisation of typical states of the furnace (cf. also [Bulsari, Saxen 1995]). 3 Modelling of the Dependency Between Temperature Profiles and Input Parameters by Neural Networks The available data records for modelling the blast furnace comprised the charging program, the distributions of the grain sizes of coke and sinter as well as several parameters of the material strength. As the grain spectrum of coke covers a relatively narrow area, the evaluation considered only the medium grain diameter. The distribution of the grain size of sinter plays an essential part for the furnace process, as it bears strongly upon the gas flow in the furnace. For that reason the distribution of the sinter grain size was used completely as an input parameter. Altogether 1 input parameters were used. Because of material transportation time and other effects the material parameters affect the temperature profile with a delay of approximately 24 hours. On the other hand, the charging program bears on the temperature profile with a delay of 8 hours due to process characteristics. An appropriate synchronisation of the data had to be carried out. After further pre-processing (scaling, etc.) these data were used as inputs of a multilayer perceptron. Some data records were extracted from the total amount of data in order to be used for validation of the network performance. The output of the network consists of 8 values modelling the temperature at the respective measurement positions of the measuring system. Figure 3 represents a schematic view of the neural network's structure.

5 Fig. 3: Schematic structure of the neural network for blast furnace modelling Several network configurations (number of hidden layers, number of hidden neurons, learning parameters) have been trained and tested with regard to their efficiency. The neural network is able to approximate the temperature profiles with good precision. This is proven by the comparisons shown in figure 4. The diagrams show the temperature profiles calculated by the neural network (indicated as "Network" in figure 4) as opposed to the actually measured profiles ("" in figure 4) for four data records. The cases shown in figure 4 have not been used for training the neural network. The neural network proved to be able to recognise relatively unusual temperature profiles as well (like the one displayed in figure 4 on the lower right) Fig. 4: Comparison of measured and calculated temperature profiles However, the test results made clear as well that there are certain situations where the neural network is not able to achieve a reasonable result. Therefore, additional examinations have to be performed to quantify the influence of several additional magnitudes that have not been considered yet. In particular, these are magnitudes influencing the gas flow in the furnace and more detailed information about the input material. With inclusion of this information modelling and optimisation of the blast furnace process can be largely improved.

6 4 Conclusions In this contribution modelling of a blast furnace by neural networks and fuzzy logic has been investigated. The neural network model achieved a high correlation between actual and estimated temperature profiles. Based on these results an improved process control with respect to input material savings and quality of the produced steel can be implemented. 5 References [Bezdek 1981] Bezdek, J. C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York London, [Bulsari, Saxen 1995] Bulsari, A., Saxen, B.: Classification of blast furnace probe temperatures using neural networks. Steel Research 66, 1995, No. 6, pp [Omori 1987] Omori, Y. (ed.): Blast Furnace Phenomena and Modelling. The Iron and Steel Institute of Japan. Elsevier, London, [Windham 1981] Windham, M.P.: Cluster Validity for Fuzzy Clustering Algorithms. Fuzzy Sets and Systems 5, 1981, pp