SICE Journal of Control, Measurement, and System Integration, Vol. 1, No. 3, pp. 199–206, May 2008
Development of an Operation Support System for the Blast Furnace in the Ironmaking Process: Large-scale Database-based Online Modeling and Integrated Simulators Harutoshi OGAI ∗ , Masatoshi OGAWA ∗ , Kenko UCHIDA ∗∗ , Shinroku MATSUZAKI ∗∗∗ , and Masahiro ITO ∗∗∗ Abstract : In the pig-ironmaking process, factors that cause operation malfunctions have increased with both the enlargement of the blast furnace and the increasing use of low quality ore. Therefore, an operation support system that predicts blast furnace performance is demanded. This paper reports the development of a blast furnace operation support system with an integrated simulator and “Large-scale database-based Online Modeling (LOM).” To develop the integrated simulator, a sophisticated burden distribution model is integrated with a two-dimensional total internal phenomenon model for the stationary state by using Java technology. Moreover, an integrated simulator for the partial non-stationary state is developed by modifying the two-dimensional total internal phenomenon model for the stationary state. To incorporate the LOM system into the operation support system, a cross-platform LOM system with general versatility is rebuilt by an existing LOM system. The operation support system is realized by the simulator of the physical modeling method and the LOM of the local modeling method. As a result, the operation support system predicts a dynamic molten pig-iron temperature in the blast furnace. The operation support system is expected to provide staff with useful information. Key Words : blast furnace, process simulator, LOM, partial non-stationary simulator, Java, steel industry
1. Introduction Recently, the blast furnace process in the ironmaking process has been upsized to supply a larger amount of hot metal. The radial distribution of furnace has a considerable impact on the operation. Therefore, the enlargement of radial in the furnace makes the operation difficult. The current quality in raw materials has deteriorated, in comparison to the past quality of raw materials. Cases occur where raw materials of an undesirable quality are used; thus, factors causing operation malfunction have increased with both the enlarging the blast furnace and increasing use of low quality ore. Operation support system predicting blast furnace performance is demanded. On the other hand, various mathematical models [1]–[6] as predictive technology have been developed. The mathematical models have played a significant role in predicting the real operation of the blast furnace. A simulator that analyzes and evaluates the non-stationary state of the blast furnace quantitatively is required to realize stable operation. However, with the development of computing machines and database systems in recent years, it has become possible to accumulate and retrieve a large amount of data at very high speeds. Attention has been drawn by the local modeling techniques of a new idea called “Just-In-Time (JIT) [7],[8] modeling” or “Lazy Learning” [9]. To apply “JIT modeling” to a large amount of databases online, “Large-scale database-based ∗
∗∗ ∗∗∗
Graduate School of Information, Production and Systems, Waseda University, Fukuoka 808-0135, Japan (Tel : +81-93692-5147) Department of Electrical Engineering and Bioscience, Waseda University, Tokyo 169-8555, Japan Nippon Steel Corporation, Chiba 293-8511, Japan E-mail:
[email protected] (Received January 20, 2008)
Online Modeling (LOM)” [10],[11] has been proposed. LOM is such a technique that makes the retrieval of “neighboring” data more efficient by using the “stepwise method” and quantization of topological space. The stepwise method decreases the dimension of multi-dimensional space of actual processes. In addition, the multi-dimensional space is quantized. The purpose of this study is to develop an operation support system for the blast furnace with an integrated simulator and a LOM. To develop the integrated simulator, a sophisticated mathematical model for the burden distribution is integrated with a two-dimensional, stationary mathematical model for the total internal phenomenon by using Java technology. Moreover, the integrated simulator for the partial non-stationary state is constructed by modifying the two-dimensional, stationary mathematical model. To incorporate the LOM system into the operation support system, a cross-platform LOM system with general versatility is rebuilt by an existing LOM system. The operation support system is realized by the simulator of the physical modeling method and the LOM of the local modeling method. The paper is structured as follows. Section 2 explains the integrated simulator for the blast furnace and development of the partial non-stationary simulator for the blast furnace. Section 3 introduces the cross-platform LOM system. Section 4 discusses the operation support system using the simulator and the LOM. Finally, Sect. 5 summarizes the main conclusions.
2. The Integrated Simulator for Blast Furnace A Java-based integrated simulator for the blast furnace is effectually created by integrating existing partial simulators. The integrated simulator for blast furnace is developed by settling compatibility issues in sub-models with Java technology. Re-
c 2008 SICE JCMSI 0003/08/0103–0199
200
SICE JCMSI, Vol. 1, No. 3, May 2008
mote Method Invocation (RMI) and Java Native Interface (JNI) of Java technology are applied to integrate an existing RABIT model [4],[5] and an existing BRIGHT model [1],[2] in the blast furnace. The RABIT model is a burden distribution model in the blast furnace, while, the BRIGHT is a total internal phenomenon model. JNI is a structure to execute a native code, a C code or a C++ code, from a Java code. JNI can integrate those simulators of different program languages. RMI is a structure to provide an environment to use distributed objects in Java. RMI can integrate those partial simulators in a different computer environment. Jni4FCB [12] is a useful tool to access variables of Fortran from a Java program. The schematic diagram for integrating those partial simulators is shown in Fig. 1. The Java integrator calls distributed objects in a different computer environment by RMI. The distributed object accesses the model of Fortran through the C code by JNI. Next, the blast furnace, the mathematical models, and Java technologies are described. Furthermore, the processing flow of the Java-based blast furnace simulator is described.
Fig. 1
The schematic diagram for integrating BRIGHT and RABIT by JNI & RMI.
2.1 Blast Furnace The blast furnace [13] is a complicated process, because three phases of gas, liquid, and solid in a container interact together. The blast furnace is a moving-layer type counter-flow reaction vessel as shown in Fig. 2 [1],[2]. Iron ore and coke, both in grains, are charged from the top one after the other so as to form a pile of alternating layers. Hot air is blown in through blast injection nozzles (tuyere) provided at its lower portion and the hot air makes the coke burning generate high-temperature reduction gas; then iron oxide in the iron ore is reduced and melted into molten pig iron by the high-temperature reduction gas. The keys for operation of the blast furnace are that the solid component is dropped, the gas component is raised, and the heat is maintained at a constant temperature. It is required that the operation support system can predict the process state and understand the process characteristics. 2.2 Mathematical Models for the Blast Furnace 2.2.1 RABIT model (Radial Burden distribution Index Theoretical model) The RABIT model [4],[5] is a burden distribution model of the blast furnace. The RABIT is a model for considering the influence by in-furnace gas flow, falling motion and depositional process of charge. The feature of the RABIT model is formulated by knowledge based on the model experiment regarding the influence that is given by the phenomenon of in-furnace gas flow, coke layer collapse, and the falling motion of charge.
Fig. 2
Outline of the blast furnace process.
An algorithm of the RABIT model consists of the following 6 steps, “calculation preparation,” “burden shape and gain size distribution,” “coke layer collapse calculation,” “gas flow distribution calculation,” “criterion of convergence,” and “output of calculated data.” The “burden shape and grain size distribution calculation” and “coke layer collapse calculation” are important steps in this calculation. 2.2.2 BRIGHT model (Blast furnace Realization for the Instruction Guide by Hybrid Theory model) The BRIGHT model [1],[2] is used to construct the partial non-stationary simulator. The BRIGHT model is a 2dimensional mathematical model of the blast furnace as shown in Fig. 3 [1],[2]. The BRIGHT model consists of five partial models. The five partial models are burden distribution, gas flow, solid flow, chemical reaction, and heat transfer. The model with integrated five partial models estimates a cohesive zone shape. The calculated data are to be converged when the average value of the change in the shape of the cohesive zone falls within 0.5 m.
Fig. 3
Configuration of the BRIGHT model.
2.3 Jni4FCB (JNI for Fortran Common Block) A Java program accesses common block variables of Fortran through the C structure corresponding to the Java class. The tool that enables Java program to access the Fortran code
SICE JCMSI, Vol. 1, No. 3, May 2008
is called Jni4FCB [12] (JNI for Fortran Common Block). Jni4FCB is a useful support tool. By using the Jni4FCB, a Java program can access common block variables of Fortran. Furthermore, the Jni4FCB generates automatically interfaced programs in order to access common block variables from Java. If common block variables are declared, most of those interface programs are created. 2.4 Processing Flow in Java-based Blast Furnace Simulator A processing flow in simulation by the Java-based blast furnace simulator is shown in Fig. 4. The RABIT subroutine of Fortran is calculated through C codes from the Java main method by using JNI. Similarly, the BRIGHT subroutine is calculated through C codes by using calculated data of the RABIT subroutine. The calculated data of the integrated simulator are stored in the database. Moreover, the BRIGHT subroutine and the RABIT subroutine can calculate in those private machines by using RMI. The processing flow is shown in Fig. 5.
Fig. 4
201
furnace process dynamics by investigating influences based on control means of the tuyere on the partial non-stationary model. The partial non-stationary simulator is constructed by partially changing the stationary simulator of the BRIGHT model. The partial non-stationary simulator assumes that variables changing extremely quickly in the blast furnace are stationary ones while variables changing slowly in the blast furnace are non-stationary ones. For example, the gas, a variable that changes extremely quickly, arrives in the throat from the tuyere in a few seconds, while the solid, a variable that changes slowly, arrives in the tap hole from the throat in about 8 hours. Therefore, the gas heat transfer model is assumed as being stationary, while the solid heat transfer model is assumed as being non-stationary, because the simulator requires a decrease of the computational complexity to support the operation. The solid heat transfer model is a significant model in the final step of the blast furnace model. To develop the partial non-stationary simulator, the Fortranbased stationary model is integrated with the non-stationary model. Here, JNI and Jni4FCB are used to construct the simulator. The processing flow of the simulator is shown in Fig. 6. First, the stationary solution is derived by the stationary model. Secondly, the partial non-stationary model is calculated. The simulator can consider manipulated variables of the blast rate, the pulverized coal, the oxygen enrichment, the blast humidity, the blast pressure and the blast temperature. When the manipulated variables are changed, the functions for pulverized coal injection are calculated.
The processing flow of the integrating simulator.
Fig. 6 The processing flow of the partial non-stationary simulator for the blast furnace.
Fig. 5
The processing flow of the integrating simulator by using RMI.
2.5 Development of Partial Non-stationary Simulator The blast furnace is controlled by two points of operation at the top of the throat and under the tuyere. In the throat, the charge ratio of ore to coke, some qualities (particle size and strength), the charge of raw materials and the like are controlled. In the tuyere, the pulverized coal, the blast rate, the oxygen enrichment and the like are controlled. Here we examine the partial non-stationary simulator that can evaluate the blast
The solid heat transfer model for the non-stationary state in the partial non-stationary model is calculated. The solid heat transfer model for the non-stationary state is expressed by the following equation. dCS ∂T S (1 − ε)ρS CS + T S dT S ∂t dCS ∂T S ∂T S GSr = − CS + T S + GSz dT S ∂r ∂z ⎧ 2 ⎪ 2 ⎪ ∂ T S 1 ∂T S ∂ker ∂T S ⎨ + +⎪ k + ⎪ ⎩ er ∂r2 r ∂r ∂T S ∂r
202
SICE JCMSI, Vol. 1, No. 3, May 2008
2 ⎫ ⎪ ∂2 T S ∂kez ∂T S ⎪ ⎬ +kez 2 + ⎪ ⎭ ∂T S ∂z ⎪ ∂z
−Qr η + ha T g − T S
(1)
Where, GSr is the solid mass velocity in radial component, GSz is the solid mass velocity in height component, CS is the solid specific heat, ker is the solid thermal conductivity in the radial component, kez is the solid thermal conductivity in the height component, T S is the solid temperature, T g is the gas temperature, ε is the porosity in packed bed, ρS is the solid density, ha is the heat-transfer coefficient, η is the thermal efficiency, and Qr is the reaction calorie. The accumulation rate term of the left-hand side is added to the stationary model. The first term of the right-hand side implies the velocity based on the solid flow. The second term of the right-hand side implies the heat-transfer velocity based on the temperature gradient. The third term of the right-hand side implies the endothermic or the exothermic rate by heat of the reaction, heat-transfer velocity from another phase, and heattransfer velocity to another phase. The non-stationary simulator also similarly uses the Successive Over-Relaxation (SOR) method, because the fundamental BRIGHT simulator obtains the solution by using the SOR method.
3.
Cross-Platform LOM System
Fig. 7 Schematic diagram of Just-in-Time modelling.
This section explains the JIT modeling as basic conception of “Large-scale database-based Online Modeling (LOM).” Secondly, the LOM is introduced. Finally, a cross-platform LOM system is explained. 3.1 JIT Modelling [7],[8] An objects process is a nonlinear dynamic system, and characteristics of the system are given by a regression model expressed in the following equation (2). y(t + p) = f {y(t), y(t − 1), Λ, y(t − ny ), u(t − d), u(t − d − 1), Λ, u(t − d − nu )}
For example, when it becomes necessary to estimate a system state at time t, the present system state {(xkq , ykq )} is defined as the demand point. Neighboring data {(xki , yki )} (ki < kq ) similar to the demand point as past observed process data in the database are selected. When a number of data sets are obtained, a local model to interpolate output of the dataset will be constructed. By using the local model, the system output vector yki is estimated. Then, the local model is discarded and neighboring data sets in measured data sets updated database are selected in the next estimation. The schematic diagram of Just-In-Time modeling is shown in Fig. 7.
(2)
Where, u(t) is the control input vector of system at time t, y(t) is the observational output vector of system at time t, nu is the order of control input vector, ny is the order of observational output vector, p is the estimated time (or the predicted time) d is the time delay, f is the unknown nonlinear function Furthermore, the system input vector xk and the system output vector yk are redefined as the following equation (3) and (4). yk = y(k + p)
(3)
xk = {y(k), y(k − 1), Λ, y(k − ny ), u(k − d), u(k − d − 1), Λ, u(k − d − nu )}
(4)
As time passes, a large number of data, composed of the system input vector xk , the system output vector yk , for example (x1 , y1 ), (x2 , y2 ), . . ., are stored in the system as a data sets {(xk , yk )}, (k = 1, 2, Λ, ), Where k is the discrete time. Then, JIT modeling is to find out the nonlinear function f from the stored data sets {(xk , yk )} whenever they are required to be estimated (or predicted, controlled).
3.2 Outline of LOM The “Large-scale database-based Online Modeling (LOM)” [10],[11] makes the retrieval of neighboring data more efficient by using the “stepwise method” and quantization. The stepwise method decreases the dimension of multi-dimensional space of the actual process. In addition, the multi-dimensional space is quantized. The schematic diagram of the LOM is shown in Fig. 8 [10],[11]. The stepwise method is a technique that adds and deletes input variables by statistical testing to decrease input variables within the bound enough for practical use in the regression model.
Fig. 8 The schematic diagram of LOM.
3.3 Quantization of Topological Space and Searching the “Neighbour” At first, by defining quantized space X k as follows, a vector input variable xk is classified.
X k = Z xki , (i = 1, 2, . . . , n) (5)
SICE JCMSI, Vol. 1, No. 3, May 2008
203
Where, Z (·) is the quantizing operator, n is the number of the data that belongs to the same quantized space X k . Second, It is shown as follows to define a similarity S (ki , k j ) between the quantized space X ki andX k j . S (k , k ) = X ki − X k j (6) i
j
∞
Where, ·∞ is an infinite norm. Then, it is assigned that quantized space X kq includes demand vector xkq . A neighboring space Ωq of the vector xkq is defined as follows.
Ωq = X k p S kq , k p = min S kq , k p (7) X kp ∈T Where, T is a set of topological space. By applying the quantization, similarity S (ki , k j ) is defined as a discrete value. The neighboring data are retrieved simply and effectively by quantizing their distances with the demand point, at first within the shortest distance zone, then within farther and farther distance zones. The several ways of determining the quantum’s width are proposed. In this paper, a uniform equalized way, or the simplest way, is adopted.
Fig. 9 Processing flow of cross-platform LOM system.
3.4 Local Model In representative local model of JIT modeling, Locally Weighted Averaging (LWA), Locally Weighted Regression (LWR) and etc. have been proposed. In this paper, the simplest averaging way is adopted. In other words, the estimated system output vector yˆ kq is calculated as follows. 1 yˆ kq = F(X kq ) = yk (8) M k k k
Fig. 10 The environment of existing LOM and cross-platform LOM system.
y ;(x ,y )∈Ωq
Where, M is the number of the system output vector yk that belongs to the neighboring space Ωq . 3.5 Cross-platform LOM System The cross-platform LOM system is realized by implementing all processes of the LOM using Java and the versatile database. The processing flow is shown in Fig. 9 and the following. The processing flow consists of the process updating measured data and the prediction process. By using the versatile database and Java, large-scale measured data can be efficiently managed with expanding the versatility of the system. The system environment in the existing LOM system and the cross-platform LOM system is shown in Fig. 10. The cross-platform LOM system is used in different computer environments. Moreover, the management of largescale measured data is unified by the versatile database. Thus, the future state of the object system based on the updated measured data in all computers can be estimated by updating the versatile database. 3.6 Example of Prediction Result A result of the molten pig iron temperature predicted by cross-platform LOM system is described. There are 15 data items of a high contribution ratio for the molten pig iron temperature selected by the stepwise method. The sampling time is 1 hour. The number of data set is 3,622 points. Quantized number for the retrieved space of database is 20. For example, the No.1 demand point is selected. The temperature after 6 hours is predicted by using the past similar
Fig. 11
Actual molten iron temperature of the past similar data.
data. The temperature results of past similar data retrieved from neighboring data sets of the demand point are shown in Fig. 11. The estimated value is calculated by using the temperature results of past similar data as equation (8). The estimated value and the actual value are shown in Fig. 12. The horizontal axis of a graph represents time from the past 6 hours to the future 6 hours and the position of zero is the demand time. The vertical axis is the molten pig iron temperature. The past data similar to the present operation state is obtained in 6 places as shown in Fig. 10. It is confirmed that the estimated value is similar to the actual value in Fig. 12. The predictive accuracy of the molten iron temperature is evaluated by the scatter diagram between the estimated molten iron temperature by the cross-platform LOM system and the actual molten iron temperature in the 1,000 data items as the de-
204
SICE JCMSI, Vol. 1, No. 3, May 2008
Fig. 12 Actual and estimated molten iron temperature.
Considering the features of the simulator and LOM, the molten pig iron temperature of an important indicator for the operation is selected as the predictive object. The temperature can be estimated by both the simulator and LOM. First, the future variation of the molten pig iron temperature is predicted by using LOM. Secondly, the influence of manipulated variables is investigated by changing manipulated variables of the high contribution ratio for the molten pig iron temperature in the partial non-stationary simulator. Therefore, an operation support system that provides the dynamic trend of the temperature is proposed. The schematic diagram of the operation support system is shown in Fig. 14. The simulator and the cross-platform LOM system are controlled by the management system of the operation support system. For example, engineers or operators are provided with the prediction results by the LOM and the simulator.
Fig. 13 Scatter diagram of actual values and estimated values after 1 hour. Fig. 14 Schematic diagram of the integrated operation support system.
mand point from all the 3,622 data sets. The scatter diagram of actual values and estimated values after 1 hour from the present time by LOM is shown in Fig. 13. The correlation coefficient is 0.7835. Therefore, it confirmed that the cross-platform LOM system can predict the temperature adequately.
4. Operation Support System To support the blast furnace operation, both the simulator and LOM are used for the future prediction. First, the feature of the blast furnace simulator is shown as the following.
Moreover, the processing flow for setting the manipulated variables in the tuyere is shown in Fig. 15. When the pulverized coal ratio (PCR) is changed, the system calculates the processing flow automatically so that a theoretical combustion temperature in the tuyere may remain constant and calculate the blast rate in a manner which is consistent with the heat balance. Here, the PCR is the rate of pulverized coal to daily production.
(1) The operation condition is necessary. (2) The internal phenomenon and the measured data can be predicted. (3) Phenomena that have not been caused in the past can be predicted. (4) The dynamics can be predicted by the non-stationary model. Secondly, the feature of the LOM is shown as the following. (a) The accumulating a large amount of operation data and the measured data is necessary. (b) The measured data can be predicted. (c) Phenomena that have not been caused in the past can be not predicted.
Fig. 15 Flow for setting the manipulated variable.
An application example is described. Here, a demand point is selected. First, a calculated result of the molten pig iron temperature of the cross-platform LOM system is described. Here, conditions similar to section 3 are used. The actual value and the estimated
205
SICE JCMSI, Vol. 1, No. 3, May 2008
value are shown in Fig. 16. It is confirmed that the estimated value is similar to the actual value. Thus, when the manipulated variable isn’t changed, the temperature will change as shown in Fig. 16. Secondly, calculated results of the partial non-stationary simulator are described. The non-stationary calculation for 3 hours from the demand time is performed. The influence of manipulated variables is investigated four times by changing the manipulated variables of the high contribution ratio for the molten pig iron temperature at 1 hour after demand time. Here, three variables are manipulated; the blast rate, the PCR and the oxygen enrichment. Four cases are shown as the following. (Case 1) Change PCR 115 kg/t into 250. (Case 2) Change PCR 115 kg/t into 200. (Case 3) Change PCR 115 kg/t into 150. (Case 4) Maintain PCR 115 kg/t.
5.
Conclusion
The development of an operation support system for blast furnaces with an integrated simulator and a LOM has been described in this paper. To develop the integrated simulator, a sophisticated mathematical model for the burden distribution is integrated with a two-dimensional, stationary mathematical model for the total internal phenomena by using Java technology. Moreover, an integrated simulator for the partial non-stationary state is constructed by modifying the total phenomena two-dimensional mathematical model for the stationary state. To incorporate the LOM system into the operation support system, a cross-platform LOM system with general versatility is rebuilt. As a result, the operation support system has been realized by the simulator for the physical modeling method and the LOM for the local modeling method. It is confirmed that the system aids us to understand the process dynamics. The system is expected to provide staff with useful information. References
The results in those cases are shown in Fig. 17. The horizontal axis of the graph represents the time from the demand time to 3 hours in the future and the position of zero represents the demand time. In the actual operation PCR 115 kg/t is maintained. Consequently, the actual value is increased to about 1560 C◦ for 3 hours after the demand time. The first case, the second case and third case show that the temperature is significantly increased above 1600 C◦ for 2 hours after the demand time. As PCR is increased, the temperature is increased. The forth case shows that the temperature is maintained. By the partial nonstationary simulator, the upward tendency of molten pig-iron temperature was calculated. Therefore, it is confirmed that the system aids us to understand the process dynamics.
Fig. 16 Actual and estimated molten iron temperature.
Fig. 17 Calculation results by the partial non-stationary simulator.
[1] T. Sugiyama and M. Sugata: Nippon Steel Technical Report, No.325, pp.34-42, 1987. [2] T. Sugiyama: Nishiyama Memorial Technically Lecture Series, ISIJ, No.94&95, pp.137-173, 1983. [3] Kuwabara M. Takane S. Sekido K. Muchi I.: Mathematical Two-dimensional Model of the Blast Furnace Process, Tetsuto-Hagane, ISIJ, Vol.77, No.10, pp.1593-1600, 1991. [4] Y. Okuno, K. Kunitomo, T. Irita, and S. Matsuzaki: Tetsu-toHagane, ISIJ, Vol.7, pp.783-790, 1986. [5] Y. Okuno, S. Matsuzaki, K. Kunitomo, T. Isoyama, and Y. Kusano: Tetsu-to-Hagane, ISIJ, Vol.1, pp.91-98, 1987. [6] M. Naito and T. Nishimura: Prediction of Sinter Reduction Behavior in a Blast Furnace by a Mathematical Model, Proc. Asia Steel International Conference, B (Iron making), pp.268-276, 2000. [7] A. Stenman, F. Gustafsson, and L. Ljung: Just In Time Models for Dynamical Systems, Proc. 35th Conf. IEEE Decision and Control, pp.1115-1120, 1996. [8] S. Ushida and H. Kimura: Just-In-Time Approach to Nonlinear Identification and control, Journal of The Society of Instrument and Control Engineers, Vol.44, No.2, pp.102-106, 2005. [9] G. Bontempi, H. Bersini, and M. Birattari: The Local Paradigm for Modeling and Control from Neuro-fuzzy to Lazy Learning, Fuzzy Sets and Systems, IFSA, Vol.121, No.1, pp.59-72, 2001. [10] M. Ito, S. Matsuzaki, N. Odate, K. Uchida, H. Ogai, and K. Akizuki: Large Scale Database Online Modeling for Blast Furnace, Proc. 2004 IEEE CCA, pp.906-911, 2004. [11] M. Ito, S. Matsuzaki, H. Ogai, N. Odate, K. Uchida, S. Saito, and N. Sasaki: Large Scale Database-based Online Modeling Blast Furnace Operation, Tetsu-to-Hagane, ISIJ, Vol.90, No.11, pp.59-66, 2004. [12] M. Koga, M. Ogawa, H. Ogai, M. Ito, K. Uchida, and S. Matsuzaki: Integrating Java-Based Simulator for Blast Furnace, Proc. 16th IFAC World Congress, CD-ROM, 2005. [13] M. Ito, S. Matsuzaki, K. Kakiuchi, M. Isobe, T. Ishida, A. Fuji, and M. Naito: Development of the Visual Information Technique of Blast Furnace Process Data, Proc. 2004 IEEE CCA, pp.884-889, 2004.
206
SICE JCMSI, Vol. 1, No. 3, May 2008
Harutoshi OGAI (Member) Worked at Nippon Steel Co. from 1976 to 2003. He received a Dr. Eng. degree in control engineering from the Tokyo Institute of Technology in 1996, and is presently a professor of Waseda University in the Graduate School of Information, Production and Systems. His research interests include control engineering and its applications. He is a member of IEEE, IEEJ, and the ISIJ.
Masatoshi OGAWA (Student Member) Received an M.E. degree from the Graduate School of Information, Production and Systems of Waseda University, Japan, in 2005. He joined the Graduate School of Information, Production and Systems of Waseda University, Japan, as a Ph.D. student. His current research interests are in modeling, simulation and control for processes. He is a member of ISIJ, IEEJ and ISCIE (Japan).
Kenko UCHIDA (Member) Received B.S., M.S. and Dr. Eng. degrees in Electrical Engineering from Waseda University, Tokyo, Japan in 1971, 1973 and 1976, respectively. He is now a professor in the Department of Electrical Engineering and Bioscience, Waseda University. His research interests are in robust/adaptive/optimization control and time-delay systems. He is a member of IEEE, ISIJ, IEEJ and ISCIE (Japan).
Shinroku MATSUZAKI Received a B.S. degree from the Department of Metallurgical Engineering of Tokyo University, Japan, in 1981. He has been working at Nippon Steel Co. since 1981. He received a Dr. Eng. degree from Tohoku University, Japan in 2003. His current research interests are in the simulation, modeling and control of blast furnaces. He is a member of ISIJ.
Masahiro ITO Received an M.E. degree from the Graduate School of Science and Technology of Waseda University, Japan, in 1991. He has been working at Nippon Steel Co. since 1991. His current research interests are in the modeling, system development and design of control systems for heat processes. He is a member of ISIJ and the Japan Society of Mechanical Engineers.
