IEEE Instrumentation and Measurement Technology Conference Budapest, Hungary, May 21-23,2001.

Hybrid-Neural Modeling of a Complex Industrial Process

P. Berenyi, G. Horvath, B. Pataki, Gy. Strausz Budapest Uiniversity of Technology and Economics,

Dept. of Measurement and Information Systems Miiegyetlem rkp. 9 Budapest, Hungary H-1521 Phone:+36 1 463 2057, Fax:+36 1 463 41 12

Email: (berenyi, horvath, pataki, strausz} @mit.bme.hu URL: http://www.mit.bme.hu/eng/department/mit/researcMldl

Abstract; This paper deals with a complex industrial modeling problem the modeling of a Linz-Donawitz steel converter. The main purpose of the paper is to show that in suchr cases where classical modeling methods cannot be applied successfully and where the nature of knowledge available is heterogeneous hybrid intelligent approach can give new possibilities. The proposed hy- brid advisory system is composed of different neural networks and rule-based systems exploiting the advantages of bohk approaches. The paper describes the main features of the modeling task, lists the most serious d@iculties of this industrial problem and presents the motivaiions behind the construction of hybrid solution. At the end it gives details about the architecture of the pnoposed system and an overview about the results achieved Kevwords: Hybrid-neural modeling, data preprocessing, industrial process, steel indusiry.

I. INTRODUCTION

In industry many complex modeling or control problems can be found where exact or even approximate theoreti- caymathematical relationship between input anld output can- not be formulated. The reasons behind this can be the unsatis- factory knowledge we have about the basic underlying physi- cal behavior, chemical reactions, etc., or the high complexity of the input-output relationship. In these cases, experimental black-box model can be constructed if many input - output data are available. Black-box modeling based on measure- ment data raises many general questions. This paper deals with some of them using the experience gained1 from a com- plex real-life modeling problem: modeling of a Linz- Donawitz (LD) steel converter.

Steelmaking with an LD converter is a complex physico- chemical process where many variables have influences on the quality of the resulted steel. The complexity of the whole process and the fact that there are many effects that cannot be taken into consideration make this task difficult. The main features of the process are the followings: a large (-150-ton) converter is filled with waste iron (-30 tons), molten pig iron (- 110 tons) and many additives, then this fluid compound is blasted through with pure oxygen to oxidize the unwanted contamination (e.g. silicon, most of the carbon, etc.).

At the end of the oxygen blowing the quality of the steel is tested and its temperature is measured. If the main parameters are within the acceptable and rather narrow range, the whole process is finished and the steel and the slag is tapped off for further processing.

The quality of the steel are influenced by many parameters, however the amount of oxygen used during blasting is the main parameter that can be controlled to obtain predeter- mined quality steel. It is an important and rather hard task to create a reliable predictor for determining the necessary amount of oxygen. To give a reliable prediction we have to know the relation between the input and the output parame- ters of the process, therefore we have to build a model of the steel production process. The inputs of the model are formed by all available data of a charge (mass, temperature and the quality parameters of the pig iron and the waste iron, the mass and some quality parameters of all additives, as well as the amount of oxygen used during the blasting process, etc.). The outputs are the most important parameters of the steel pro- duced, namely the temperature and the carbon content at the end of the blasting. By the technology under study to gain steel with the predetermined carbon content is an easier task, than to reach the final temperature within the acceptable range. This is why during modeling the output carbon content was not taken into consideration and the developed model had only one output parameter, the final temperature of the steel. Using this model of the process - we call it temperature model - we can build another one that we call oxygen model. From the point of view of the required amount of oxygen this model can be regarded as the inverse model of the process. (Fig. 1).

Converter modeling is a really hard task where many different approaches have been applied so far. The experience - at least at the analyzed steel converter - shows that the classical mod- eling approaches e.g. using heat- and mass-balance [l], or Kalman filtering [2] etc. do not give acceptable quality re- sults. The very reason behind this is the lack of satisfactory relevant information. In our case the most important part of

This work was partly sponsored by the Hungarian Fund for Scientific Research (OTKA) under contract T023868.

0-7803-6646-8/01/$10.00 02001 IEEE

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the knowledge about the process is the relatively large data- base that contains information about a few thousand previous blastings. One of the best ways to utilize these data is to apply neural networks. As neural networks are universal approxi- mators they are general tools to build complex, non-linear mappings between inputs and outputs, to build black-box models [3].

In steel industry there are many applications of neural net- works or hybrid information systems (e.g. [4], [5]), however, according to the knowledge of the authors, the application of hybrid-neural system for LD converter modeling is a new approach.

Measured temperature Parameters

Oxygen Plant

‘Neural

traih?g -b plank model

‘.

l b I Copy of the plant model

(fixed)

Model output temperature

Fig. 1 . The role of the temperature (forward) and the oxygen (inverse) model

The work we have been working on for a couple of years is to develop an advisory system for oxygen prediction using dif- ferent (soft computing: expert and neural, classical mathe- matical) methods. The reason behind the need of a combined approach is that a model with required accuracy and reliabil- ity can only be built with the utilization of all achievable in-

formation. The main part of the knowledge is in the form of a database of input-output data vectors. The available symbolic knowledge (rules) comes from the staff of the steel factory. The third type of knowledge is represented in exact mathe- matical equations and is based on the theoretical background of this complex metallurgical process.

The paper overviews the main steps of this modeling task and details the motivations behind the use of hybrid intelligent approach. At the end it shows the structure of the developed system and concludes some general experience obtained through the whole project.

TT. THE POSSIBTLTTIES FOR MODELING THE INDUSTRIAL PROCESS

There were several possibilities to develop an advisoly system for this type of steelmaking process. First a theoretical model was built [ 1,6]; this model was based on the physico-chemical laws known in metallurgy, and resulted in precise mathemati- cal equations. Unfortunately such theoretical information can be obtained only for some aspects of the whole process and could be directly used only in ideal situations, which are far fkom the real practical ones. So, as it was expected this model was not able to give appropriate estimates.

Next a neural model was built. It was solely based on the measured input-output parameters of the process. This model gave appropriate estimates for the oxygen to be used in most of the cases, but the extreme situations were not treatable by it. It is obvious that such extreme situations occur only in a few cases, therefore there is not enough data to train the net- work for those cases.

Parallel to the neural approach the application of practical knowledge - heuristic rules used by the staff of the factory, who controls the blasting process - was investigated as well. Expert systems are appropriate tools for utilizing such knowl- edge, however it turned out again, that a standalone expert system based only on the available rules does not provide proper estimate for all cases.

Finally we chose to combine the different (rule-based, neural, and classical mathematical) methods.

In a combined solution the main tasks are to define subtasks, to determine what approaches can be used for the different subtasks and how to integrate the results of the different ap- proaches.

TIT. THE MAIN STEPS OF NEURAL MODEL BUILDING.

The steel production with an LD-converter is organized in campaigns. During one campaign the production is contigu- ous and about 3000 charges of steel is produced. More than 60% of all charges of a campaign can be considered as typical ones, the rest contains the special cases. The lion’s share of the knowledge about the given technology is embodied in the data measured of the blasting process of all charges of a cam- paign. Therefore the central part of the system contains one or more neural networks, which are to give proper estimates for most of the charges, the typical ones.

There are many steps of building a neural model for a com- plex industrial problem. These are the next ones: to build a reliable database, to select a proper neural architecture and to form and validate the model.

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Tn forming a proper database the following main problems have to be considered:

the problem of dimensionality,

0

0

the problem of uneven distribution of data, the problem of noisy and imprecise data, the problem of missing data, the effects of the correlation between conseciutive data.

To overcome these problems extensive data analysis and pre- processing were required, where both mathematical methods and domain knowledge have been used.

To select proper neural architectures many expeiriments were carried out and validated for building the temperiature and the oxygen model using both static and dynamic network archi- tectures of different size. The tests made so far show that the best results can be obtained using dynamic MLP networks, implementing second-order NARX (series-pararllel) models [3] with 53 inputs and two hidden layers: N(53,12,10,1). The performance could be slightly improved using modular neural architecture where the different modules were formed apply- ing the boosting technique [7]. The reason behind the appli- cation of boosting techniques was that there may be some significantly different operating modes of the converter which have to be handled differently. The neural approach resulted in acceptable good models for the standard cases,, which form most - approximately 6570% - of the charges, lhowever this approach alone is not good enough for the rest of the cases.

More details of data analysis as well as of the nleural model- ing were presented in previous publications [8, 9,101.

TV. HYBRID MODELING: EXTENDING THE NEURAL SOLUTION

According to the results obtained using the neural models of the steel converter, it is obvious that a model, which can be used for predicting the required amount of oxygen for all or almost all of the practical cases can be formulated only using a hybrid solution, where different approaches are utilized. The hybrid solution exploits the knowledge carried by the database and all other information that can be obtained in addition to the recorded data.

Although the main purpose of the advisory system is to give reliable prediction of the oxygen, this system will help to analyze the different situations, to form new questions and finally to gain new knowledge that can be built into the sys- tem again. This iterative process helps to improve the mod- eling capability of the'system, to extend its ability to handle more and more special cases.

The functional scheme of the hybrid advisory system can be seen in Fig. 2. The hybrid system is built from blocks of dif- ferent neural networks exploiting the databases and blocks of

expert systems. The expert system modules utilize the avail- able theoretical knowledge - this knowledge is represented in mathematical equations -, and such experimental knowledge, that can be formed by symbolic rules: some rules of thumb gained by the staff of the steel factory during the many-year operation of the converter. This latter information is in the form of IF-THEN rules.

The system has three layers. The first (input) layer is an ex- pert system and it is responsible for data preprocessing, data filtering, data correction, filling the gaps in the database, etc. It is also responsible to find inconsistency of the data, and to find - if any - clusters of the data that can be handled by dif- ferent means, approaches. The input expert system has to decide how to handle the current data record, whether it is a standard case or it has to be treated specially. Tt decides ac- cording to the given rules of the current model which neural network or other estimator system must be used to predict the amount of oxygen. Tt also can correct some of the data ac- cording to the knowledge about measurement noise or meas- urement device errors. It records this decision also in the knowledge base to be used by the later experts to calculate correction terms and to integrate the results.

The second layer contains the direct modeling devices. It is formed from different neural models that can work with the data belonging to different clusters. Tn some cases such model cannot be used alone, it may happen that it can be used just together with certain correction terms that modify the result of a neural model.

The system makes it possible to built into this layer any other modeling device (e.g. mathematical models or expert sys- tems) in addition to the neural models. However, at present neither mathematical models nor expert systems can compete with neural ones. So far only such mathematical models could be formed that gave reliable prediction in a small neighbor- hood of some special working points. These models can be used in the validation of the neural models, or in the explana- tion generation (see below).

The third or output layer is the decision-maker of the advisory system. Tt has two main tasks: to validate the results of the various oxygen predictors, and to make the final prediction using some direct information from the first layer. This layer also uses symbolic rules. It validates the result of the second layer and makes a decision if the result can be accepted at all. This decision making is based on different information: e.g. some direct information from the input layer, or the informa- tion obtained from more than one experts of the second layer. As an example for the first case it may happen that the input data are so special that there is no valid model for them in the second layer. This situation is detected by the input expert system and although it is a rare situation, the advisory system must be able to give some valid answer even in such cases. This answer informs the staff that in this special case the ad-

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visory system cannot give reliable oxygen prediction, they must determine the required oxygen using any other (e.g. conventional) method.

In the second case validation is based on the results of more than one expert modules of the second layer. Using these re- sults the output expert system will form the final answer, which may be some combination of the results of more ex- perts or a corrected value of a given expert. The correction term can be determined using the results of other expert mod- ules (e.g. other neural networks), or a separated expert sys- tem, the role of which is to determine directly correction terms for special cases.

Explanation output Estimate

I I Output Expert System I

output Estimator

Correction

System

Input Expert System

Input Data

Fig.2 The functional architecture of the hybrid-neural advisory system

A further important task of the output layer is the explanation generation what is also based on built-in expert knowledge. As neural networks themselves form black-box models, they cannot generate explanation of the result automatically. How- ever, the acceptance of such results by an industrial commu- nity is rather questionable even if this result is quite good. The purpose of explanation generation is to increase the ac- ceptance of the results of the advisory system.

V. EXPERIENCES WITH THE ADVISORY SYSTEM

Currently the hybrid intelligent advisory system is in its test- ing phase. The advisory system has two operating modes: the development mode and the on-line mode. The development mode is for constructing the different oxygen estimator mod- ules, to train and test the different neural networks, to form the symbolic rules for both the input and the output expert systems. In the development mode the system is used off-line, which means that the data come fiom an archive system and not directly from the industrial plant. The validation of the oxygen prediction can be done using one of the direct models

(the temperature models) as it was shown in Fig. 1. The tem- perature models are also neural ones. To choose one of them for a given case similar expert knowledge is used as in the selection of the proper oxygen model.

The on-line mode serves as the advisory system. For a given charge input data are fed to the input expert system, which qualifies and preprocess the data. If necessary, it corrects some values or calculates reasonable data for the missing ones, etc. The input expert system activates one or more neu- ral (or other) moduls for oxygen prediction and give result(s) to the output expert system. Some direct signals fiom the in- put expert system are used to indicate to the output expert system that for a given case reliable estimation cannot be done. The on-line system can be run several times for one charge. In standard operation the system is run at the begin- ning of each blasting when all input parameters and the re- quired output parameters of a charge are determined. A new run is made when significant changes have happened in the input parameters (e.g. if some further additives like lime or fluorite are supplemented), or if some special state of the blowing process (e.g. special-color flame) is observed by the human supervisor. These states as special signals can be given to the advisor system.

A further possibility of the on-line mode is to run a so-called post-process model. Post-process model is used when all out- put parameters are known after the whole blasting process of a given charge is finished and the temperature, the carbon content and many further parameters of the post-process chemical analysis are known. This post-process model is used for on-site validation of the advisory system, to get further information for improving the whole system, to form new rules for special cases, etc.

Currently more versions of the system are under testing. These versions differ in the number of experts and the selec- tion process of the data for the given experts (neural net- works). In the rule-based parts at present several rules are used to filter and correct the input data and to modi@ the re- sults of the newal networks.

The preliminary results on test data show, that using the hy- brid system acceptable oxygen prediction can be obtained for a larger proportion (-80 %) of all charges than usirig any other approach. However, more detailed validation needs much longer on-site test. It is expected that during this test period many new rules can be formulated and added to the rule base making it possible to improve the overall perform- ance of the system.

VI. A HYBRID INFORMATION SYSTEM

The primary task of the developed system is to give reliable prediction of the oxygen, but it also provides information

1427 -.- / .

I Real time display system

d\ I I

User interface controller

Services Process and (explanation, oxygen models 3 help, etc.) (hybrid neural- expert models)

Result verification, model maintenance, model adaptation.

e filtering

I I

Process control and database servers

Fig.3. The structure of the system

about the significant features of the whole process. This way it serves as a blasting information system as well. The infor- mation system has three main functional parts:

- Blasting pre-qualijjing subsystem. Utilizing the information about the current state of the converter and about the sched- uled composition of the next blasting the system pre-qualifies the next blasting process.

- Blasting process advisory subsystem. The basic task of this subsystem is to calculate the necessary amount of oxygen to be blasted in order to reach the desired steel temperature. The system is able to recalculate its estimation several times dur- ing the blasting if it obtains recent information rt:flecting the current state of the process. The advisory subsystem is also able to run in off-line (off-process) mode, when the user can teach and test the internal neural and expert models on the available databases.

- Blasting evaluation subsystem. The subsystem evaluates and qualifies the finished blasting process exploiting all blasting information including post-blasting measurement data. The blasting evaluation subsystem can be used for on-line adapta-

tion of the process models and also supports the off-line model development tasks.

Tn order to build a blasting information system the hybrid intelligent process models are embedded in a complex envi- ronment. The block structure of the system can be seen in Fig. 3. The developed system realizes these functional parts, it also serves as a development environment for different hybrid models, and allows the on-line testing of the developed mod- els.

The numerous modules of the system support the off-line model building. The system allows the developer to create and train neural networks, create and test rules and evaluates the combined model on previously recorded data sets. Cur- rently only feed forward MLP type networks are supported with maximum two hidden layers. The networks can be trained using different versions of the backpropagation train- ing.

It also provides monitoring services of the on-line system and accomplishes the necessary interfacing tasks to the connected process control and database systems. The on-line system is

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VTT. CONCLUSIONS

Database sewer

Process controller

Information terminals

Y

T E Y M

Fig. 4. The on-line system

used in the factory to test the developed models with the col- lected data of the actual blasting process. It has three main interfaces: one for the process controller, one for the database system and one for the information terminals (see Fig.4.).

The process controller controls the on-line prediction system, it gives commands like PREDICTION or POST- PROCESSING to the on-line system.

The necessary data for the prediction come from the database server. The results of the on-line system are sent back to the database server for archiving and for further processing if necessary.

The different connected information terminals display the actual information about the system, such as the process state, the actual oxygen prediction, results of data analysis, etc.

This paper deals with the experience gained from the building of a hybrid-neural advisory system for a complex industrial process. One of the most important results of this project is that hybrid approach, where many different means are com- bined, can help to solve complex industrial modeling prob- lems. The main advantage of the hybrid solution is that it can utilize all available information, even if they are represented in rather different forms. Although the whole system is pri- marily based on the information contained in measured and archived data records, the resulted system is not a simple black-box model. The fsrst results of the on-site testing show that the hybrid system gives good predictions for more cases of all possible ones than any system that is based on a single (e.g. neural, or expert) approach. Also the results are better, than in the conventional situation when a human operator controls the amount of oxygen, so using this advisory system a significant improvement could be achieved. However, the real value of this system can be determined after a long period of everyday usage.

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