A New Method for Carbon Control in Basic Oxygen Furnaces

T2C2 A NEW METHOD FOR CARBON CONTROL IN BASIC OXYGEN FURNACE

I.D. Landau Laboratoire d'Automatique, Institut National Poly technique, Grenoble, France L. Muller ALSTHOM-DRE, Departement d'Automatique et d'Electronique, Grenoble (Meylan) France

G. Dolle, G. Bianchi IRSID - Maizieres les Metz - France

the reaction. This research was carried on joint= ly by ALSTHOM - Directions des Recherches and IRSID under the partial support of D.G.R.S.T.

SUMMARY This paper describes a new method for carbon con= trol in a basic oxygen furnace based mainly on an on-line identification of the dynamic model for the decarburation process, using the technique of hyperstable model reference adaptive systems. The dynamic model used for identification is of the form dc

dt

1

B+A c

2

An algorithm for the identification of such type of models has been developed which allows on-line identification of the two parameters A and B. The identified parameters are used for the esti= mation and the prediction of the carbon content in order to implement the predictive control. For on-line implementation of the estimation and control algorithm, a procedure for the vali= dation of the process model was also included. The algorithm has been tested on-line on a B.O.F. from the Usinor Dunkerque melting shop nO 1 where a digital computer is associated with the process. An objective evaluation of this control procedure compared with another procedure using an off-line adaptation for one parameter is presented.

THE PROCESS The L.D. refining process differs from the Besse= mer and Thomas process by the use of pure oxygen blown by a water-cooled nozzle through the con= verter throat. During the blowing a hood is used to evacuate the gas of the reaction without air penetration. The scheme of this converter is given by Figure 1. The reaction is controlled by means of two active variables : the altitude of the nozzle above the bath surface and the oxygen flow (400 m3 /minute). Spectrometers are used to make a direct analysis of the gas and then control of the reaction. BALLISTIC MODEL The melting shop NO 1 at USINOR Dunkerque is equip= ped with three converters, two spectrometers and one CII 90/10 computer. The computer assures first the management of the steel works, the main= tenance of the spectrometers (calibration), the acquisition and memorisation of the data. It secondly assures the calculation of the materials quantities that will be put in the furnace taking into account the production request and the inven= tory of the stock (cast iron, old iron) and the temperature of the converter. The operator dialogues continuously with the computer but he is responsible for the decision for the control of the active variables taking the gas analysis and his experience of the reaction into account.

INTRODUCTION The application of numerical computers to indus= trial process began in the French iron industries ten years ago. One ot the first applications was the automation of the oxygen refining process that represents now more than 50 percent of the sweet steel production. The IRSID (Research Institute of Siderurgy) conducted simultaneous research for the knowledge of the chemical reac= tion and for the use of mathematical models to the regulation and the reproductivity of the quality of the steel obtained by this discontin= uous process. The first realization was made on the 160 tons unit converter in the melting shop NO 1 at USINOR Dunkerque. A ballistic model was implemented in order to calculate the material quantities that will be put in the furnace to obtain a steel with the quality and temperature. desired. Then a dynamic model was used to im= prove the control of the final phase of the blowing. This paper presents a new method for the on-line adaptation of the parameters of this model and the predictive estimation of the carbon content of the steel to control the stop point of

DYNAMIC MODEL To control the properties of the steel and the slag during the reaction, high precision spectro= meters have been set up. The knowledge in real time (with a time delay of 20 seconds) of the gas composition that the converter (carbon mono= xide, nitrogen, argon, hydrogen, oxygen) gives the decarburation rate of the steel by the rela= tion dc dt

12 22,4

Q.

(CO + CO 2 )

(Q is the gas flow given by this analysis, CO, CO 2 are carbon monoxide and dioxide content in the gas).

257

Figure 2 represents the decarburation rate for the complete reaction. We can see that the three last minutes are characterized by a simultaneous decrease in the decarburation rate and the carbon content of the steel. The studies made by the metallurgists have led to the relation: dc

where : pI(k)+H(k)Y(k)e(k+l) Q(k)Y(k)e(k+l) Defining xO(k+l) as the "a priori" estimation of the output model at (k+l) time:

1 B (B + 4 1 (Ac)2) (Meyer's Model)

dt

xO(k+l) = x(k)-6T AI(k) x(k) [1-B (k)x(k)]}3/ 2 I

as the most chemically and statistically repre= sentative model. This relation allows by means of the measure of the decarburation rate (dc) an dt estimation of the carbon content and then a con= trol of the stop point of the blowing when the desired quality is obtained. However this sup= poses a perfect knowledge of the two parameters A and B. An off-line adaptation method has al= lowed an improvement of the steel qualities (mean carbon content: 0,060 % and variance 0,012 %) I I [ (The parameter B is fixed, the parameter A is adapted at each heat taking the analysis results of the ending sample of the steel into account).

and eO(k+l) = x(k+I)-xo(k+l) one has :

where H(k) is the integral adaptation gain matrix given by the relation

T

Q(k) is the proportional gain matrix that has the particular expression

ON-LINE ADAPTATION

AI

On-line estimation of the carbon content in a Basic Oxygen Furnace (BOF) was made first by Meh= ra and Wells 121. The continuous model d = k2d (l-k j ) was used in the Kalman extended filter (d is the decarburation rate and kl' k2 two parame= ters). Aguilar-Martin and Fournie 131 have developed a continuous discrete filter for this model and Meyer's model.

~2 Q(k)

x(k)

dc (k) decarburation rate dt

6T

sample period.

F2 (k)2

such that : yT(k) Q(k) Y(k) = AI + A = constant. 2 Such a simple algorithm is easily implemented on industrial computers; no integration is made with= in two samples as is necessary in most of the other non-linear filters.

From Meyer's model the specific discrete model is obtained by derivation x(k) - 6T.A (x(k).(I-Bx(k»3/2

0 AZ

0

In the present paper one has used for the decar= buration rate estimation a non-linear filter based on the hyperstable model reference adaptive system theory (MRAS) 141.

x(k+l)

T

H(k+l) = H(k) -H (k)Y (k)Y (k)H(k) I + yT(k)H(k)Y(k)

To implement the algorithm on the process computer we need a strategy to determine the first point from which the identification must start. An estimation of the concavity is made to define this point.

A general development for non-linear identifi= cation and estimation of such model is made in

We must recycle the measurements in the algorithm with new initial gain matrix in order to assure good convergence of the estimation because of the small numbers of measurements that are avail= able during the final phase.

For the above model the following algorithm has been developed. At each sample period we have :

The control of carbon content at the time k is computer by Meyer's relation:

[5[.

X(k+I)=X(k)-6T.A(k+l){X(k).[I-B(k+l)X(k)]}3/ 2

c(k)

The final carbon content is predicted by extra= polation of the discrete model from k = k o to k f with the estimated parameters AI(ko )' B~(ko) (integral components)

where x(k) is the estimate of x(k). We introduce the notations: pT(k+I)=[-6T.A(k+I),B(k+I)] (parameter vector) yT(k)

-6T A(k) x(k)[J-B(k)x(k)]3/2

F 1(k)

cS G C(6T .A) = -{x(k) [ I-B(k)x(k) ] } 3/2

F 2 (k)=

x(k+I)=x(k)-6T.A I (k ){x(k) [1-BI(k )x(k)]}3/2 o 0

=[F (k), F (k)] 1 2

G(k)

~ ~

=

t

vi

2 x(k) . A(k)I-B(k)x(k)

for: k = k+J, k+2 .... k and then using the f relation

2 6T.A(k)x (k){x(k) [1-B(k)x(k)]}1/2

The parametric adaptation law has an integral part and a proportional part :

EXPERIMENTAL RESULTS The present algorithm was first tested with recorded measurements. The implementation in the melting shop was made without disturbing the usual running of the furnace. This allowed the

258

comparison of the results obtained by the classic method of off-line adaptation of the parameters from one batch to another and this new method using the same measurements. SixtY-five heats had been completely conducted with these two methods and analysed. The results are presented on Figures 3, 4 and 5 and in table I. Figures 3 and 4 represent the evolution of two final phases of the decarburation rate with the onput of the identified model. Table I contains the results of the real carbon analysis and the results of the two estimation methods. Figure 5 shows the percent of reactions which have realized a final carbon content with an error 6 C in the two cases. We can conclude from these results that the proposed algorithm gives in all cases a good prediction of the decarburation rate, as well as a good estimation of the carbon content, but the final carbon prediction presents a certain dispersion. However it is remarked that the mean error between the on-line estimated and realized carbon is lower when the present procedure is used (-1,9 instead of -7,2 10- 3 %). CONCLUSION

NCH

CV

CR

CA

NCH

CV

CR

CA

36266 36272 36289 36288 36300 36298 36308 36310 36312

50 55 45 55 50 50 60 60 60

54 90 60 81 60 70 80 68 67

48 70 40 60 40 50 90 45 62

36478 36479 36482 36484 36485 36487

50 60 50 65 60 65

51 59 38 83 50 68

30 40 32 76 35 70

NCH

no of the batch

CV

desired carbon content (10- 3 %)

CR

carbon content obtained by the off-line identification method (10- 3 %)

CA

realizable carbon content using the proposed method (10- 3 %)

REFERENCES

A recurrent identification algorithm has been used for the control in real time of the refining LD process. A good estimate of the decarburation rate is obtained, also of the carbon content, but the irregularities encountered at the end of the process induce variations of the parameters of the mathematical model and therefore affect the final carbon prediction.

Bianchi, Dolle, Mikolajek, David and Lecigne, "Automatisation dynamique de l'affinage ii l'oxygene", LAM. A. RP 30 Juin 1972.

A dynamic control for the complete reaction with the active variables (nozzle altitude and oxygen flow) must improve the regularity of the reaction and so the final phase will not be disturbed and then we can have by the proposed method a good prediction for the final carbon content. The non-linear identification and estimation method used for B.O.F. has been extended for a class of multivariable nonlinear systems and it probably also has other applications than those discussed in the present paper.

CV

CR

CA

NCH

CV

CR

CA

36490 36488 35924 35939 35941 35963 35965 35967 35969 35988 35993 36032 36115 36118 36126 36128 36152 36158 36204 36208 36213 36220 36231 36256 36264

50 60 60 55 55 70 70 70 70 65 60 60 60 55 60 60 60 60 60 60 60 60 60 60 60

47 45 95 97 67 55 61 97 80 50 113 60 65 50 50 35 94 77 66 65 66 87 74 78 47

30 48 95 87 55 55 5'1 80 78 50 105 60 59 50 42 45 100 110 50 65 72 93 54 88 57

36320 36328 36346 36348 36352 36353 36394 36400 36401 36403 36402 36404 36410 36434 36436 36439 36441 36447 36448 36460 36462 36465 36467 36470 36476

50 60 55 55 70 60 60 50 60 65 60 50 70 60 60 60 45 45 55 60 55 45 45 50 65

34 53 78 58 81 89 70 49 60 91 68 37 64 87 60 56 33 48 42 100 51 49 51 56 77

20 38 56 40 95 70 60 30 35 91 80 42 30 50 58 80 25 48 42 70 50 30 60 35 100

Mehra, R.K. and C.H. Wells, "Dynamic modelling and estimation of carbon in a basic oxygen furnace", 3RD INTERNATIONAL IFAC/IFIP CONFERENCE HELSINKI, June 2.5.1971.

3

"Estimation predictive de la teneur en carbone clans un convertisseur

a

l'oxygene",

Compte rendu final de Recherche DGRST. Publication LAAS no 1292, Toulouse (FRANCE).

TABLE I NCH

2

259

4

Landau, 1. D., "Sur une synthese des systemes adaptatifs avec modele utilises pour la commande et l'identification d'une classe des procedes physiques". These de Docteur es Sciences. Grenoble 1973 (FRANCE).

5

Landau, LD., and L. Muller, "Estimation predictive de la teneur en carbone dans un convertisseur ii l'oxygene par l'intermediaire d'une identification dynamique". Compte rendu final de Recherche DGRST. Publication ALSTHOM. Juin 1975, Grenoble (FRANCE).

Depoussierage primaire

.. Tirage direct

1-

Tour de conditionnement

SPECTROMETRE DE MASSE CO,CO ,0 ,H ,N ,Ar 2 222

Oxygen ' "

Pot de conden sat ion

CALCUL DE dc

'"'Cit Analyse au

Calcul

Q et

niv~au

du bain

C et vis.alisation

correction , bilan oxygene

FIG.!

de

FIG.2

BLOCK

DIAG~l

OF THE B.O.F

EVOLUTION OF THE DECARBURIZATION RATE

(dc/dt)


t

1200.0 Temps

260

de

df

de

at

_______ Measured values

~1easured

values

Estimated values

t FIG.3 et FIG.4

t

MEASURED AND IDENTIFIED DECARBURIZATION CURVES .

% 100

f" ,,,

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,-'

,I ,,

,-

."

____ Estimation off line

,!

Estimation on line

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FIG.S PERCENTAGE OF REACTIONS WITHIN A CERTAIN CARBON TOLERANCE

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o

11 261

C