monitoring, forecasting and simulation of the steelmaking process

Dev. Chem. Eng. Mineral Process. 14(3/4), pp. 41 7-428, 2006.

Monitoring, Forecasting and Simulation of

the Steelmaking Process

Xin Hong*, Jia-Ying Chen, Shao-Bo Zheng, Xiao-Lei Qiu Shanghai University, Shanghai Enhanced Laboratory of Ferrometallurgy, PO Box 275, Shanghai 200072, P. R. China This paper introduced the sonic level monitor for continuous detection of slag condition, forecasting end-composition of molten steel using artificial neural network, dynamic modeling and computer simulation of the steelmaking process.

Introduction China is now taking a path into industrialization with help of information technology. One of its tasks is to reform traditional industry with appropriate advanced techniques. The renovation of metallurgical technologies and its equipment requires more comprehensive mastering of information for the whole process. However, it is often very difficult to carry out the numerous tests to obtain the necessary rules for process control because of not only the complexity of muitiphase reaction procedures under high temperature and the large scale of equipment, but also the high running costs. A more economic and effective solution is to set up static or dynamic mathematical models of the process from abstraction and synthesis of primary information based on advanced measurements. Forecasting, diagnoses and optimization of parameters can be realized using computer systems. This paper detailed achievements of monitoring, forecasting and simulation of steelmaking processes based on our laboratory studies with cooperation of the iron and steel industry in recent years.

Detection of Slag Condition in Steelmaking Furnace (i) Principles of slag condition detection in steelmaking furnace from sonic level 111 There was a relationship between furnace condition and noise, the later being able to provide details of particular features of the smelting process. In LD converter, three types of noise sources existed, namely: (1) The aerodynamics noise of oxygen flow from a supersonic lance and the noise

from impacts of iron liquid, molten slag and solid phase particles. (2) The noise from collapse and outflow of carbon monoxide bubbles. (3) The noise from movements of the steel bath and molten slag.

* Author for correspondence ([email protected]).

417

Xin Hong, Jia-Ying Chen, Shao-Bo Zheng, Xiao-Lei Qiu

The noise from a supersonic oxygen lance was the main sonic source. One part of the strong noise was transferred directly onto the takeout spot through the firnace roof, the other part formed a noise with characteristic frequency range after the repeated reflection and absorption by the furnace lining. The former was a direct sound field, and the later a reverberate one. The relative sonic level (SPL) at takeout port P may be expressed approximately by the acoustic formula [2]:

1 4 4m R

SPL = 10 l O g ( 7 ) + - - 9.8

where r was distance from sound source to P; R = s. Z/(J - C); s the total area of furnace lining; and C average acoustic absorption coefficient of the lining surface.

Assuming that the noise strength of supersonic oxygen flow measured at P was lo, and it attenuated to I after absorption of foaming slag, then:

where H was the lance position (m), and L the thickness of foaming slag (m). The sonic level detection technique for the slag foaming condition in EAF was an

extension of the sonic level monitor in LD converter. In electric arc furnace, the impact of arc against the charge was the main noise source apart from self-consumed or water-cooled supersonic oxygen lance. The arc noise may reduce as the foaming slag became well developed and a submerged arc operation formed. The melting procedure, submerged arc state, and the heating efficiency of the arc could be obtained indirectly from detection of arc noise intensity, so that relevant adjustments of power supply and slagging became easier.

(ii) Forecast of slag splashing and drying in converter Depending upon the slag state, the oxygen blow in LD converter may be considered in five basic conditions (as shown in Figure 1): A. Blow start - oxygen flow impacted directly against the molten bath and the

B. Splashing - foaming slag spilled or sprayed from the furnace. C. Drying orientation of slag - the foaming level of slag was low, but still covered

the oxygen flow. D. Slag drying - the melting point of slag was too high, large quantity of solid phase

material with high melting point was separated, the oxygen flow exposed in the firnace chamber.

E. Fine slagging - foaming slag was thick enough, it was coming close to the furnace mouth, but not spilled or sprayed out.

Table 1 shows the forecast rate of slagging using a monitoring system with sonic level detection in a large converter workshop. From statistical data of 158 heats, the accuracy rate of the splashing alarm was 97.5%, and the accuracy rate of the drying alarm reached 100.0%. It demonstrated that the detection method had very good reliability.

charged solid materials for slagging.

418

Monitoring, Forecasting and Simulation of the Steelmaking Process

Class: C Heat: 3682 Date: 2003/3/7 Time: 8:13:35 Blow: 15'12

. -~ . . . . . . . . . . . . . . . . The Tine o f Blowing Oxygen ( m i d -r

Class: D Heat: 3616 Date: 2003/3/6 T i m e : 4:55:28 Blow: 13'9

A: blow start; B: splashing; C: drying orientation; D: slag drying; E:fine slagging.

Figure 1. Schemes of various blowing states.

419


Table 1. Accuracy rate of alarms and curve analysis of slagging monitoring system.

(iii) Continuous detection of foaming slag condition in EA Fprocess By means of sonic detection and collection of other signals in the process, the time needed for metal bath formation, the state and thickness of foaming slag were determined more accurately, timely and visually, in order to have a good judement of the melting procedure. This information can be used to reduce energy and materials consumption, reduce nitrogen absorption into the bath, improve the product quality, as well as to increase productivity. It is useful to EAF process and to improve its overall technical-economic indexes [2, 31. Figure 2 (a) and (b) show the curves of typical arc noises.

% il: t ptpr6 t i l ' t ajgl 1 I < f! af 1q t > L t a $. * I,.'

Figure 2. Typical sonic level curves of EAR

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(iv) Research on correlation between sonic level detection information and the heat transfer efficiency of electric arc The variation of slag condition in EAF can be analyzed by intuitive observation of sonic curves records. However, it is necessary to obtain a quantified index for more practical comparison of the total level. Investigations indicated that an average sonic level could be adopted to properly reflect the overall operation level at each heat. The definition of average sound level was:

where N was expressed as the number of detecting points of sonic level in the entire power period; yi were sonic values on the different detecting points; andd ti time intervals between each detecting point.

Sonic level indicated the shielding effect of foaming slag on arc, and the heat transfer efficiency of electric arc was dependent on the shielding condition of the slag. Therefore, a definite relationship existed between sonic level and heat transfer efficiency of arc. Applying regression analysis from simulations based on 6 1 heats data, gathered during the commissioning tests of a 150-ton DC EAF, are shown in Figure 4. A relationship between heat transfer efficiency (%) of arc and average sonic level S was obtained as follows:

q = 96.51 - 12.06.S (4)

The correlation coefficient of the formula was 0.37907, which exceeded the required coefficient of 0.25 for 60 samples with 5% reliability indices, and indicated a close relationship between above two parameters.

Forecast of Steel Bath Composition (I) Forecast of end manganese andphosphorus contents in LD converter /4/ In the past, the end point control of a converter focussed mainly on forecasting the carbon content and temperature of molten steel. However, forecasts of other elemental contents was also very significant for reducing blowing time and auxiliary material consumption. The BP network was applied to obtain the forecast of manganese and phosphorous contents at the end blow point in the converter. According to metallurgical principles, the end contents of manganese and phosphorus are closely related to parameters such as slagging agent, coolant, the composition of molten iron, oxygen supply quantity, the bath temperature, etc. Among these parameters, the oxygen supply quantity and temperature exerted the largest influence. Besides these factors, the effects of parameters such as quantity of furnace charge and hot charging ratio, etc., were also taken into account.

42 I


In total eleven parameters including those mentioned above, their values were taken before sub-lance measurement, were chosen as the input data. End contents of manganese and phosphorous were defined as the output goal. Data from hundreds of heats with stable procedures and operational data were sieved as samples, of these 20% of samples were used for verification of off-line forecast and the other 80% were chosen for network testing. After testing, it was found that the fastest convergence and the best precision occurred if four latent layers were built into the neural network model. During testing, the momentum factor of the network was set as 0.9, the learning parameter as 0.6, and the square error sum of the expected value of network output layer as 0.001. After testing, the forecast results of relative error within the range of *20% were removed in the assembly of hits, which was equivalent to the forecast deviations of (A Mnl S 0.025% and JA PI I0 .0028%.

(ii) Four types of forecasting models According to the production practice, it was often not possible to obtain the accurate analytical manganese and phosphorus contents measured by the sub-lance before reaching the end point. On these occasions it was unrealistic to use them as input data for the current heat, unless they were used as data from reference heats for neural network testing of the following heats. There were four different kinds of neural network models for the end content prediction of manganese and phosphorus: 0 Model based on sub-lance measurement (models SLMn and SLP). 0 Model based on multiplex regression instead of sub-lance measurement

(RCSLMn and RCSLP). 0 Model used sub-lance measured value from reference heats instead of current

heat (RCSLMn and RCSLP). 0 Model without any data from sub-lance measurement (NSLMn and NSLP).

Analysis and comparison of the above-mentioned four models was performed. A comparison of hit ratios with relative error of 20%, i.e. corresponding to prediction error of IAMnJ 5 0.025% and IAPJ S 0.0028'30, are shown in Figures 3 and 4.

The hit ratio of models SLMn and SLP reached 91% and 84% respectively. The comparison between forecast and actual values are shown in Figures 5 and 6.

SLNn NCSLkl RC"SLh[n NSLMri SLP E S L P FXSLP NSLP

Figure 3. Comparison of four models Figure 4. Comparison of four models for phosphorus content forecast. for manganese content forecast.

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cr 0 5 LO I5 20 25 30 Forecast content of Mn in end point (~0.01%)

Figure 5. Comparison between forecast and actual values of end manganese content.

n 's 10 fi $ 5 u

0 5 10 15 20 25 30 Forscost content of P in end point (~0.01%)

Figure 6. Comparison between forecast and actual values of end phosphorus content.

Dynamic Analog and Simulation of Steelmaking Process (i) Dynamic model of converter based on the online detection of ahaust gas /5-6/ The model was set up on the basis of exhaust gas detection data from blowing practice. Temperature and composition of steel bath were continuously predicted by means of analysis of exhaust gas composition (CO, COz , 0 2 , etc.), thus providing real-time data for the dynamic control model.

423

Xin Hong, Jia- Ying Chen, Shao-Bo Zheng, Xiao-Lei Qiu

oxidation Xi b

variation of elements I

Figure 7. Flow diagram of computer program.

( I ) Basic hypotheses for the model The reaction and melting heat in the furnace distributed well in the steel and slag phases; the thermal resistance of scrap or cold charge presented mainly on the surface; and distribution of temperature and composition of molten end steel were considered as uniform.

0 Direct and indirect oxidation of the four elements: carbon, silicon, manganese and phosphorus occurred in the molten steel simultaneously. Their reaction rates were determined from oxygen flow and diffusion rates of reactive elements between the slag and metal.

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0 During the initial blowing period, oxidation of elements such as carbon, silicon, manganese and phosphorus, etc., occurred simultaneously both on bubble surfaces and at the border between steel bath and molten slag. Oxidation rates of different elements depended on oxygen flow.

0 In the later blowing period, the main reaction in the bath was carbon oxidation. The oxidization rate of carbon was controlled by mass transport due to its low content.

0 Most of the oxygen blown into the bath was involved in reactions, some oxygen evolved from the bath but not dissolved in molten steel.

(2) Solution steps of model Figure 7 shows the schematic diagram of the stages in the model calculations.

(3) Calculation results of model Based on operational data from a 250-ton multi-blowing converter, time-dependent variations of composition and temperature of bath and slag, as well as the slag weight and alkalinity were obtained with application of the model for computer simulations. (as shown in Figures 8 and 9).

n A: v - U U

*. 6

4

1 .S

3

I .c

2

i .I

1

0 .s

0

n a U - .d cn Y

1 4 T 10 13 lb 1Q 22 2s 26 I1 I4 Sl 4 0 4 3

B l o w time (X20 seconds)

Figure 8. Change of bath composition during blowing.

Because of the limited operating conditions, samples were only obtained for analysis from small time intervals. A comparison of calculated and measured values of bath composition at end blowing was obtained. Under stable operating conditions for the detection instrument, and if the original data agreed with the statistical requirements, then more than 1000 data were gathered from five heats (about 270 data per heat). They were analyzed and verified off-line. Table 2 shows absolute and relative errors between calculated and measured values of elemental contents.

425

Xin Hong, Jia- Ying Chen, Shao-Bo Zheng, Xiao-Lei Qiu

Carbon content, [C] % period

Figure 9. Change in slag composition during blowing.

Manganese Phosphorus content, [Mn] % content, [PI %

Table 2. Deviation between calculated and measured values flve heats).

I at blow end I Relative error 1 ~ 27.7% I 13.2% 1 16.8% I

(ii) Dynamic model of E4 F smelting process ( I ) Model structure and computational illustration As a multi-variable, nonlinear and dynamic system, the mathematical description of the EAF smelting process can comprise several nonlinear ordinary differential equations [7, 81. The main variables in the model can be expressed as a vector:

xT = {msc 9 mi 3 mbme * m,, 3 m*o* mcgo 9 mMgo 3 msjol 9 muno 9 mqo, m(sp mro 3 ( 5 ) mCO,, m0,, mN,, , [o/,C1 [o/,sil [o/oMnl [o/,Pl [o/,S] T, , T,, PR I q8.}

If 0 represents the coefficient matrix composed of the relative parameters and variables, this set of nonlinear equations can be simplified as:

( 6 ) a!x - = ox* dt

426

Monitoring, Forecasting and Sirnulation of the Steelmaking Process

The model was used to describe the EAF smelting process for its middle and later periods where the initial bath temperature was 18 1 1 K, scrap temperature 800 K, and the key parameters needed in the model were obtained from published literature or experiments [9]. Equations were solved with the classical fourth-order Runge-Kutta method. A time step of half second was adopted to give the best convergence solution. The numerical simulation of the process by computer required a few minutes.

(2) Simulation results The deviations between the calculated and measured temperature were within the range of k15"C. The analog results of elemental contents in molten steel and the slag weight are shown in FigureslO(a) to lO(f) , which show the agreement between the calculated and measured values [lo].

z - Y u

E

- calculated value

waxured value 0.30

0.00 I 0 8 16 24 32 40

Time. rnln

(a) Carbon content

- calculated value

0.1 8

0.1 0 L a 8 16 24 32 40

Time, min

(c) Manganese content

$ 0.006 .

n 0.005 . v1

0.004 -

0.003 .

..I

U

0.002 I 0 8 16 24 32 40

Time, rnln

(b) Silicon content

0.022 r

0.020 o m .

0.014

0.008

0.006 0004D

6 01312 - Q O I O

8 16 24 32 40 Time. rnin

(d) phosphorus content

Figure 10. The analog results of elemental contents in molten steel.

42 7


ow2-

OWD- ..__. ... .. . . . . . . . . . .-. . _. . ... 3000 _........' ------kpuldsIaqpaQ - - S Q h d I b g ( C t q - - I 0

* * -a f ----.- ..._.__

i , o a o - I

!-

0 9 16 24 32 40 0 8 1 6 1 4 32 4 0 4 8 Tlmr, mln llmr, mln

(e) Sulphur contents &I slag weight

Figure 10 continued.

Conclusions In recent years, collaborative research and development have taken place between our enhanced laboratory and steelmaking industries. Among them include the application of a sonic technique for slag monitoring in the steelmaking process, forecasting of bath end composition using artificial neural network, the introduction of dynamic process modeling methods and numerical simulations in LD and EAF process, etc. Some of these achievements have been applied to production practice, others provided a reliable basis for online application through offline verifications. These efforts benefit not only a better comprehension of the metallurgical process, but also make a contribution to the steel industries in their technological and process optimization for more economical operations and better competitive ability.

References 1 .

2.

3.

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10,

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