Condition Monitoring of Ferroalloy Furnaces

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ELECTRIC FUR:'>IACES

CONDITION MONITORING OF FERROALLOY FURNACES P. Borg*,

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G. Waalmann* and T. Leidal**

*Illstitute fin Energy Tcchnolllg)', N-20()7 Kjeller, Nonuay **Elkl'ln a.s Information Teclllllllog)', N-.J602 Kristians(l/ul, Norwtrv

Abstract. This paper presents a condition-monitoring system for heatexposed components of ferro alloy furnaces. A prototype is now installed on a furnace at Sa1ten Verk in Norway. The instrumentation includes about 250 measurements of different kinds. The computer software which handles the measurements. includes a mathematical model of the furnace lining. a leakage and "blow-out" detection system for the power supply and smoke hood. a calculation of thermal stress on exposed components. a system for handling of alarms. and an instrumentation diagnostic system. The operator interface is based on a colour graphic display system. The project has been successfully completed. and the system has proved itself as an important tool for the operators controlling the furnace. Keywords. Condition monitoring. ferro silicon. metallurgical industries. modelling. reduction furnaces.

The development of a condition monitoring system for ferro alloy furnaces was initiated in 1986 as a cooperation project between Elkem a.s. which is a Norwegian ferro alloy producer. and Institute for energy technology. The project has been partly sponsored by E1kem and partly by the Royal Norwegian Council for Scientific and Industrial Research.

INTRODUCTION A major problem in connection with smelting furnaces for ferroa110ys is the sudden and unexpected breakdowns of the production equipment. which leads to production loss and increased equipment expenses. More extensive breakdowns may even be hazardous to the operators. Table 1 below shows examples of expenses in connection with total loss of a few main components on a medium size smelting furnace.

By installing a monitoring system on a furnace system it was expected to - avoid. or at least limit damage to furnace equipment

TABLE 1 Cost of equipment. installation and production loss in 1000 US $ for some components of ferro alloy furnaces.

Component Smoke hood Furnace lining Electrode. lower part Charge tube

Equip. cost

Inst. cost

Prod. loss

850 250

40 85

1250 2500

200 8

5 0.5

350 7.5

- reduce maintenance expenses - increase the effective operation period of the furnace - increase production by optimizing the process operation and the load on the equipment - improve safety for the operators by reducing the danger of gas. fires and explosions. The system will not be limited to the utilization on ferroa1loy furnaces only. but will be potentially suitable for all kinds of smelting furnaces and similar equipment.

An unforeseen breakdown of a smelting furnace is also usually followed by a period of ineffectiveness in both the furnace operation and electrode conditions.

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PROCESS DESCRIPTION

Originally, the project was intended for furnace 9 at Fiskaa Verk in Kristiansand , southern Norway . However, a serious breakdown of the furnace lining excluded this furnace from operation for an indefinite period of time, and the project was transferred to furnace 3 at Salten Verk in northern Norway . Furnace 3 is an open 51 MVA ferro silicon furnace producing 75% FeSi (i.e. 75% Si and approx . 25% Fe). The furnace consists of: - A furnace pot, equipped with an outer steel jacket and an inner heat-resistant lining . The diameter of the pot is typically 10 meters and the weight of the lining materials is about 500 tons .

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2) The power supply system; i . e. all water cooled equipment of the electrodes, as well as the flexible joints to the transformers. Electrode equipment, such as contact clamps, protection shields etc, may be subject to very high temperatures . Large water leakages are therefore critical to the equipment and may in addition disturb the furnace operation seriously. Unstable thermal conditions will lead to thermal fatigue of the components. By continuously monitoring and storing information on component stress, it is possible to estimate the probable working life of the components. Knowledge of total thermal stress also provides important information about the furnace operational conditions.

- 3 S0derberg electrodes of diameter 1.7 m. - Smoke hood for the vent of the outlet gases , which at the same time serves as heat protection for the above equipment . - Raw materials supply system . - 3 transformers. Most of the heat exposed equipment above the furnace pot is water cooled . The raw materials for 75% FeSi are quartz (Si02), coke or coal (C), wood chips (C), and iron (Fe) . These components are mixed in the appropriate ratio and fed to the furnace via the raw materials transport system. The heat required to combine the oxygen in the quartz to the carbon , is supplied by electric arcs underneath each electrode (130 000 A and 200 V). Some of the materials are transformed to gas, which may cause combustion at the furnace surface , and lead to an intense heat generation ("blow-out") . This unfavourable result will expose the equipment to heavy thermal stress. The melted metal is collected on the bottom of the furnace pot and is tapped in big ladles 12 times per day . The operation is continuous, and the production yield is over 100 t per day . Five subsystems of the furnace were selected for supervision by the monitoring system. These are (listed in decreasing order of importance) : 1) The heat-resistant lining of the furnace pot; which are degraded in the course of time, and it is therefore desirable to supervise the degree of degradation . The cause of the disastrous breakdown in furnace 9 at Fiskaa Verk was a burn-through of the bottom of the furnace.

3) The smoke hood; included glands for the electrode passage through the hood . The entire smoke hood is water cooled, and possible leakages as well as thermal stress are equally critical here as they were in the power supply system .

4) The water cooled components of the raw materials supply system, i.e. the feed lines (tubes) leading down through the smoke hood to the charge surface. These components are highly exposed to heat and mechanical stresses . Large water leakages would carry water directly down into the furnace or to the electrodes. Both eventualities would be very harmful to the furnace operation. 5) . The maladjustment (skewness) of the electrodes. The electrodes are lowered approx . 2 . 8 m down into the raw materials . They are fixed , while the furnace pot with the raw materials is rotating slowly (1 rotation/ 2-3 weeks) . This fact, combined with crust formation in the furnace , may in some periods cause maladjustment of the electrodes. The electrode column, having a constant weight of approx. 50 tons, may impose considerable mechanical load to the furnace equipment, as well as to the electrode itself.

COI1
HEAT -RESISTANT LINING

Fig. 1.

Sectional drawing of an open FeSi furnace.

INSTRUMENTATION

Data acquisition system

Primary sensors Conventional and well-proven measuring techniques have been applied for the project, as indicated in Table 2. Much attention has been paid to the protection of the electronic equipment, and galvanic insulation is extensively used. This is of utmost importance in the presence of strong magnetic fields and unstable conditions of the protective ground .

TABLE 2 Applied measuring principles. Measurement

Range

Temperature, -50-+950 °c furnace pot

Device Thermocouples type K

Temperature water, air

0-130 °c

Pt 100

Flow

0-30 m3 /h 0-15000 A 0-360° 0-1 m 0-7 bar

Orifice meters

Current Angel Position Pressure

Rogofski coil Potensiometer Ultrasonic Strain gauge

A decentralized data acquisition system was selected . For this purpose 7 "boxes" of inhouse manufacture were installed, 6 of which are data collectors, and 1 is the communication device between the data collectors and the main ASEA MP200 computer. The main components of the data aquisition system are shown in Fig . 2 . The data collector box for the heat-resistant lining is installed directly on the furnace, and connected to 35 type K thermocouples and one potentiometer for angle measurement. During the furnace rotation, communication (serial) and power supply is carried over 4 collector rings to an adjacent unit. This unit communicates directly with the communication concentrators through 2 optical fibers. The data collectors for the power supply system and the smoke hood are identical, and each has 75 inputs for 4-20 mA signals, and 75 inputs for Pt-lOO temperature sensors. From the power supply system the following instruments are connected : 48 for water flow, 58 for temperature, 27 for water pressure, and 2 position meters.

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Fig . 2 . Principle of the data acquisition system .

The data collector for the smoke hood is connected to 24 flow meters for water flow in the smoke hood sections and the charge tubes, 26 temperature sensors, and 1 water pressure meter . In addition there are 3 reference measurements in each box for the control and calibration of the measuring equipment . A data collector for the transformer's secondary currents is installed in each transformer room . The collector has 16 inputs of which 8 are used at present to measure the secondary current by means of Rogofski coils . The data collector in one of the transformer rooms collects data from the other transformers via fiber optic cables .

Computer system The computer system at Salten Verk is shown in Fig . 3. Furnace 3 was already equipped with computer control for the weighing and transport system, the outlet gas cleaning system (filter) and for process control. By selecting the same type of computer for condition monitoring as well , ASEA MASTER PIECE 200 , the existing colour graphic system (MMC) and communication with the central computer (ND100) could be used . On the MMC display the current situation is presented to the operators . The ASEA MP200 has been programmed in Pascal . The program development is performed on a VAX 750 and the resulting programs are dumped to the MP200 in executable form .

ConInunication For the communication between the concentrators and the local data collectors, fiber optic cables were selected . This type of communication cable is especially well suited in the vicinity of electric reduction furnaces , with strong magnetic fields, inducing disturbances to conventional cables . Three of the data collectors are coupled to a fiberoptic bus . For the bus connection 4-port Siemens light splitters are applied.

Data for long term storage, such as thermal load data for exposed components and some trends for the heat-resistant lining, are transferred to the central computer of the plant , which is a Norsk Data ND100. Here , a database system is available for storing of data both from the process control s ystem and the condition monitoring system, and also programs for the retrieval of this data. The database is also accessible over the terminal network .

I'll

weigh ----->

transp . '-_-"-_--' control room filter

control room CENTRAL

furnace

"------1 COMPUTER ND 100

I

optical

-..:....------>1 1 cone en tr .

fiber b u s ·

Elkem termin a l

netwo rk ( UPNODE )

I .

control room

tapping room

Fig . 3 . Computer system at Salten Verk

METHODS Condition monitoring of furnace lining Problem . The materials in the lining are exposed to hard wear when the process is operational . First, the temperature in the o molten metal is approximately 1800 c. Then, during production, SiO is released and some of it penetrates the heat resistant materials. At the same time the molten metal seeps down into every crack in the lining . The result is that the heat resistant materials are transformed and partly replaced by molten metal . When examining old linings, a relatively well defined boundary is frequently found between the transformed and the untouched parts . This boundary, "the transformation front", usually has a parabolic shape with axis of symmetry along the vertical centreline of the furnace pot . The lowest temperature , at which one can find molten ferro silicon, is 1207° C (eutectic temperature), and the transformation front is often called .. the 1200 degrees isotherm" . Al though it is an oversimplification, one can say that there is a well defined boundary, above which the material is transformed while the material below remains intact . The use of preventative maintenance to avoid bottom-drainages will improve both safety and economy . A system for condition monito-

ring should be able to act as a maintenance tool and estimate the position of the transformation front based solely on temperaturemeasure ments made on the outside of the furnace-pot. Mathema tical model of the furnace pot. The furnace pot is equipped with 31 thermocouples to measure the surface temperature and 4 thermocouples to measure the air temperature. The estimator uses 13 of the surface temperatures and all of the air temperatures. In order to estimate the position of the transformation front of the pot lining, a relatively simple 3-dimensional mathematical model has been constructed (Waalmann, 1988) . The model describes the temperature distribution in the bottom of the lining . The distribution de~ends on the shape and position of the transformation front. We have chosen a model description using a heat balance method (Welty, 1974) . In this description the lining is divided into a number of computational elements, the segments . Each segment is described by mean values (temperature and thermal conductivity) . Heat transfer is calculated between node points situated in the centre of each segment . This approach is chosen to minimise the computational time. It is sufficient to use a static description because the transformation process is very slow compared with the updating interval of the model .

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Examinations of old linings. have led us to describe the transformation front as a paraboloid, Since tne furnace has a cylindrical shape. these two factors have made us choose a model which is symmetrical around the central axis of the lining (rotational symmetry). In our description. the lining is divided into 21 segments, This number follows the natural arrangement in layers (7 layers including the jacket) . and divides every layer into three circular segments (Fig. 4). This division ensures that there is a simple relationship between the measured and the calculated values .

The model has three boundaries. each of these having its own boundary conditions . The uppermost layer in the lining receives heat transferred from the molten metal . At the boundary underneath the furnace-pot. the heat is transferred by a combination of convection and radiation . The convective heat transfer is expressed by Newton's rate equation and the radiative heat transfer is expressed by the Stefan-Boltzmann radiation law . Linearized around the last calculated surface temperature . the heat transfer may be written (for a grey body) : E a A «T4 O.s

q

1

I Fig . 4 .

1

T

1

where

I

1

Lining with transformation front . Arrows indicate position of temperature measurements .

Based on this geometry a static heat balance equation system for the lining is established . The equation system contains 21 unknown temperatures corresponding to the mean temperatures of each box . To speed up the computations these equations have been linearized . and become in matrix form : A' x = where

A

!?

coefficient matrix

~

21 unknown temperatures

!?

vector

The heat transfer between two boxes follows Fouri er's heat rate equation : _ A' A' 6T

q where

heat transfer coefficient surface area

E

emissivity

a

Stefan-Bolzmann constant

A

surface area

TO . s T s T air

new surface temperature

point of linearization air temperature

Heat is also transferred through the wall of the lining . We assume that the wall has rotational symmetry and that there are no vertical heat transfer in the wall. The heat is transferred from the wall by a combi nation of convection and radiation in a similar manner as above . With a finned surface. the total heat transfer may be expressed as :

T4 2 n rh E a \' T4 y O.y- ~ where

1

+

4 T3 (T - T » O. y Y O. y

fin effectiveness total finned surface area

A

thermal conductivity

A

area of the boundary normal to the direction of the flow

heat transfer coefficient

6T

difference between temperatures

point of linearization

1

distance between node points

The horisontal heat flow between the two outer-most boxes in a layer is described by (Wong. 1977): (T A· 2·n · h

q

0

- T

In ( r / where

h c A

T4) air

1

r2

)

height of the boundary between the boxes.

h T

0

T

1

temperature at radius r temperature at radius r

0 1

surface temperature air temperature 2 nryh

primary surface area

The connection between the transformation front and the temperature distribution is described using thermal conductivities and volume computations . In the chosen system of coordinates. the transformation front may be written as a paraboloid : ( 1)

Condition :\Ionitoring Given values for a and b, the front will cut the boxes in a specific way, see Fig. 4. The volumes of the transformed and the untouched parts of the boxes are calculated and the transformed part are given a new, high conductivity, while the untouched part retains the original conductivity. The original thermal conductivity of heat resistant stone may be 1.25 W/m'K, while the conductivity of the transformed material may be 6.0 - 8.0 W/m·K. In this way, the mean conductivities of the boxes become functions of the parameters a and b. Given a and b, the temperature distribution of the lining is found by solving the model equations using Gauss elimination with pivoting. Estimation of the transformation front. The location of the calculated surface temperatures underneath the furnace pot, is the same as measured, and the deviations from the measurements form a basis for estimating a and b in Eqn . (1), as depicted in Fig. 5.

emperatures of ambient air and molten metal

~

MODEL

L:~:~ures Temp.

ESTIMATOR

I

,..-+L_ _ _ _--'

Parameters a and b Fig. 5.

Mathematical model and estimator.

To find the parameters a and b, the following criterion is minimized with respect to a and b:

measured values

where

calculated values qi

weighting factors

The criterion (2) forms the basis for the least squares estimation algorithm. Since the parameters are implicitly included in (2) we have used a central difference approximation for the derivatives. In order to find the zeroes of the derivatives, we have used a recursive algorithm. To avoid the calculation of second order derivatives, the Gauss-Newton method is chosen . In this algorithm, the Hessian, which consists of derivatives of F of the second order, is approximated by the Jacobian (Dixon, 1976):

or Ferroallm

Furnaces

If, for the sake of simplicity, we form a vector of the two parameters,

~

= [

:~]

= [

:J

the Gauss-Newton algorithm can be expressed in this form:

where the elements of the Jacobian are approximated using central differences. Each calculation of the Jacobian implies that the model equations must be solved four times. The restricted computational accuracy may cause the iterative algorithm to calculate too long steps, and thus pass the minimum point. To avoid this we have modified the Gauss-Newton algorithm using a Fibonacci division method (Schwefel, 1977). One of the main problems concerning the reliability of the estimator, is the initial conditions . Furnace no. 3 at Salten Verk is approximately 10 years old, and the initial condition when starting the estimator is not entirely known. This means that our initial values must be based on manual heat flow measurements and assumptions . A final evaluation of the estimator can only be made when the lining is taken apart . Leakage detection Problem. To withstand the high thermal load, most of the exposed components of the furnace are water cooled. There are separate circuits to each component, but the circuits are gathered in groups coming from the same manifold. It is important to detect leakages as soon as possible of two reasons. First, a leakage upstreams of the component will result in insufficient cooling of the component, and second, spill of water in the furnace may be disadvantagous . Visual inspection of equipment inside the furnace is difficult, and therefore an automatic leakage detection system is needed. Flow measuring equipment that is robust enough for the present purpose, is very expensive. To minimize the number required, we only measure the return flow of water from the circuits of particular interest, and in general we do not measure the flow of every circuit coming from one manifold. To detect a leakage in one circuit we do not have enough information directly in the measurements. Simple mathematical models have therefore been developed as a supplement to the measurements.

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Leakages detection methods. The leakage detection is based on a comparison of the measured flow and an expected flow assuming there is no leakage. Since we have no measurement of the inlet flow to the circuit, we calculate this flow using a simple model of the drop in pressure along the circuit :

To filter out disturbances, the cross-correlation of X and Y is used to detect leakages . The cross-correlation ~ is calculated recursively with a suitable forgetting factor A, close to 1 . 0:

1

Normally, the cross-correlation is close to zero, but when a leakage occurs it drops to a negative value. A typical behaviour of the cross-correlation is shown in Fig. 7.

Pin - Pout where

Pin Pout a

2

(3)

w

a

manifold pressure

~

xy

(T,k) = A

~

xy

(T,k-1)

+

(l-A) X(k- T) Y(k)

atmospheric pressure flow conductivity volume flow

w

The manifold pressure is measured . The flow conductivity depends on the friction and the geometry of the circuit and on a Reynolds number . We assume that a could be considered constant . This is valid for small flow variations . Due to the geometrical complexity of the circuits and many unknown quantities , it is impossible in practice to calculate a from physical data. Instead, a is estimated from the measurements using Eqn . (3) on all circuits and assuming an initial period without any leakage . Although the conductivity a is considered constant, it is allowed to vary slowly over time. Such a variation may be due to a change in the pipe friction and/or diameter from growing scale inside the pipe. To adapt to such variations a is updated recursively in a very slow manner. To detect leakages we define the following quantities:

x

w

-

y

w

- w

1

2

W

1

t tL Fig .

7. Typical behaviour of the crosscorrelation of X and Y.

We have considered different ways of obtaining ~ and ~ . If the flow is sub2 ject to ver~ smal1 variations ~ and ~ may be set equal to a constant, l nominai flow . If the flow varies slowly, a better solution would be to set these flows equal to a moving average of the previous real flows. In the current implementation of the syst~m , we have chosen to set-w equal to w and w2 equal to w . This implie§ that both and y equals the ieakage flow, but with opposite sign. The problem then reduces to the calculation of the auto-correlation of the estimated leakage flow. If the autocorrelation is below a prescribed value . an alarm signal is given to the operator.

*

2

where wand ware actual inlet and outlet flow and w ana ~ are the corresponding 2 flows assufuing n0 leakage . w is calculated from Eqn . (3) and w is measu~ed . Normally, X and Y varies arouna zero due to disturbances acting on the system, but when a leakage occur, X turns positive and Y turns negative as shown i Fig . 6.

This leakage detection system is implemented on all 72 circuits. The lowest detectable leak depends on the disturbances in the system. Simulations have indicated that leakage flows of a few percent of nominal flow could be detected. The alarm limit is now 4 % of nominal flow in each circuit. Thermal load on the components

X

o

------Y t L

Fig.

6 . Typical behaviour of X and Y when a leakage occurs.

Problem. The components of the equipment inside the furnace are subject to severe thermal load, primarily caused by the "blowouts" . The monitoring of this thermal load has two functions . Firstly, the operators need continuous information about the load on the components in order to adjust the operation of the furnace accordingly. Secondly, such information is useful in connection with preventive maintenance . The load history of the components may be logged and used as a background for the replacement or repair of components .

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Calculation of thermal load . For all the components where we have measurements of flow and temperature of the cooling water, a thermal load per unit of area is calculated . This includes the following components : -

Contact clamps Pressure rings Protection shields Lower part of charge tubes Smoke hood glands Sections (15) of the smoke hood

Q

*

cp

*

w

* (

T - Tin ) / A

(4)

density of water c

p

2

2

x (k)

a x {k-1)

x{k)

~

x{k-1)

+

+

2

(I- a ) x (k) (1-~)

x{k)

suitable filter constants

Since a moving average of the standard deviation is used , slow variations in the heat fluxes will not influence the load figure . This property is suitable since such slow variations are not considered harmful to the components . Blow-out detection

where

w

where

a, ~

The thermal load is defined as the heat flow transferred to the water from the component and is calculated by the formula Q

2 s{k) " ( x (k) - (x{k) )2 )1/2

heat capacity of water flow of water through component inlet temperature of the water outlet temperature of the water "effective" area of the component in m3 . This is the area of the part of the component which is exposed to the heat from the furnace .

The effective area of all components except for the shields, are constant . The effective area of the shields is the area of the part of the shield which is below the smoke hood. This area will vary with the position of the electrode columns . A large and fluctuating thermal load on the furnace equipment may cause fatigue after some time. This stress on the components is not measured directly, but we assume that the heat flux from the component to the cooling water is representative of the thermal stress . The level of the load is easily expressed by the mean of the heat transferred to the cooling water, Eqn . (4), over some period , say 24 hours . To store the fluctuations over the same period we have studied different methods, the most advanced involving Fourier transform of the heat flows . In the current implementation, we have used a moving average of the standard deviation of the heat flux of each component as a "load figure". This method is easy to implement and requires little memory and computing time . Compared to the Fourier transform method, it seems to give a satisfactory description of the load variations. The following formula is used :

A blow-out will normally influence several components simultaneously. This fact is used to detect blow-outs by a logical gathering of adjacent components in groups and calculating the average heat load of each group . A blow-out alarm is given if at least one of following criterions are satisfied: - The average heat load on the group of components exceeds a limit. - The gradient of this load over the last 5 minutes exceeds a limit . Miscellaneous The condition monitoring system also includes several other sub-systems and functions . The most important of these are: - Monitoring of the water pressure on the pressure rings. - Monitoring of electrical current in the flexible cables. - Alarm handling system. - Diagnostic system for the instrumentation . The water pressure of the pressure ring cooling water is used to press the contact clamps on to the electrodes . It is therefore of major importance to maintain this pressure. Insufficient contact between clamps and electrode will result in unsymmetrical current distribution in the electrode and thereby insufficient baking of the electrode carbon .

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A break of the flexible cables may be impossible to detect visually because of the hose enclosing the cable . Such breaks may affect the current distribution and lead to the same trouble as described above . The monitoring system may include a large number of possible alarms on different levels . To handle these alarms we have adopted a simplified version of an alarm handling system called HALO, which was developed for nuclear power plants by IFE's department in Halden (Nilsen , 1983) . The main purposes of this system are to reduce the number of irrelevant alarms and to give an alarm text as informative as possible . Because of the large number of sensors and the relatively complex instrumentation and data acquisition system, we have also included a diagnostic system for this instrumentation . All measurements pass through this system before being used in the condition monitoring system . If any errors are detected , the corresponding measurements are marked with an error flag, and the system tries to identify and locate the cause of the error and forwa rd an informative message to the user . RESULTS The output from the condition monitoring system consists primarily of the screen pictures on the graphic display system in the control room . Twelve pictures have been developed for the monitoring system showing the measurements i n numbers and by bar graphs, trend di agrams of some of the measurements a nd pictures showing the transformation front of the lining and the heat load on the components in colour coding . The alarms are presented on top of the screen in a reserved field. In addition , the alarms are placed in an alarm list which is displayed on request . The other form of output from the monitoring system , is trend diagrams from the database system on the central computer. Examples of such are shown in Figs. 8-11 . Fig. 8 shows the variation over 3 months in the temperatures measured at each side of one of the tap holes of the furnace . One can easily see the periods when the tap hole has been active. An increasing trend in these temperatures will indicate a deterioration of the lining around the hole .

<111(\

_

T . Lcid
Furn.ac:..., pot.

side ",,,1 1 till' hole L

temperature .lD

rurnace pot,

"id" "all tap hole I.

t""'.pcr .. t" ... ., ' 9

'c

200

100

8 80112

880901

880810

881 006

Fig . 8 . Side wall temperatures of the furnace pot Fig . 9 shows the trend over 2 . 5 months of the distance from the estimated transformation front to the lower layer of the lining . One can here observe a significant decrease in this distance. The main reason for this is probably an increase in the electrical input to the furnace over the same period . The transforma ,·:ton front has later been stabilized on a level slightly below 20 cm . The lining is about 10 years old and will have to be rebuilt in the near future .

_

O.s ~

Dist .. nce fro .. transfonDation f ront to lower layer of 11n1n9

-r

__~____L-__~__~____~__-L____~__~__~__

0. '

0.3

0.1

880317

880331

880414

880512

Fig . 9 . Trend in position of transformation front of lining. Fig. 10 shows a typical variation in the heat load on the components of the electrode system over about 2 days . Fig . 11 shows the heat load variation of a contact clamp over one week together with the load figure of the same component . The load figure is calculated for each component once a day, and as is clearly seen in Fig . 11 , it attains a high value in periods with large fluctuations in the thermal load .

Condition I\luniwrin);{ of Fe rroaIlm" Furnacl's

load on the components, and owing to this, the electric input to the furnace could be increased without risk of damaging equipment. Since the installation of the system at Salten Verk, the input to the furnace has been increased by about 4 %, which corresponds to an increased production of 2 millions US dollars pr year. The introduction of the monitoring system is one of the main reasons behind this increase .

--- - . Heat lo.d . elec t r ode 2 , contac t cl.-;o 1

1000+----------'----------'---, . 00

600

In addition, the system provides for early warning of different failure situations so that actions could be taken before serious accidents occur. The long-term storing of data describing the load "history" of the furnace components could be used for preventive maintenance . These data may even be important as a basis for new, improved furnace designs .

<0 0

200

8Sl1::z?

881126

0 8 1125

Fig . 10 . Heat load of contact clamps.

-

He a t

load, electrode 2. c o nt"ct cl ...p 1

.• -_ ..

Lo . ~

f igu r e. electrode 2. cont ac t c l ... p 1

1 000 +-~~--~--'-------'------~--'--r'00

kW/ . '

The system includes monitoring and supervision of five subsystems of the furnace . The design of the system is modular, making it possible to select sub-systems according to the requirements of various applications. Salten Verk has already decided to install the furnace pot monitoring system on their two other furnaces .

150

100

50

88 120 4

881206

88120 8

The described prototype of the monitoring system has been installed on n ferro silicon furnace. However, the principles could be adapted to other processes of similar design, e . g. ferromanganese and calciumcarbide furnaces.

881 210

REFERENCES Fig . 11. Load figure compared with heat load of one contact clamp .

CONCLUSION A condition monitoring system for ferrosilicon reduction furnaces was developed and installed on an open furnace at Salten Verk in northern Norway. The condition monitoring system has been under development for the last three years. During these years the parts of the system have been gradually installed and connected to the furnace. The development has been of an iterative nature where improvements have been made based on the experience gained by real testing of the system. Our main conclusion is that the furnace operators now possess an important tool for supervision of the process and the equipment . Although the system was originally intended as a condition monitoring system for the various furnace components, the most valuable function may turn out to be process supervision. The system provides the operator with full information about the thermal

Waalmann, J . G. (1988) . Estimating the condition of the heat resistant lining in an electrical reduction furnace . Modeling, Identification and Control, Vol.9, No . 1 . pp.47-56 . Dixon, L. C. W (1977). Optimization in action. Academic Press, p . 5. Schwefel, H.P . (1981) . Numerical optimization of computer models . John Wiley & Sons , pp . 27,29 . Welty, J . R. (1974) . Engineering heat transfer . John Wiley & Sons, pp.85,88. Wong, H. Y. (1977). Heat transfer for engineers . Longman. p . 19 . Nilsen, S . , F . 0wre (1983). A description of the software for the HALO alarm handling system. OECD Halden Project Report: ~ May 1983.

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