Neural Network Modeling and Steady-State Optimizing Control for Nickel Flash Furnace in Smelting Plant

NEURAL NETWORK MODELING AND STEADY-STATE OPTIMIZIN...

14th World Congress of IFAC

N-7b-02-1

Copyright (g 1999 IFAC 14th T'rlennlal World Congress~ Betjing, P. R. China

NEURAL NETWORK MODELING AND STEADY-STATE OPTIMIZING CONTROL FOR NICKEL FLASH FURNACE IN SMELTING PLANT

Wei-han WAN

Bai-wu WAN

Institute of Systems Engineering, Xi'an Jiaotong University, 7] 0049, Xi'an, P.R.China Email: lllunhl'v@X)tu. edu. en

Yong.. fa YUAN

Jin-yi YANG

Jinchuan Non-ferrous Metals Company, 737103, Jinchang, P.R.China

Abstract: The paper proposes an approach that uses neural net\\'ork technique to establish the neural network models of the complex metallurgical process--...-nickel flash smelting process. These models are performance index modeJs for the production requirements of the process and product yield modeL Different modeJs based on different input variables and different structures of neural network are discussed in the paper, and they perfonn excellently. Then~ the paper studies the industrial-site implementation of steady-state optimizing control for the furnace. The results show that the modeling and optimization control to the process provides a better effect on saving energy. COPJ'right cD 1999 IFAC Keywords: neural network, modeling of neural network, steady state optirnizing control, nickel flash furnace

1. INTRODUCTION

The nickel flash furnace (FF) is a typical complex system in metallurgical industry, which consists of multiple sophisticated chemical processes. From the overall process system point of vie",', FF is a highly non linear system with MIMO, strong coupling, timevarying, distributed-parameter, and significantly uncertain behaviors in nature. In recent years, many researches have been made in order to develop models for flash furnace on-line control and further realizing optimization control (Davenport and Partelpoeg, 1987). However, most models developed for the furnace are mainly based on the mechanism of metallurgical reaction process (i.e. material balancing

calcuJation that between input material and output heat balancing caJculation between heat generation and heat loss of the flash sme lting process) and math-based parameter estimation techniques. The resulting models based on the balance mechanism are too complicated to be applied for a real-time optimization purpose, and at sarne time, the models haven't adaptive ability for the change of environments. The paper presents an approach that uses neural nem'ork technique to establish the neural net\lflork tnodels of the furnace (Wan and Huang, 1998). The models can approach exactly the process and is adaptable to the process of the furnace. After modeling, the paper studies the industrial site implementation of steady state optimizing control for the furnace. The result shows that optimization provides a better effect on saving energy. material~ and

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dry

concentrate

. . .- - -

~---~--.I....-

+ - - - - - 7 Vv"ater

stream

heavy oil

settler

Fig.I. Industrial flow of flash furnace

2. INDUSTRIAL PROCESS AND ITS CONTROL SYSTEMS

2.1 Industrial Process Flash smelting is a pyrometallurgical process for smelting metal sulphide concentrates. It is used primarily for copper sulphide concentrates, but it is also used as a significant extent for nickel sulphide concentrates.

Flash furnace is the key equipment that changes sulphide concentrate into matte. The industrial flow of flash furnace is shown in Fig.I. It contains the following five major components: • concentrate burners \vhich combine dry concentrate feed with 02-bearing blast and direct the mixture in suspension form dOWl1""rards into the furnace; • reaction shaft (RS) where most of the reaction between O 2 and the dry concentrate takes place; 4a settler where molten matte and slag collect and form separate layers; • slag cleaning area which makes the slag further depletion and recovery nickel metal and others valuable metals; • an off-take for removing S02-bearing gases from the furnace; Flash smelting consists of blowing fine, dried sulphide concentrate and silica flux and dust ,"vith air, oxygen-enriched air or oxygen blast into a hot (=1460±30K) furnace. Entry of these materials into the hot furnace causes the sulphide minerals (NiFeS 2 ) of the concentrate to react rapidly with the O 2 of the

blast. This) in turn, results in: • controlled oxidation of Fe and S from the concentrate;

• a large evolution of heat; • melting of the solids. The products of the process are~ • a molten nickel-rich Ni-Cu-Fe-S matte, 38-46°/() Ni-l-Cu; • a molten slag which contains iron oxide from iron oxidation plus gangue and flux oxides; • an off-gas which contains 802 from sulphur oxidation; The molten matte is the principal product of the flash furnace. It is always sent onto a converting furnace where its Fe and S are oxidized \vith air or oxygen~ enriched air. The slag inadvertently contains 0.5-10/0 nickel metal, is usually sent to a slag cleaning area for nickel recovery. The final nickel depleted slag is discard; the off-gas contains from 20 to 90 volume 0/0 S02 depending on the O 2 content of the input blast. It is cooled fraIn its furnace temperature, usually in a waste heat boiler for energy recovery; clean of its dust; and sent on to a S02 fixation plant. The SOz is most often fixed as H 2 S04 • Simplicity, efficient energy utilization and efficient S02 capture have made flash smelting the pre-

eminent modem process for copper and nickel smelting. It will probably continue to be used so well into the twenty-first century.

2.2 Control Systems Considering the advanced control technology of flash

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NEURAL NETWORK MODELING AND STEADY-STATE OPTIMIZIN...

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furnace~ the flash furnace control systems combine Distributed Control System (DeS) with the process. Blocks of distributed control systems are showed in Fig.2. The control system is composed of measuring instrument such as various sensors, input...output units, data processing units, controllers units, monitoring units and enforcelnent units. This configuration constitutes a structure of hierarchical control system. The purpose is modeling the system that consists of low layer controllers and industrjal process, and further realizing supervisor optimization control for the flash smelting process.

Cl CJ Token Ring

2.3 ,Veural Networks Selection and its Learning Algorithm controllers

subsystems

data processing high-density multi-functior subsystems JlO subsystems subsystems

Through the evaluation of ability of various neural networks modeling and their function approximation characteristics, and combined with the time-delay of the furnace. It is considered to select BP (BackPropagation) neural network as a model (pican and Alexandre, 1996).

Fig.2. Blocks of distributed control system

Define the error function:

E==t(Y1(k)-Yl(k»)2

(1)

yf

where (k) is the desired output and Yi (k) is the current output. BP neural network learning algorithm used is~

w(k + 1)

= w(k) +

1l(-:.) +

(3L'1.w(k - 1)

steady-state operation data including inputs data

(2) where w's are the weights that link between neurons, 11 is the learning rate, ~ is the inertia factor.

controllers

x.

2.4 Neural Network Modeling Flash furnace steady-state mathematical model is, on the condition of treating concentrate at a constant, when the furnace works steadily, the non-linear mathematical equations that describe the furnace's performance indexes and matte yield with the input ra\¥ materials. It is expressed in the following: (3)

where Yi denotes performance indexes or matte yield, Xl' X 2

, .•. ,

X n denote

input

variables,

such

Xi

yf

and outputs data from the plant. At the same time, it must be considered the system time-delay in collecting input output data, that is to say, the input data must correspond with the output data that these input data cause. Another attention point is that,

fJash furnace

neural networks

Fig.3. Structure ofNN Modeling industriaJ air

I indus. oxygen heavy oil

as

concentrate, flux, industrial air, industrial oxygen, heavy oil, dust and other materials~ respectively. f i (0) denotes the non-linear function relation between the performance indexes or yield and input

flux

rate of Fe&Si02 in slag

dust

variables.

Fig.4. Structure of Neural Network Modeling

The building of neural net\lYork models to the plant is shown in Fig.3. It must first obtain many groups of

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NEURAL NETWORK MODELING AND STEADY-STATE OPTIMIZIN...

yf

computation. Vlhen we get the plant output in the next period and then use it to nlodity the neural networks models.

50

i

47

~

44

~ E 41

38

10

20

30

40

50

60

70 time

Fig.5. Matte grade rnodeling from NN

~

E

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Through the comparison of leamjng abiHty of different neural network structures and different input variables, it is considered to adopt the m'o hidden-layer BP neural network as the model of the furnace. The structure of neural nenvork is shov.'l1 j n Fig.4, the nodes of neural nehvork are 5-5-5-]. The real operation and the learning results are showed in Fig.5~ Fig.6, Fig.7 and Fig.8.

1230 1220

1210

3, OPTIMlZING CONTROL

& 1200

§

1190

u

1180

~ E

1170 1160

3.1 Single Objective Optimization with Non-linear 10

20

30

40

50

60

70 time

Fig.6. Matte temperature modeling from NN

1.28

N

0

+U3 C J

(4) where: converting the engineering units into the sanle cost unit (Yuan per engineering unit) 0 1=0.98, u 2 =O.5, 0.3 =0.015. The technical limit value of perfonnance indexes~ matte yield and the limit value of optimizing input variables are shown in Table 1.

ro

0v

1990)

E=a]Fair +a 2 FOXY +u3 FO i} =a1c 1 +U2 C 2

1.38

.5

Lin~

Defining the energy consumption E as objective function

b.O

7;j

Constraints (Wan and

1.18

~

1.08

10

20

30

40

50

60

70time

Fig.7. Rate of Fe&Si02 in slag modeling from NN

So, the optimization problem can be formulated as the follo~'ing:

min E(c i ) 28

S. toO !.pi

min

"0

~

26

C j min

<

~

:g e

(5)

C j

30

rnax" S 'Pi (C) 1 = 1,2,3,4 i ::;
Cj

<

C j max·1

= 1 , 2 , 3 ,4 , 5

(6)

(7)

24

22 20 10

20

30

40

50

60

70 time

Fig.S. Matte yield modeHng from NN

matte yield is measured by material balanc.ing calculation. Through the training of neural networks, it builds up the prototype models of neural network.

yf

In the production of the furnace, the rea] outputs have a large time-delay with the input variable because the industrial process has a large time-delay and output is measured on X-ray analyzer and obtained from the material balancing calculation. At

y:n

this time, the neural network output is considered as the real-outputs for the optimization

To change above constraint conditions into the form g j (c j) :::; 0, First of all, to convert the optimization problem with non-linear constraints into the unconstraint optimization problem, utilize SUMT (Sequential Unconsrraint Minimization Technique) method to resolve the above optimization problem.

E·

= E + M -I

f {max [0,

g;

(c j

n 2

)

(8)

i=1

Utilizing gradient method:

(\ (k +]) ==

Cl

(k) + lli

8E* -"""1-

(9)

OCi

We obtained the results shown in Table 2. The E value of energy consumption: initial cost per hour £=7,393 .2(Yuan), optimal cost per hour

E

=6,385.5 (Yuan). The cost of saving energy consumption per hour = 1,007.7 (Yuan). The models

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NEURAL NETWORK MODELING AND STEADY-STATE OPTIMIZIN...

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Table 1 Performance indexes and optimizing variables

cp.max c.max 1 • 1 Q?i min

c;min

Matte Matte Heavy Fe/SiO z Matte Industrial Industrial Flux Dust Air Oxygen Grade Temperature in Slag Yield Oil DC (Cu+Ni) t/hour (nm 3 /hour) (nmJ/hour) (kglhour) tJhour tlhour 22000 12000 1250 1.3 50 1200 13.0 10.0 20 18000 10000 40 1.0 1150 850 9.0 7.50 rrab le 2 Optimization results

Industrial air (nm 3/hour)

111 =0.004

Industrial Oxygen (nmJ/hour)

112 =0.004

heavy oil (kg/hour)

113 =0.004

Jnitial value of penalty initial value of 0 t' I I all variables P Ima resu ts factor ffi 1 =O.006,m 2 =0.006 Cl =18292.05 Cl =19522.90 ffi 3 =O.006,m 4 =0.006 c 2 =12325.37 C"2 =10018.69 ID s =O.006,m6 =0.006 C 3 =974.10 C =1105.44

flux

(t/hour)

't14=O.004

ill?

dust

(t/hour)

115 =0.004

ID 9 =O.006~

Optimization variables

Learning rate

3

=O.006,m g =O.006

matte yield (tfhour)

c 4 =11.10

64 =13.13

Cs =7.46

13 5 =10.13

Y=25.89

Table 3 Optimization results (matte yield>24 ton per 100 tone concentrates)

Optimization variables (nm 3 /hour)

Industrial air

Learning Initial value of penalty Initial.value of 0 timal results factor variables P rate 111 =0.004 ID 1 =O.006,m2 =O.006 Cl =18292.05 Cl =19652.6

Industrial oxygen (nm3 /hour)

112 =0.004

m 3 =O.006,m4 =0.006

Heavy oil (kg/hour)

113 =0.004

ffi s

=O.006,m 6 =O.006

c 3 =974.10

C2 = 10453.1 C3 =1127.5

Flux (tfhour)

114 =0.004

m 7 =O.006,m g =O.006

~=11.10

C4 =13.21

Dust (t/hour)

Tfs =0.004

m 9 =O.006~

<:5=7.46

C5~10.2

C2 =12325.37

Y =25.89

Matte yield (t/hour)

I

I

[-safety check

f1ash fumace

controllers

~

y~ J

f--- ----- ----------. ----------- --------1 Xi

I'I--T--i_ _----..I

1

modeling block ,

'

~ _ .... - - - - - - ... - - - - - - - -- ..... - ~ ~ - -- ... - - _ .... - - - -- - - -~..!

Fig.9. Structure of on-line ~'N modeling and optimization control that used above adopt 30 day data \'\rhen the furnace worked steadily. So~ the cost of saving energy

consumption in 30 days = 725,544(Yuan). It is considered that the furnace works regularly 10 month

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(300 days) in a whole year, the cost of saving energy consumption = 7,255,440(Yuan).

3.2 Multi-Objective Optim,ization with 1von-linear

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works continually for five years and has accumulated a lot of expert knowledge and operator experience. It is considered to incorporated fuzzy logic method with the neutral netv..·ork technique in order to utilize all information and knowledge during operating the flash furnace (Astrom et at, 1986)..

Constraints.

The energy consumption is also considered as an objective function. Based on the above optimizing results~ we only have a try to increase the matte yield a little in the constraints. The results show, the energy consumption can increase a Jot as matte yield increase. This way can supply a whole consideration for man's decision to the furnace. The E value of energy consumption: initial cost per hour E=7,393.2 (Yuan). Optimal cost per hour

E =6,626.3 (Yuan), the cost of saving energy consumption per hour =766.9 (Yuan). The models that used above adopt 30 day data \vhen the furnace worked steadily. So, the cost of saving energy consumption in 30 days ~552,168 (Yuan). It is considered that the furnace works regularly 10 rnonth (300 days) in a whole year, the cost of saving energy consumption =5 ~521 ,680 (Yuan).

6 ACKNOWLEDGMENT The authors gratefully acknowledge Mr. Shi-wen FENG, Mr. Yi-chuan ZHANG, and Mr. Shu-liang W ANG and many other engineers and operators of flash furnace workshop of Jinchuan Non-ferrous Metals COlllpany for their contribution in computer implementation, industrial tests, and providing production knowledge to the authors.

REFERENCES

Astrom K.J., Anton J.J. and Arzen K.E. (1986). Expert control Automatica, 22(3), pp277-286. Davenport W.G., Partelpoeg E.H., (1987) Flash SI11elting Analysis, Control and Optintization,

4. OFF-LINE ADJUSTMENT AND ON-LINE OPTIMIZATION CONTROL

Pergamon Press. Pican N. and Alexandre F . (1996)~ Artificial neural net\vorks for the presetting ofa steel temper mill. IEEE~ Expert intelligent Systems and their App/ications~

11(1): 22-27.

When performance indexes and matte yield are

Wan B. W. and Huang Z.L.(1998). In: On-line

available by NN and input variables have been attained through the optimization computation~ .it provides guidance for operator, this is called off-line adjustment. On-line optimizing control operates on modeling, models updating and input variable optimization computation automatically and periodically. The results of the computation are checked for safety and input variables are acted as the set-point of controllers. The structure of steadystate optimization control for the furnace is shov-.'n in Fig.9. In March 1998 to May 1998} 'live have industrial experiment of above study results, the way is off-line adjustment. The result shows when the furnace worked steadjly~ the optimizing control can overcome the slow fluctuatjon of dry concentrate's ingredient. The optimization results fit the real operation and save energy consumption better.

Hierarchical Steady-state Optimizing Control of Chap~ 9, Science Press~ Beljing. (in Chlnese). Wan B. W.~ Lin J., (1990) Steady-state hierarchical control of large-scale industrial processes: A survey, Act Automatica Sinica, 16(2): 186-192. (in Chinese).

Large-scale Industrial Processes,

5. CONCLUSIONS In this paper, the study and industrial site experiment for the optimization of the furnace have obtained a satisfactory result. It takes a base in carrying out online steady-state optimizing controJ for the furnace. The next step is to make a further implementation to the furnace and resolve new problems that occur in the production~ On the other hand~ the flash furnace

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