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Intelligent Dynamic Modeling for Online Estimation of

Burning Zone Temperature in Cement Rotary Kiln* Ping Zhou and Meng Yuan

State Key Laboratory of Synthetical Automation for Process Industries

Northeastern University

Shenyang, Liaoning Province, China

[email protected]

* This work is partially supported by NSF Grant #61104084, #61290323, #61333007, IAPI Fundamental Research Funds Grant #2013ZCX02-09, and theFundamental Research Funds for the Central Universities Grant #N130508002 to P. Zhou and M. Yuan.

Abstract - Cement rotary kiln is a complex multivariable,large-disturbances and nonlinear system which is full of mass

transfer, heat transfer, and physical and chemical reactions. The

burning zone temperature (BZT) in cement rotary kiln is a very

important production index and has a significant role on the

quality of the clinker. However, the BZT is generally difficult to

be measured online using the conventional instruments. Although

the BZT can be detected by using the expensive infraredpyrometer which located at the kiln head hood, it generally loses

veracity due to the complex dynamics of the cement rotary kiln.

Obviously, such an inaccurate measurement may guide the

operator to do some improper operations in practice. To attack

such a practical engineering problem, an intelligence-based

dynamic soft-sensor modeling approach is proposed to online

estimate the BZT in cement rotary kiln in this paper. The

proposed approach mainly includes two digital filters which are

used to pre-process the original measurement data, and an

intelligent CBR soft-sensor system which is adapted to online

predict the BZT in time, according to the measured secondary

variables. At last, industrial tests have been performed to

demonstrate the good estimation performance of the proposed

method for a real cement rotary kiln process.

Index Terms - Burning zone temperature, Cement rotary kiln,

Dynamic soft-sensor, Case-based reasoning, Digital filter.

I. I NTRODUCTION

Rotary kiln is a kind of large scale sintering device widely

used in various process industries, such as metallurgical,

cement, refractory materials, chemical and environment

protection [1-3]. In the cement production industry, the

cement rotary kiln decomposition is the most important unit,

and its operating status serious affects the output, quality,

energy consumption, and environment pollution. The

automation problem of such complicated processes remains

unsolved because of the following inherent complexities. It isa multivariable nonlinear system with strong coupling. The

complicated working mechanism includes physical change

and chemical reaction of material, procedure of combustion,

thermal transmission among gaseous fluid, solid material fluid

and the liner. Moreover, the key controlled variable of burning

zone temperature is difficult to be measured. In fact, most of

rotary kilns are still under manual control with human

operator observing the burning status; this is especially true in

China. As a result, the product quality is hared to be kept

consistent and energy consumption remains high. Although

several advanced control strategies including fuzzy control,

artificial neural network based control and predictive control

have been introduced into process control of rotary kiln, all

these researches focused on trying to achieve complete

automatic control without human operators [1, 3-6]. As amatter of fact, the boundary conditions of a cement rotary kiln

change heavily. For example, the material load, water content

and components of the raw material slurry vary frequently and

severely. Moreover, the offline analysis data of components of

raw material slurry reach the operator with large time delay.

Thus complete automatic control without human operation for

such a complex process is unpractical.

For the cement rotary kiln process, the most difficult

control problem is that the key technical index, burning zone

temperature (BZT), is difficult to be measured online using

the conventional instruments. Even if some factory use

expensive infrared pyrometer located at kiln head hood to

measure the burning zone temperature directly. However, due

to the complex dynamics of the cement rotary kiln, such a

measurement generally loses veracity, which will misadvise

the operator to do some improper operations to the running

process. To attack such a practical problem, this paper

develops an intelligent soft-sensor modeling approach for

burning zone temperature using case-based reasoning (CBR)

[7-10] estimation technique. Industrial test and results have

show the effectiveness and validity of the proposed method.

II. PROCESS DESCRIPTION OF CEMENT R OTARY K ILN

Rotary kiln is one of the key equipments in a cement

industry used to convert calcareous raw meal to cement

clinkers. The kiln, as shown in Fig. 1, is a long and complextunnel, with a cylindrical shape. The cement rotary kiln

process under study can be described as follows:

Raw material slurry is sprayed into the rotary kiln from

upper end (the kiln head), the coal powders from the coal

injector and the primary air from the air blower are mixed into

bi-phase fuel flow, which is sprayed into the kiln head hood

and combusts with the secondary air, which comes from the

cooler. The heated gas was brought to the kiln tail by the

induced draft fan, while the material moves to the kiln head

978-1-4799-5825-2/14/$31.00 ©2014 IEEE

Proceeding of the 11th World Congress on Intelligent Control and AutomationShenyang, China, June 29 - July 4 2014

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Coal

Powder

Air

Clinker

Cooling

zone

Bunring

zone

Reaction

exothermic

zone

Decomposite

zone

Drying

zone

Pre-

heating

zone

Raw

material

Head-

end

of kiln

Back -end

of the

kiln

Cement Rotary Kiln

Fig. 1 Schematic diagram of cement rotary kiln

?

Input

VariableCement Rotary Kiln Process

Data Pre-

processing

CBR

Soft-sensor

Model

Learning Algorithm

Burning zone temperature

(Sampled value)

Estimated value

Fig.2 The proposed intelligent soft-sensor modeling strategy for burning zone

temperature

1 z

1 z

1 z

1 z

1( ) x t

2( ) x t

3( ) x t

3( 1) x t

2( 1) x t

1( 1) x t

4 ( 1) x t

( ) y t

4( ) x t

CBR System for

Soft-Sensor of

BurningZone

Temperature

1 z ( 1) y t

Fig.3 Dynamic ANN soft-sensor model

via the rotation of the kiln and its self weight, in counter

direction with the gas. Raw material is carried along the kiln a

very low speed. Near the middle of the kin is the firing zone,

where gas burners are placed to impose a given temperature

profile. In a kiln, the back-end is responsible for the

calcification of meal before the main baking, so if the

temperature of back-end is more than the acceptable range, the

baking will be done before entering the burning zone, and vice

versa is to happen for lower the temperature to be. At the

burning zone, the high temperature melts the classificated

meal. Then the main chemical reactions between silicates andoxygen of the air occur. A part of the combustion gases is the

Co gas produced here. Finally, the cement crystals are made

and go out from the kiln as the clinker [1-3]..

The burning zone temperature (BZT zt B ) in cement

rotary kiln is a very important production index and has a

significant role on the quality of the clinker. According to the

physical phenomena taking place in the rotary kiln, the main

process variables (measurable and adjustable) that affect the

BZT are coal (fuel) feed rate F C (t/h), exhaust air feed rate

F A (m/s), raw marital feed rate F R (t/h), and kiln rotation

speedS K (r/m).

Increasing F C will enhance the reaction in the rotary

kiln, causing the lame to burn well and the BZT to rise.

Increasing F R will increase the reactant in the rotary kiln

and cause its temperature to rise; however, when F R

increases to a certain extent, it will cause the BZT to

decrease.

Increasing F A will speed up the reaction in the kiln and

increase the exhaust emission, causing the kiln

temperature to rise. However, when F A increases to a

certain extent, there will be insufficient air for

combustion, and incomplete combustion will generate

CO, causing the sintering temperature to decrease.

S K can be adjusted by the volume of the input materials

to rotary kiln, feed rate of raw mix. In the case of more

input materials, S K should be adjusted to complete the

burning process. On the other hand, S K can itself

interfere in the kiln temperature.

III. DYNAMIC SOFT-SENSOR MODELING FOR BZT

The key problem of closed-loop control of the BZT is that

it is cannot be measured online with conventional methods.

The most effective method of overcoming it is that employ

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soft-sensor technique to online estimate the BZT. Due to case-

based reasoning (CBR) has good ability to identify and

control complex nonlinear systems [6-10], the CBR-based

soft-sensor dynamic modeling approach is therefore employed

to develop a BZT soft-sensor in this paper.

The CBR utilizes the specific case information availableas historical precedence for proposing solutions to current

problem. The most important aspects of the existing cases are

first stored and indexed. New problem situations are then

presented and similar, existing cases are identified from the

knowledge base. Finally, the previous problem solutions are

adapted and the revised solutions are proposed for the current

situation [7-10].

The proposed CBR based soft-sensor modeling approach

for dynamical estimating the BZT is shown in Fig.2. It mainly

consists of a process data pre-processing module and a

dynamic CBR soft-sensor system.

A. Data Pre-ProcessingIf the original secondary variables O { , , F F C A

, } F S R K are used for soft-sensor modelling and calculating

directly, it will cause some adverse influences on estimation

precision. Therefore, digital filtering technique is employed to

pre-process these original data.

a) Noise peak filtering algorithm [11]: It is used to

eliminate the noise peak jump.

O E

E O

E O

E E

O E

E E

If ( ) ( 1)

Then ( ) ( ),

If ( 1) ( )

Then ( ) ( 1)

If ( ) ( 1)

Then ( ) ( 1)

t t

t t

t t

t t

t t

t t

where t denotes sampling time, E ( )t denotes the pre-

proceed data by the noise peak filter, is the maximal

allowed variety value of O ( )t at successive sampling time.

b) Average moving filtering algorithm: It is used to

eliminate the lower and high frequency noise fluctuation.

E EF F

( ) ( )( ) ( 1)

t t N t t

N

where N is the length of average moving filtering.

B. CBR-Based Dynamic Soft-sensor Algorithm

Fig.3 illustrates the CBR-based BZT soft-sensor model,

where 1 2 3 4, , , x x x x present the secondary variables of

, , , F F F S C A R K , y stands for the main variables, i.e. the

burning zone temperature ( zt B ). This means that the CBR

soft-sensor model consists of 9 inputs and 1 outputs, therefore

the dynamic input-output relation of the soft-sensor can be

represented as follows.

1 2 3 4 1

2 3 4

( ) [ ( ), ( ), ( ), ( ), ( 1),

( 1), ( 1), ( 1), ( 1)]

X t x t x t x t x t x t

x t x t x t y t

It is noted that to capture the system dynamics, the time

series and time delays of the input and output variables have

been taken into account in the proposed dynamic CBR model.

The reasoning flow of CBR-based soft-sensor mainly

includes case representation, case retrieval and case matching,

case reuse, and case revision.

a) Case representation. As shown in Table I, the case

representation consists two parts, case descriptors and

solutions of cases. The case descriptors include the coal (fuel)

feed rate ( ) F C t , the exhaust air feed rate ( ) F A t , the raw

marital feed rate ( ) F R t , the kiln rotation speed ( )S K t , .the

past value of ( 1), ( 1), ( 1) F F F C t A t R t , ( 1)S K t , and

( 1) zt B t , which defined as 1 2 9, , , f f f , respectively. The

case solution is ( ) zt B t which is needed to be estimated.

b) Case retrieval and case matching. Let the description

characteristics of the current cement rotary kiln process isT T T T

1 2 9( , , , ) F f f f . Define the case similarity between the

current rotary kiln system and the k th (1 )k N case of the

case base :{ }k k k C F J as SIMk , which is given by

9 9T

,

1 1

T

,

,

,

SIM ( , ) sim ( , )

sim ( , ) 1max( , )

T

k k i i i i k i

i i

i i k T

i i i k T

i i k

F F f f

f f f f

f f

(1)

where coefficients j denote case feature weights that

generally attained by expert experience. The cases that satisfy

the following condition

T

z

T

1, ,

T T

1, , 1, ,

SIM ( , )

0.95, max (SIM ( , )) 0.95

max (SIM ( , )), max (SIM ( , )) 0.95

k k

k k k m

k k k k k m k m

F F y

F F

F F F F

(2)

will be retrieved as the matching cases with ranking in

descending order of SIMk .

c) Case reuse. Suppose that the matching cases areM M M:{ }, 1, ,r r r

C F J r R , where R is the number of

matching cases. The case solution TS of T T T1 2( , , F f f T

9, ) f can be obtained by

3T M

1

3TT

1

M

1

(SIM ( , ) )

, 3

SIM ( , )

, 3

r r r

r

r r

r

F F s

if r S

F F

s if r

(3)

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TABLE I

CASE REPRESENTATION

Case descriptor ( F ) Case solution (S )

1

2

3

4

5

6

7

8

9

1 s

( ) F C t ( ) F A t ( ) F R t ( )S K t ( 1) F C t ( 1) F A t ( 1) F R t ( 1)S K t ( 1) zt B t ( ) zt B t

TABLE II

I NITIAL CASE BASE OF CBR SOFT-SENSOR SYSTEM

Case descriptors ( F ) Case solution (S )

( ) F

C t ( ) F

A t ( ) F

R t ( )S

K t ( 1) F

C t ( 1) F A t ( 1) F R t ( 1)S K t ( 1) zt B t ( ) zt B t

167.42 28.82 9.02 3.633 166.22 27.852 8.92 3.734 1451.7 1471.1

166.64 28.23 9.07 3.834 167.34 28.21 8.87 3.721 1463.2 1473.3

168.76 27.42 8.46 3.656 166.46 27.82 8.25 3.726 1510.2 1500.1

167.83 27.62 8.71 3.728 168.23 28.22 8.93 3.672 1502.7 1501.3

… … … … … … … … … …

0 10 20 30 40 50 60 701475

1480

1485

1490

1495

1500

1505

1510

1515

B u r n i n g z o n e t e m p e r a t u r e , ℃

Sampled data

Estimated value

Sampled value

Fig.4 Testing results of burning zone temperature estimation with the

proposed method

10 20 30 40 50 60 700

1

2

3

4

5

6

7

8

9

10

Sampled data

E s t i m a t e d e r r o r

Fig.5 Estimating error of with the proposed method

d) Case revision. The case revision is a very important

issue in the CBR-based decision system. After the grinding

system obtains the solution TS as the estimation of BZT for

the CBR soft-sensor system, if the actual BZT is better, it

confirms that the formerly estimation is reasonable. Therefore,

there is no need to carry out case revision. Otherwise, it needs

to revise the case and store this revised new case. The detailed

revision procedures can be consulted in Ref. [3].

1480 1485 1490 1495 1500 1505 15101480

1485

1490

1495

1500

1505

1510

Estimated value

S a m p l e d v a l u e

Fig.6 Scatter diagram of the burning zone temperature estimations with

proposed method

IV. I NDUSTRIAL APPLICATIONS

In this section, we will use the above proposed CBR

based dynamic soft-sensor method to model a cement

production line. In the past, the BZT could not be obtained

online and closed-loop control for it could not be realized in

this cement production line. Using the proposed data filter

method on the sampled data, collect 110 groups of sampled

data from the industrial process was collected to develop the

initial case base for the CBR soft-sensor system. A partial

sequence of the case data of the initial case base is shown in

Table II.

The prediction effect of the developed dynamic CBR-

based soft-sensor under a wide range of operation conditions

is shown in Fig. 4 and Fig.5. It can be seen that the developed

CBR soft-sensor system obtains satisfactory performances. No

matter what operation conditions are changed in the cement

rotary kiln process, the output of the developed soft-sensor

can estimate the actual burning zone temperature very well.

According to statistical analysis, the average absolute

estimation errors are small than 3.2.

The performance of estimation can also be visualized by

plotting the measured results against the predicted ones, which

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as shown in Fig.6. The abscissa of this figure is the value of

actual measurement, and corresponding coordinate is the

value of estimation with the proposed algorithm. The closer

distribution of splashes gets to the black diagonal line, the

better estimation effects are realized. When the estimated

values match the measured ones, all points would lie on adiagonal. It can be seen from Fig.6 that the proposed modeling

method gives the best estimation of the burning zone

temperature. Although some points are relatively far from the

diagonal line, the prediction of the proposed model is closer to

the actual value very well. Such results show that this

predictor can satisfy the requirement of BZT control.

IV. CONCLUSIONS

The burning zone temperature in the cement rotary kiln

process is a very important technical index, on which the

sinter quality mainly relies. However, due to the complex

dynamic charactertics in terms of nonlinearity, large timedelay and time-varying, it is difficult to online measure the

burning zone temperature using conventional instruments. In

this paper, an intelligence-based dynamic soft-sensor

modeling approach for burning zone temperature using CBR

estimation technique is proposed in this paper. Industrial test

results show that the developed soft-sensor can online

estimate the burning zone temperature very well.

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[4] M. Jarvensivua, K. Saari, S. L. Jamsa-Jounela, “Intelligent control system

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[8] J. L. Kolodner, “An introduction to case-based reasoning,” Artif. Intell.

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