Abstract—In order to improve the production stability of cement Precalciner Kiln calcination process, it is necessary to conduct in-depth analysis of the calcination process, knowledge of the process in running state and laws. To save energy and achieve stable production, we establish the simulation model of the calcination process used to find effective control methods. In view of the calcination process parameters of complex mathematical model is difficult, so we expressed directly using neural network method to establish the simulation model of the calcination process. Choosing reasonable state and control variables and collecting actual operation data to train neural network weights. Constructed two types of neural network BPNN and RBFNN based models, both achieved good fitting results. RBFNN based model can reach very high fitting results, but the BPNN based model has good generalization ability. So the BPNN based model can be used as simulation model of the calcination process for exploring new control algorithms.

I. INTRODUCTION hina is the world's largest cement producer. Now, China is vigorously promoting the relatively energy-efficient cement Precalciner Kiln technology. However, the

technology especially calcination process contains the complex physical, chemical, mechanical, electrical process and so on, non-linearity, lag and other notable features, there is no precise mathematical model to represent. So precise control is very difficult to present, its production depends on the operators to adjust parameters now. In order to explore more effective automatic control methods, it is necessary to establish the model of calcination process firstly. Neural network method does not require a mathematical model of the mechanism of the object, but also a reasonable description of the characteristics of the object, with parallel processing, learning ability, robustness and good features, so the modeling of complex systems has been widely used. In this paper, calcination process, was raised the yield and quality prediction models based on BP and RBF neural network. First, the process operating parameters, correlation was analyzed; and then we used on-site production data collected to establish the

Manuscript received April 2, 2010. This work was supported by Anhui

Education Department Nature Science Foundation under Grant KJ2010B224 and in part by KJ2010A352.

B. S. Yang is with the Department of Computer Science and Technology, Suzhou University, Suzhou, Anhui 234000 China (phone: +86-557-2871028; e-mail: bsyang@ymail.com).

H. M. Lu is with the Department of Computer Science and Technology, Suzhou University, Suzhou, Anhui 234000 China (e-mail: lhm1100@ 126.com).

L. L. Chen is with the Department of Computer Science and Technology, Suzhou University, Suzhou, Anhui 234000 China (e-mail: liming8231@ 163.com).

quality of clinker calcination predictive models and carry out generalization ability test, finally the BP method and RBF method were compared to obtain a satisfactory model of calcination process.

II. BPNN AND RBFNN

A. BP Neural Network BP neuron transfer function is the non-linear function; the

most commonly used function f is the logsig and tansig function. The output layer can use a linear function of purelin, its output is

)( bWpfa += （1） BP networks are generally multi-layer feedforward neural

networks, information flows from the input layer to output layer. BP network learning is divided into two stages: the first is to enter a known learning samples, by setting the network structure and the previous iteration of the right value and the threshold value, from the network's first layer of backward calculation of the output of each neuron; then weight and threshold are modified to move from the last layer of the right to calculate their value and the threshold value on the overall impact of the error, the error transfers back layer by layer, whereby pairs of weights and thresholds will be modified. These two processes alternately repeated until the convergence so far. In the MATLAB neural network toolbox, newff used to create a BP network. The standard BP algorithm is a gradient descent learning algorithm, it amend the value of the error performance function in right direction with the gradient of the anti-direction.

B. RBF Network RBF (radial basis function) method is a kind of high space

interpolation technique, the output expression is )()( bpWradbasbpWfa ⋅−=⋅−= （2）

where radbas is radial basis function, usually using the Gaussian function.

]2

exp[)( 2

2

i

ii

cxxR

σ−

−= ， mi ,...,2,1= （3）

where x is the n-dimensional input vector; ic is the center of

basis function i, and it has the same dimension vector x ; iσ is the perception variable i, can be chosen parameters freely, its decision the center width of basis functions; m is the

BPNN and RBFNN Based Modeling Analysis and Comparison for Cement Calcination Process

Baosheng Yang, Hongmei Lu, and Lili Chen

C

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Third International Workshop on Advanced Computational Intelligence August 25-27, 2010 - Suzhou, Jiangsu, China

978-1-4244-6337-4/10/$26.00 @2010 IEEE

number of perceived neurons. The function is smooth and good, radial symmetry, simple form.

RBF network is also a feed-forward back-propagation network, it has two network layers. Hidden layer is radial grassroots level; the input layer is linear layer. RBF network is a local approximation network. For each training sample, it was only a small number of pairs of weights and thresholds to modify, and training faster. Therefore, it is applied to the pre-kiln system, real-time control, with a strong advantage. The BP network in the training process needs for ownership of the network value and the threshold to be modified. BP network is a global approximation neural networks, with the former compared to the learning speed is very slow. RBF network approximation capability, sorting capability and learning speed is superior to BP network. In the MATLAB neural network toolbox, there is available newrbe (or newrb) to create an RBF network[1][2].

III. THE PROCESS OF CLINKER CALCINATION Clinker calcination process mainly consists of preheater,

calciner furnace, rotary kiln and grates cooler four parts. In the case of precalciner kiln system with five-stage suspension preheater, the whole calcination process is divided into two directions, one is the materials flow from the top, and the other is the flue gas flow from the bottom[3].

A. Two Opposite Processes of Calcination Process In the materials flow, raw materials were feed into the

connection pipe between the first preheater C1 and the second preheater C2. Materials were blown in suspended state by the hot gas from the bottom-up. At the same time raw materials exchange heat with the hot gas. Then it was put into preheater C1 by hot gas. After separating from the airflow, it is feed into inlet pipe of preheater C2 through the bottom barrel tube of the preheater C1. And then, materials reach the preheater C3, C4 in the same way. Raw materials were put into the calciner furnace after the separation with the airflow in the preheater C4. At this point, raw material powder becomes hot through the five-stage preheater. The fuel injected into the calciner furnace with the first-air. Thermal material is decomposed by the furnace, with the air flowing into the end of the preheater C5. At this point, the rate of decomposition of material powder in the general is about 90%. After gas-solid separation, the decomposed powder flows into the rotary kiln. With the gas coming from the bottom up, the heat that breaks down the raw materials is provided by the coal burning stove. Pre-decomposed raw materials follow into the kiln. Because of kiln-place tilt and rotation, materials constantly move. In the kiln, the material was heated into clinker by the reverse flowed high-temperature gas. Finally, the clinker falls into the bottom of the grate cooler through the hood of kiln, and then unloads into storage by air cooling.

The gas flows and the materials flow are essential opposite. One part of the calciner furnace requires air coming from the exhaust gas of grate cooler. This air is named tertiary-air, with a temperature range of about 700 ℃ ~ 850 ℃. Another part of the air comes from the gas of chamber in the back-end of kiln. The airs for rotary kiln combustion come from the first-air

with coal and the secondary-air from grate cooler[4].

B. Production Control Requirements of Calcination Process After cement raw material is put into the precalciner kiln

system, it needs to go through various physical and chemical reactions. These physical and chemical reactions required temperature environment and the need to heat are vastly different. Thus, in the production process, we must ensure the stability of the chemical composition of raw material, raw material feeding amount, the fuel heat value of the composition and fineness, fuel feeding amount and equipment operation, etc. These are important principles of cement production. In order to ensure the quality of cement, we need to pursuit three balances of the production process:

(1) Material balance: Raw materials heated by suspension preheater, are sent into the calciner. The rate of decomposition of carbonate in the furnace gets up to 85% -95%. It is hoped that materials stay at a certain level in the process of production in reality.

(2) Gas balance: The gas flow is in the opposite direction with materials and exhausts from the upper part of the preheater. The gas sucked in furnace and kiln relies on negative air pressure, which can be adjusted by control valve. The first-air is sucked in furnace or kiln with the coal.

(3) Heat balance: The needed fuel of the whole system is poured about 60% into the furnace and 40% into the rotary kiln. Heat consumption of unit clinker is dropped to below 3000kJ/kg and the thermal efficiency is raised to above 60%.

IV. THE MAIN ELEMENT ANALYSIS AND SELECTION OF CLINKER CALCINATION PROCESS

A. System Analysis Cement production is a complex industrial process

involving mass transfer, heat transfer and physical chemistry reaction, and its stability of the production directly impacts the quality of cement. From the foregoing analysis of this paper, it can be seen that cement clinker calcination system has several notable features as follows:

(1) Complex physical and chemical process: The production should go through the process of combustion reaction, heat transfer of materials, water evaporation, chemical decomposition of water, the decomposition of carbonate, the decomposition and compound of the oxide, solid-state reaction, and liquid-phase reaction, etc. Its basis theory consists of thermodynamics, dynamics, heat transfer, fluid dynamics and the crystallization of the mineralogy, and so on.

(2) Many interference factors: There are many factors in the calciner process of cement production. From the perspective of process control a precalciner kiln can be seen as such a system: certain parameters of raw materials and operation act on the parameters of the device (collectively referred to as process parameters), resulting in the corresponding parameters of state. The parameters of the total number can be as many as several dozens, with functional relationship expressed as:

),,,,( tSOEMfI = (4)

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where I is production targets, M is the raw material parameters, E is the equipment operating parameters, O is operating parameters, S indicates the system running state, t represents time[5][6].

)())( , , (

IndicationStateequipmentoperatingmaterialsRaw

→ (5)

(3) High non-linearity: When running at stability PCK system is in a dynamic balance as a whole. The impact of each parameter maintains a balance in this process. The changes are all non-linear in the process of running, which further increased the level of non-linearity of the system.

(4) Large lag: The total time from the raw powder being put into the suspension preheater to the cooler unloading clinker into silo, is about 30 minutes. Gas pumped from the cooler to be eliminated out of the preheater would take about 3 minutes. Only in the end of the cooler can one observe some state of the calcination. The above mentioned process indicates a large lag in the calcination process. In addition, some characteristics of clinker are obtained only by testing. Workers generally test clinker once in every 2 hours, and the testing process will take about 1 to 2 hours. According to the processes of the hybrid operation, it is necessary to take 1.2 to 3.2 hours to get most of characteristics of the clinker.

(5) Large interference exists in data acquisition: Because of the impact of the device, environment, measurement and man-made factors, collecting raw data from the process of industrial production is vulnerable to interference by noise, negligence and missing data points. The mass of raw data is often incomplete, with noise (including error or existing

isolated points which deviated from the desired values) and the lack of consistency.

B. Main Element Analysis of the System The selection of control and state variables are based on

quality assurance of cement clinker under the premise. According to our analysis, the entire system should be considered to select these variables. Only a link for the single point of control to ensure the quality of clinker is meaningless. Therefore, we have adopted the following principles:

(1) The variables we selected should be measured directly or indirectly.

(2) Select the variables of normal operation of the production to make the production process more stable. Under the unusual circumstances, such as testing, kiln drying, firing, kiln hanging skin, heading, and stopping the kiln, we should use manual control.

(3) Select several major factors as the objects for simulation study. This is to make sure that system stability depends on major factors and to avoid non-major factors changing to major factors.

Present new dry kiln production control is used mainly distributed control system, with a high degree of automation. Therefore, we are able to capture real-time production process of solid and gas flow, temperature, pressure, gas composition and many other parameters. Table 1 is from the Guangxi cement kiln system control room, the main parameters of real-time acquisition. These data basically met the demand parameters of this study. We simplify the system, and the model is shown in Figure 1.

The dashed arrows indicate the flow of materials, small

black arrows indicate the flow of the air, and bold arrows indicate the flow of coal. As can be seen from the figure, the major factors which impact the system are the air, coal and materials. Therefore, the selected variables for control are focused on air, coal, and materials. According to the analysis of the preceding sections we select state variables as follows. Among them, furnace export is a meeting point of PCK system, which reflects a number of integrated indicators for the system state. As a result, we select the temperature of the furnace export as a state variable in this paper. Oxygen content of exhaust gases can indicates the extent of calcine. Lack of oxygen means materials being not fully calcined. Excessive oxygen-containing indicates excessive ventilation, which increases heat loss. Moderate oxygen assures both to full

calcine and at the same time avoiding excessive heat loss. Therefore, this paper selects oxygen content gas of cyclone export as another state variable. Keeping theses two state variables within a reasonable range is the goal of production.

Raw

material Coal for furnace &

first-air

Pre-

heater

Calciner

furnace

Rotary

kiln

Grate

cooler

C1

Tertiary-air

Coal for kiln &

first-air

Clinker

Fig. 1. Operation state of calcination process.

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V. CLINKER CALCINATION PROCESS MODELING

A. Mathematical Modeling Method Adapt mathematical methods to optimized design, usually

go through several stages: first is to establish the mathematical model to reflect the actual process of a engineering system; second select appropriate optimization method, and prepare the corresponding computer program; finally debug on the computer, modify the model, obtain the optimal solution, then analyze and judge the calculation result, and make the optimal design[7][8].

Some researchers believe that dynamics of the pre-decomposition of the rotary kiln system can be described by partial differential equations. The equations included the system's main variables, such as gas, materials, and substrate materials changed with the kiln location and time, etc. It also included the various variable involved chemical reactions. The partial differential equations provide a rotary segmentation model by the discrete form for the location of the rotary kiln. These dynamic characteristics of rotary kiln can be analyzed by computer. Through a variety of simulation experiment to study the changes in the various operating variables, as well as the response under the influence of various disturbances. Such simulations can reproduce the actual production process with a small deviation. However, in the simulation test, this time-dependent control system analysis and the reality is much slower in comparison. Currently, the feasibility of parameter values assumed in the model lacks in-depth study.

As the mathematical model in cement rotary kiln system involved very complicated problems. It mainly because many

coupled factors, it has a serious non-linear; and a lot of the process mechanism is not clear. Therefore, the design for such a complex problem is difficult to design a mathematical model. Therefore, this study tried another way.

B. Neural Network Method In view of the foregoing pre-kiln system complexity, as well

as the advantages of artificial neural networks, this paper uses artificial neural network (ANN) for the pre-kiln system modeling, as shown in Figure 2. BP-based neural network model can better describe the non-linear system performance, and has a capacity of generalization. In Figure 2

5,2,1),( …=itui , are the control variables of the system, including raw materials, coal-fed for furnace, coal-fed for kiln, rotary speed of kiln and negative pressure of C1 export. Variables 2,1),( =jtx j , are the temperatures of furnace export and oxygen content of exhaust, respectively.

Modeling data are as shown in Table Ⅱ , which are

real-time data from a new dry-process cement plant 5000t/d production line of Guigang City, in Guangxi province. Sampling time is 2 minutes. The scope of the sample data is showed in Table Ⅲ.

TABLE Ⅱ REAL-TIME DATA OF CALCINATION PROCESS 5000T / D PRODUCTION LINE Time step

u1 (t/h)

u2 (t/h)

u3 (t/h)

u4 (r/m)

u5 (kPa)

x1 (℃)

x2 (%)

t 444.41 18.992 15.06 3.72 -5.32 880.13 3.19 t+1 452.07 17.869 15.06 3.775 -5.32 875.98 3.19 t+2 453.39 19.748 15.06 3.72 -4.97 880.37 2.45 t+3 450.29 19.748 15.06 3.765 -4.97 886.47 2.45 t+4 426.18 19.748 15.06 3.724 -4.97 880.62 3.08 t+5 457.43 19.748 15.06 3.769 -4.97 881.35 3.08 t+6 452.89 19.748 15.06 3.726 -4.97 880.13 3.08 t+7 384.06 19.748 15.06 3.738 -5.23 883.79 3.08 t+8 426.33 19.748 14.555 3.739 -5.49 885.25 3.08 t+9 438.63 19.748 14.555 3.726 -5.49 892.09 2.55 t+10 460.79 18.18 14.555 3.767 -5.49 902.34 2.55 … … … … … … … …

.

ANN

model

based

Clinker

calcination

u2(t)u1(t)

u3(t)u4(t)u5(t)x1(t)x2(t)

x1(t+1)

x2(t+1)

Fig. 2. Simplified model of calcination process based on the ANN.

TABLE I THE REAL-TIME MAIN DATA LIST OF CALCINATION PROCESS FROM THE

CONTROL ROOM OF A CEMENT PLANT IN GUANGXI PROVINCE

No. Sample data Scope of the sample can be measured

Units

1 Limestone 0～500 t/h 2 The pressure of first-air 0～35 kPa 3 The temperature of atmosphere -50～100 ℃ 4 The temperature of secondary-air 0～1200 ℃ 5 The temperature of tertiary-air 0～1200 ℃ 6 The negative pressure of tertiary-air -1.5~0 kPa

7 The air pressure of preheater C1 entrance -7～0 kPa

8 The negative pressure of preheater C1 exit -7～0 kPa

9 Oxygen content of C1 exit 0～25 % 10 Cold valve opening 0～100 % 11 Calciner furnace exit temperature 0～1200 ℃ 12 Calciner furnace exit Pressure -2～0 kPa

13 Coal flow (calciner furnace) Feedback 0～24 t/h

14 Coal flow (rotary kiln) Feedback 0～18 t/h

15 CO concentration of Coal storage exit 0～2000 ppm

16 Rotation speed of rotary kiln 0～4.21 r/m 17 The negative pressure of Kiln tail -1.5～0 kPa

18 The temperature of clinker from the kiln 0～1800 ℃

… … … …

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VI. SIMULATION RESULTS

A. BPNN Model Using MATLAB neural network toolbox of newff function

to set up the model, the number of network hidden layer is selected to be 60; the hidden layer and output layer used the tansig function. The training method for model uses Bayesian normalized algorithm (trainbr). Training data are shown in Table 2. In Table 2, 4000 groups of data are used to train, 100 new groups of data are used to test the neural network model generalization ability. Training parameters are as follows:

The learning rate net.trainParam.lr = 0.002. The largest number of training time net.trainParam.epochs

= 5000. The training goal net.trainParam.goal = 0.002. Selection of the relevant parameters is based on experience

of parameters adjusting and the settings for many times. After much training has been completed, we got the BP neural network model of the pre-kiln system. Part of the training data of the fitting results was shown in Figure 3. The ability of generalization curve is shown in Figure 4, and can be seen to well fit the training data. In view of the generalization ability of BP network model is better suitable for calcining section in a simulation model.

B. RBFNN Model Using newrbe functions to establish the pre-kiln system,

RBF neural network model. The function is to establish a zero error of the radial basis function network function. Part of the training data of the fitting results was shown in Figure 5. Generalization shown in Figure 6, it remains to be further improved.

Fig. 5. Fitting curve of calcination process model based on RBF network after training.

Fig. 4. Generalization ability testing of calcination process model based on BP network after training.

Fig. 3. Fitting curve of calcination process model based on BP network after training.

TABLE Ⅲ

THE SCOPE OF SAMPLING DATA Time step

u1 (t/h)

u2 (t/h)

u3 (t/h)

u4 (r/m)

u5 (kPa)

x1 (℃)

x2 (%)

max 488.18 22.13 15.91 3.99 -4.82 967.04 8.56 min 360.73 6.51 14.11 1.94 -5.77 847.66 1.688

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VII. CONCLUSION Through the above analysis, cement clinker calcining

process is a matter of mass transfer, heat transfer and physical and chemical reactions of complex multi-variable, multi-perturbed systems. It has a number of parameters, and interacts, constantly changing. To obtain a new control method, one must build an accurate model of the calcination process. We have investigated and careful analyzed on the production line, and carefully choose the controlled and operated variables. In the absence of accurate expressions of mathematical model, this paper adopts BP and RBF neural network algorithm to attempt to pre-kiln system modeling. Through the debugger choose suitable training parameters, to

obtain a good fitting results and generalization capacity. From the above analysis, we can see RBFNN based model can reach very high fitting results, but the BPNN based model has good generalization ability. So we choose the BPNN based model as simulation model of the calcination process for exploring new control algorithms.

ACKNOWLEDGMENT B. S. Yang thanks professor Hong Li and Xiushui Ma who

give a lot of support and useful suggestions to the author’s research projects.

REFERENCES [1] D. Graupe. Principles of Artifical Neural Networks. USA: World

Scientific Publishing, 2007. [2] T. D. Sanger. “Optimal unsupervised learning in a single-layer linear

feedforward neural network,” Neural Networks, vol. 2, no. 6, pp. 459-473, 1989.

[3] K. H. Karstensen. “Formation, release and control of dioxins in cement kilns,” Chemosphere, vol. 70, no. 4, pp. 543–560, 2008.

[4] D.C. Hughes, D.B. Sugden, D. Jaglin, D. Mucha. “Calcination of roman cement: A pilot study using cement-stones from Whitby,” Construction and Building Materials, vol. 22, no. 7, pp. 1446–1455, 2008.

[5] B. S. Yang, D. G. Cao. “Action-dependent adaptive critic design based neurocontroller for cement precalciner kiln,” International Journal of Computer Network and Information Security， vol. 1, no. 1, pp. 62-68, 2009.

[6] B. S Yang, X. S. Ma. “Cooling high-speed chips using intelligent control technology,” Proceedings of the IEEE International Conference on Automation and Logistics, vol. 2009, pp. 1330-1334, 2009.

[7] S. Hashem. “Optimal linear combinations of neural networks,” Neural Networks, vol. 10, no. 4, pp. 599-614, 1997.

[8] X. F. Liao, G. R. Chen, and E. N. Sanchezc, “Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach,” Neural Networks, vol. 15, no. 7, pp. 855-866, 2002.

Fig. 6. Generalization ability testing of calcination process model based on RBF network after training.

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