Mrs.Z.Brijet*1, M.B.Sri Padmadarshan*2, S.Vigneshwaran*3,

P.B.Mohankrishna*4

*1 Assistant

Professor – III, Department of Electronics and Instrumentation Engineering,

Velammal Engineering College, ‘Velammal New-Gen Park, Ambattur-Red Hills

Road, Chennai – 600066, India

*[email protected]

*2,3,4 4th

year Bachelor’s degree, Department of Electronics and Instrumentation

Engineering, Velammal Engineering College, ‘Velammal New-Gen Park,

Ambattur-Red Hills Road, Chennai – 600066, India

*[email protected],*[email protected],*[email protected]

Abstract— The Calciner Unit plays an important role in the modern

cement industries as it is used for preheating the raw materials like limestone

which are fed into the kiln. The mathematical model of the calciner unit is

designed using System Identification technique for the real time data obtained

from the plant. A conventional PID controller has been designed to control the

temperature of the calciner unit. The parameter of PID controller is tuned

using Ziegler – Nichols tuning method. In order to achieve optimum controller

parameter a Self Tuning Fuzzy

PID controller is developed. The performance of the calciner unit has improved

significantly compared to conventional PID controller.

I. INTRODUCTION

Calciner temperature control process

is one of the most important processes in cement manufacturing. It is used to

maintain the raw mix texture, size of the mixture and perfect blending of the

raw material to produce more valuable clinker. Calciner unit is used to preheat

the raw mix sent into the kiln. The product obtained is “clinker” (cement).

Normal temperature of kiln is to be maintained at 800-960 °C and a normal coal

feeding is 10-20 t/hr. There are four basic processes in cement manufacturing. It

starts with quarry where the raw material is extracted and crushed. Then it

will be sent to raw mill wherein the blending process takes place (raw mix).

The resultant from the above process was sent to the calciner where the raw mix

was preheated and fed into the kiln. The raw mix and fuel was sent into the

kiln. Clinker and exit gases come out. The clinker was sent to finish mill,

after which the size was reduced to obtain the final product ‘cement’. The

basic schematic diagram of cement manufacturing plant is shown in Fig.1.1.

Figure 1.1: Schematic diagram of cement

manufacturing plant

II. IDENTIFICATION OF SYSTEM

A.

ANALYZING AND PROCESSING DATA

When preparing

data for identifying models, it was mandatory to specify information

such as input-output channel names, sampling time (10s). The toolbox helps to

attach this information to the data, which facilitates visualization of data,

domain conversion, and various preprocessing tasks. Measured data often has offsets, slow drifts,

outliers, missing values, and other anomalies. The toolbox removes such

anomalies by performing operations such as de-trending,

filtering, resampling, and reconstruction of missing data. The

toolbox can analyze the suitability of data for identification and provide

diagnostics on the persistence of excitation, existence of feedback loops, and

presence of nonlinearities. The toolbox estimates the impulse and

frequency

responses of the system directly from measured data. Using these

responses, system characteristics, such as dominant time constants, input

delays, and resonant frequencies can be analyzed. These characteristics can

also be used to configure the parametric models during estimation.

B.

ESTIMATING

MODEL PARAMETERS

Parametric

models, such as transfer functions or state-space models use a small number of

parameters to capture system dynamics. System Identification Toolbox estimates

model parameters and their uncertainties from time-response and frequency-response

data. These models can be analyzed using time-response and frequency-response

plots, such as step, impulse, bode plots, and pole-zero maps.

C.

VALIDATING

RESULTS

System Identification Toolbox helps

to validate the accuracy of identified models using independent sets of measured data from

a real system. For a given set of input data, the toolbox computes the output

of the identified model and lets to compare that output with the measured

output from a real system. One can also view the prediction error and produce

time-response and frequency-response plots with confidence bounds to visualize

the effect of parameter uncertainties on model responses.

Figure 2.1: Shows

the process of selecting the range for validation and estimation of data.

D. LINEAR MODEL IDENTIFICATION

System Identification

Toolbox lets to estimate multi-input, multi-output continuous or discrete-time transfer functions with a specified number of poles and zeros.

One can specify the transport delay or let the toolbox determine it

automatically. In this work, transfer function model was used for system

identification.

E.

ESTIMATING

TRANSFER FUNCTION MODEL

Estimate

continuous-time and discrete-time transfer functions and low-order process

models. Use the estimate models for analysis and control design. Polynomial and

state-space

models can be identified using estimation routines provided in the

toolbox. These routines include autoregressive models (ARX, ARMAX), Box-Jenkins

models, Output-Error models, and state-space parameterizations. Estimation techniques

include maximum likelihood, prediction-error minimization schemes, and subspace

methods based on N4SID, CVA, and MOESP algorithms. A model of the noise affecting the observed

system can also be estimated. Figure 2.2 depicts the process of obtaining the

transfer function model.

Figure 2.2: Obtaining

transfer function model

F.

ESTIMATING

STATE-SPACE MODEL

A state space model is commonly

used for representing a linear time invariant system. It describes a system

with a set of first order difference equation using inputs, outputs and state

variables. In the absence of the equation, a model of desired order can be

estimated for measured input, output data. The model was widely used in modern

control application for designing controllers and analyzing system performance

in the time domain and frequency domain. The models can be applied to nonlinear

system or system with a non-zero initial condition.

Figure 2.3: Obtaining

state space model

III. DESIGN OF PID CONTROLLER FOR CALCINER

A.

PID

CONTROLLER:

P-I-D

controller has the optimum control dynamics including steady state error, fast

response, less oscillations and higher stability. The necessity of using a

derivative gain component in addition to the P-I-D controller is to eliminate the

overshoot and the oscillations occurring in the output response of the system. One

of the main advantages of the P-I-D controller was that it can be used with

higher order processes including more than single energy storage.

From a mathematical viewpoint,

the PID control works to reduce the error e(t) to zero, where e(t) was the

difference between output response and the set point.

The control response

u(t) is given by:

u(t)=Kpe(t)+Ki?e(t)dt+Kd

de(t)/dt

where kp, ki, kd are scale

factors for the proportional, integral and differential terms respectively.

B.

ZIEGLER –

NICHOLS TUNING METHOD:

The parameters of PID controller

were tuned using Ziegler – Nichols tuning method.

The basic steps in Z-M method are

1. The value of Kd and

Ki were set to zero.

2. The value of Kp was

slowly increased such the sustained oscillation occurs (constant amplitude and

periodic).

3. The value of Kp at

which sustained oscillation occurs was ultimate gain Ku and the

period of oscillation was ultimate period Pu.

From the

calculated value of Ku and Pu, the parameters of PID

controller were calculated using the formula.

The table

3.1 shows the PID controller parameter tuned using Ziegler – Nichols method.

Table 3.1: PID controller tuning parameters

Control type

Kp= 0.6Ku

Ki=2/Pu

Kd= Pu/8

PID

0.6*200=120

2/0.2=10

0.2/8=0.025

IV. DESIGN OF FUZZY CONTROLLER

The fuzzy logic controller

consists of the following blocks. The block diagram of fuzzy controller has

been shown in Figure 4.1.

Figure

4.1: General block diagram of fuzzy logic controller

A.

FUZZY

INFERENCE SYSTEM

A Fuzzy inference system (FIS)

was a system that uses fuzzy set theory to map inputs to outputs. There are two

types of FIS .They are mamdani and Takagi sugeno FIS. In this project there are

two inputs and three outputs. Therefore, mamdani type FIS was used in this

project.

i.

MAMDANI FIS

Mamdani FIS is

widely accepted since it can be applied for both MIMO, MISO systems whereas

sugeno can be implemented only for MISO systems. In mamdani, the membership

functions can be chosen even for outputs whereas it was not possible in sugeno

type. Hence mamdani FIS was used for our project.

ii.

DEVELOPMENT OF MAMDANI TYPE FIS

The MAMDANI type fuzzy

inference system consists of two inputs and three outputs. First input was

error and the second input was rate of change of error. The three outputs were

Kp, Ki and Kd (i.e. controller gains). The rule table for fuzzy controller was shown in Table

4.1.

Table

4.1:Rule table of fuzzy controller

Here

e was error and de/dt was rate of change of error. Meaning of linguistic

variables in FIS(Fuzzy Inference System) are

VH-Very High

H-High

M-Medium

L-Low

VL-Very Low

B.

MAMDANI FIS

IMPLEMENTATION FOR CALCINER TEMPERATURE CONTROL

The two inputs of the process

have three triangular membership functions and outputs have five membership

functions respectively. The membership functions of both the inputs and the

outputs are taken in the range of -1 to +1.The membership functions of the

input and output is as shown in the figure 4.2 and 4.3.

Figure

4.2: Membership function of inputs

Figure 4.3: Membership function of outputs

The rules viewer of the

mamdani FIS is as shown in the fig.6.5.

Figure

4.4: Rule viewer of mamdani FIS

Surface viewer of the mamdani

FIS is as shown in the fig 6.6.

Figure 4.5: Surface

viewer of mamdani FIS

V. IMPLEMENTATION OF FUZZY PID CONTROLLER

A.

STRUCTURE OF

FUZZY-PID CONTROLLER

Self tuning fuzzy-PID controller means that the three

parameters Kp, Ki, and Kd of PID controller are tuned by using fuzzy tuner.

Figure 5.1: Structure of the

self tuning fuzzy-PID controller

The

error and the derivative of its error are sent to the fuzzy controller. The PID

parameter Kp, Ki and Kd is calculated according to the rules in the fuzzy

controller, at the same time, Kp was also refined by P controller which was the

immune PID controller, so the Kp, Ki and Kd can be continuous updated according

to error e(t) and its derivative de/dt.

VI. SIMULATION RESULTS AND DISCUSSION

A.

SERVO

RESPONSE OF PID AND FUZZY PID CONTROLLER

Simulation studies are carried out to

demonstrate the tracking capability of tuned PID controller and fuzzy PID

controller. The performance of process for tuned PID and fuzzy PID were shown

in figures 6.1 and 6.2 respectively. From the response, it was observed that

the calciner temperature follow the given set points and the servo response of

the PID and fuzzy PID were compared in the table 6.1.

Fig 6.1: Servo

response of the PID controller

Fig 6.2: Servo

response of the fuzzy PID controller

Table 6.1: Comparison of performance

indices of PID and FUZZY PID tuned controller for servo response

CALCINER TEMPERATURE CONTROL USING

ISE

IAE

ITAE

PID CONTROLLER

1.559 e^(+05)

416.9

3975

FUZZY CONTROLLER

1.045 e^(+05)

279.3

2138

From the responses, it was

observed that the performance criterion such as ISE, IAE and ITAE of Fuzzy PID

controller was better compared to conventional PID controller. It was also

observed that fuzzy PID controller settles quickly than PID controller

response.

B.

SERVO

WITH REGULATORY RESPONSE OF PID AND FUZZY PID CONTROLLER

Simulation

studies have been carried out to show the disturbance rejection capability of

tuned PID and fuzzy PID controller. A step disturbance was introduced. The

servo with regulatory responses of both PID and fuzzy PID controller was shown

in figures 6.3 and 6.4 respectively and the regulatory response of the PID and

fuzzy PID controller were compared in the table 6.2.

Fig 6.3: Servo with regulatory

response of the PID controller

Fig 6.4: Servo with regulatory

response of the fuzzy PID controller

Table 6.2: Comparison of

performance indices of PID and FUZZY PID controller for servo with regulatory

response

CALCINER

TEMPERATURE CONTROL USING

ISE

IAE

ITAE

PID CONTROLLER

1.605e^(+05)

622.8

9293

FUZZY

CONTROLLER

1.294 e^(+05)

410.9

4294

From the responses, it was

observed that the performance criterion such as ISE, IAE and ITAE of Fuzzy PID

controller was better compared to conventional PID controller.

VII.

REAL TIME

IMPLEMENTATION –CEMULATOR

Contrary to most cement process simulators,

ECS/CEMULATOR was developed on a fully functional control systems platform

enabling the complete set of functions and features of a modern control system

environment for the users. Having a skilled team of operators plays a crucial

role in beneficial and safe operation of industrial plants. Especially in the

cement industry, with the significant high cost of investment, practical

knowledge and experience of plant operation have a direct effect on production

economy. Insufficient insight in process dynamics and interactions, high stress

factors in real time operation conditions, and lack of adequate experience in

utilizing the existing control system are typical reasons for incorrect

operator actions. The consequences of this may result in low production quality,

production interrupts, and equipment damage, in worst case risk on human safety.

The increasing demand on production sustainability in the recent years has resulted

in requirements of which the degree of fulfillment is affected by the level of

skills of plant operators and engineers.

A. REAL

TIME RESPONSE OF THE PID CONTROLLER

The response of the PID

controller in the real time plant is as shown in the figure 7.1.

Figure 7.1: Response of PID controller in real time

B. REAL

TIME RESPONSE OF FUZZY PID CONTROLLER

The response of Fuzzy PID

controller in real time plant is as shown in figure 7.2.

Figure 7.2: Response of Fuzzy PID controller in

real time

Comparison of performance

indices of PID and FUZZY PID controller for the real time response is shown in

Fig. 7.1 and 7.2.

Table 7.1:

Comparison of performance indices of PID and FUZZY PID controller

CALCINER

TEMPERATURE CONTROL USING

ISE

PID CONTROLLER

18.4

FUZZY CONTROLLER

16.41

From the table 7.1 it has been observed that

Integral Square Error (ISE) value of fuzzy PID controller is reduced as

compared to PID controller.

VIII. CONCLUSION

The main aim of the project was

to control the calciner temperature and to obtain a good quality clinker. The

transfer function model of calciner for the process has been derived using

system identification tool. The simulink model of calciner has been developed

in MATLAB using real time steady state values of cement power plant. The open

loop response of the process where observed and the interaction effect has been

studied. The parameters for PID were obtained using Ziegler – Nichols tuning. The

fuzzy rules were written using FAM table and the rules are inserted in the FIS

using mamdani method which is used to tune the PID parameters. Thus Fuzzy PID

controller was implemented and then optimized values were obtained. It is

observed that the performance criteria namely the ISE, IAE, ITAE, and settling

time in Fuzzy PID controller is better than the PID controller. Also from the

responses, it has been observed that the proposed method has better tracking

and faster settling time. Using the tuned values of PID, the fuzzy PID

controller was implemented for cement calciner unit.

IX. Appendix

DATA FROM REAL TIME CALCINER UNIT

S.NO

CALCINER TEMPERATURE

CALCINER COAL FEED

KILN TOTAL FEED

1

894.7916

9.6501

588.4775

2

894.7916

9.6401

589.4781

3

896.5278

9.6359

585.4742

4

898.9583

9.6276

588.4867

5

901.3889

9.6184

594.3333

6

904.1666

9.6096

590.6599

7

902.7778

9.6029

588.5881

8

900.6944

9.6033

590.9871

9

899.3055

9.6079

591.7212

10

901.3889

9.6074

589.3926

11

903.1249

9.6

585.8295

12

901.7361

9.5952

584.7019

13

900.6944

9.5972

586.1656

14

901.0416

9.5997

590.9084

15

903.1249

9.5979

590.3184

16

906.2499

9.5892

591.2415

17

904.8611

9.5817

590.2633

18

903.1249

9.5822

591.3748

19

902.7778

9.5847

591.8418

20

906.9444

9.5828

585.3685

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