Outlet Temperature Control of Superheated Steam Using Intellegent Controller and Advanced Controller

IJSART - Volume 1 Issue 5 MAY 2015 ISSN [ONLINE]: 2395-1052

Page | 132 www.ijsart.com

Outlet Temperature Control of Superheated Steam

using Intelligent Controller and Advanced Controller

S.Kaaviya 1, V.Radhika

2

1,2 Department of Control and Instrumentation Engineering 1,2 Sri Ramakrishna Engineering College

Abstract- Superheaters and desuperheaters in combination

are present in boilers of power plant and paper pulp

industries. The steam from the boiler is sent to turbine for

power generation, this steam is supercritical steam and this is

to be superheated and desuperheated simultaneously before

being sent to the turbine. The precision control of superheated

steam fed to turbines for the generation of electrical power

has been a challenging task for control engineers for a long

time. There are several limitations that are associated with

conventional control philosophies used for this purpose. The

modern control techniques namely conventional and advanced

controllers are being preferred due to their inherent merits

over the conventional control techniques in this work and

results are compared. In this work, an attempt has been made

to design fuzzy logic based PID controller and Model

Predictive Control for superheater temperature control of a

boiler. The PID controllers based on are also

designed.Standard SIMULINK Software is used on MATLAB

platform to get the results.

Keywords- Superheated steam, Intelligent techniques, PID controller, Ziegler-Nichols tuning,Fuzzy Adaptive PI, Model

Predictive Controller.

I. INTRODUCTION

A boiler or a steam generating unit is an integral part

of any electric utility plant. It requires a source of heat at a

sufficient temperature level to produce steam. In generating

electric power with a turbo generator, it is much more efficient

to use steam that has been superheated and reheated as is done

in the typical electric utility plant. The general practice with

the industrial boiler is to use saturated steam or only a small

amount of superheat unless the electric power is being

generated in the industrial plant. A turbine generally

transforms the heat of superheat into work without forming

moisture. The heat of superheat is all recoverable in the

turbine. A variation in the steam temperature, pressure, etc.,

may cause unequal expansion and contraction in the turbine

parts. Rapid and excessive changes in temperature can result

in damage to the turbine. Steam temperatures that are

significantly higher than the design temperature can shorten

the life of the turbine metal parts. Such temperature variations

also cause a change in the unit electrical generation.

A supercritical steam generating unit is the one which

operates at a pressure above the critical pressure of 3208 psia.

When water at a supercritical pressure is heated, it does not

boil and does not produce a two-phase mixture of water and

steam. Instead, the fluid undergoes a transition in the enthalpy

range of approximately 850 to 1050 btu/lb. At the boiler's inlet

the high pressure feed water is forced into the boiler tubes. It

is heated as it passes through them and finally is ejected from

the boiler's main outlet (secondary superheater outlet) as a

main steam.

Superheater outlet temperature from boiler to the

turbo generating unit is to controlled accurately due to the non

linear time varying behavior of the system. Process modeling

difficulties and lack of suitable measurements of plant

dynamics make most conventional control techniques

unsuitable and manual control imperative. By manual control

the overall process objectives quality and quantity of

superheated steam produced is left in the hands of a human

operator.

In the past few years there has been a tremendous

increase in the popularity of PID controllers. The test of the

evolution of the PID is that, actually most of the classical

industrial controllers have procedure to automate its

parameters. Then, if we can get a good model of the process,

given by analytic linear equation, direct technique of control

are the simplest and less cost alternatives. The classical PID

controller provides an accurate and efficient solution to linear

control problems. But the involved process are in general

complex, time variant, with delays and non-linearitys and

very often, with a poorly defined dynamics. When the

processes are too complex to be described by analytic models,

they are hardly controlled by drastic approaches that simplifies

them but do not get the required efficiency. To circumvent,

some of these problems, modern control techniques have

emerged for their applications in power systems.

Considering these difficulties incorporating human

intelligence ino the controller would be a simple and efficient

soltion and this lead to the development of fuzzy logic

controllers. Fuzzy logic controllers provide robust control

inspite of measurement inaccuracies. This feature provides a

reasonable tolerance for prediction in dead time process. In



this approach a fuzzy controller with a simple prediction

algorithm to compensate for inherent transportation lag of

superheater.

Model Predictive Control also known as receding

horizon control, is an advanced strategy for optimizing the

performance of multivariable control systems. MPC generates

control actions by optimizing an objective function repeatedly

over a finite moving prediction horizon, within system

constraints, and based on a model of the dynamic system to be

controlled.

Thus traditional PI algorithm doesnt hold good for

such systems which has disturbances by nature. A new

algorithm that can deal with these limitations has to be

considered. The fuzzy controller is a non-linear controller and

the fuzzy control algorithm is based on the intuition and

experience about the plant to be controlled. Therefore it

doesnt rely on the precise mathematical modeling. Similarly

advanced control strategy of Model Predictive Control also

shows optimum performance.

II. MODELLING OF SUPERHEATER

AND DESUPERHEATER

The system considered here is Superheater and

Desuperheater system. The outlet steam temperature from the

recovery boiler is to be maintained by superheating

desuperheating it simultaneously.

A. SUPERHEATER MODEL

For modeling the superheater parts, it should be noted

that only the steam phase is presented in these subsystems.

Also, in once-through boilers, the pressure change is only a

function of the feedwater flow rate.

Let pT

hCp )(

v

T

uCv )(

Where t

T

T

h

t

h

(1)

Mass balance equation :

shsshps qqVdT

d)( 0 (2)

Mass change is negligible since steam temperature is over

saturated temperature.

Energy balance equation :

)()( shpsscpsss hhqQTmChVdt

d (3)

)()( shpsscs

ss

s

ssapa hhqQdt

dhV

dt

dVhTCm

dt

d

(4)

dt

dTC

dt

dT

T

h

dt

dhp

(5)

At steady state metal temperature is close to steam

temperature.

)()( outinpscout

pss

s

ssapa TTCqQdt

dTCV

dt

dVhTCm

dt

d

(6)

)()( inapa mfTCmdt

d (7)

function of mass flowrate.

This approximation is good enough to fit model response with

experimental data.

inain mkmf )(

0)(11

kC

kTTm

VQ

CVdt

dT

p

a

outinin

sspss

out

(8)

ssVk

12

p

a

C

kB 1 sso VkB 2 f u e lHmQ

pC

Hk 1

V Volume(

3m )

Specific Density( 3/ mkg )

H Specific enthalpy(KJ/kg)

Q Heat transfer (MJ)

T Temperature

(deg Celsius)

Q Mass flow rate

M Mass(Kg)

H Calorific value



))(( 2112 BBTTmmkkC

dToutininfuel

p

out (9)

Substituting the values and applying laplase transform transfer

function is obtained.

0526.0

3882.1

s (10)

B. DESUPERHEATER MODEL

The response of outlet temperature to the changes in

spray water is instantaneous. Hence, there is no dynamics

involved the coefficient connecting the spray change to

change in outlet temperature at 100% load has been derived.

The overall steady state coefficient for control valve and

desuperheater is found to be 0.556 relating the change at the

superheated outlet temperature to the change in the controller

design. Therefore the transfer function for spray valve

coefficient is obtained as,

(11)

III.CONTROLLER DESIGN

A. PID CONTROLLER:

The PID controller is commonly used to control any

parameters in process industries. The PID controller consists

of proportional, integral and derivative term. The proportional

term changes the controller output proportional to the current

error value. Large values of proportional term make the

system unstable. The Integral term changes the controller

output based on the past values of error. So, the controller

attempts to minimize the error by adjusting the controller

output. .The derivative term is used in slow processes. So, the

controller attempts to minimize the error by adjusting the

controller output. The PID gain values are calculated by using

the Ziegler-Nichols first tuning algorithm.

B. FUZZY ADAPTIVE PI

The controller works on the basics of PI tunning. As

mentioned earlier the fuzzy adaptive is used to tune the Kp

and Ki values of the PI controller. The controller fis file

include the number of inputs and number of outputs and the

fuzzy inference engine to be used. Depending on the input

values of different values the output values are decided based

on the rules framed.

The inputs to to the fuzzy controller are two,

including the error and derivative of error. The output of the

controller are Kp and Ki values which are fed to the variable

PI block designed The output of the variable PI is fed to the

plant transfer function designed.

No. of Inputs:2 (error, derivative of error)

No. of Outputs:2(Kp,Ki)

No. of rules:25

Membership function for error, derivative error , Kp and Ki : 5

Fig.1 : Rule viewer of Ki

Fig.2 : Rule viewer for Kp

C. MPC CONTROLLER

Model Predictive Control, MPC, usually contains the

following three ideas,

1. Explicit use of a model to predict the process output along a

future time horizon.

2. Calculation of a control sequence to optimize a performance

index.

3. A receding horizon strategy, so that at each instant the

horizon is moved towards the Future, which involves the

application of the first control signal of the sequence

Calculated at each step.

1. mo-measured output is the output from superheater

2. ref-reference is the temperature of steam to be

maintained.

1

556.0



3. mv-measured variable from controller is fed to

desuperheater for maintaining the temperature.

IV.RESULTS

Fig.3 : Response of PID controller of superheater I

Fig.4 : Response of PID controller of superheaterII

Fig.5 : Response of MPC controller of superheaterI

Fig.6 : Response of MPC controller of superheaterII

Fig.7 : Response of Fuzzy controller of superheater I

Fig.8 : Response of Fuzzy controller of superheaterII

V. CONCLUSION

The continuous process of superheating and

desuperheating is a tedious process. The transfer function is

obtained by deriving the mathematical model and substituting

the parameters of the plant. Then PID controller is

implemented using first tunning method. Then fuzzy adaptive

PI is implemented which has an advantage of less peak

overshoot in comparison with the conventional PID

controller. Then adavanced control stratergy MPC is

implemented which shows an advantage of fast settling in

comparison to PID controller.

VI. FUTURE SCOPE

The control is implemented for outlet temperature of

two superheaters using their respective desuperheaters

similary the third superheater. Then optimisation can be

incorporated for better performance.

REFERENCES

[1] Mohan.k, Arun.L.R and Guruprasad.B.S,( June 2013)

Nonlinear analysis and fatigue life estimation of

attemperator using fe based approach, International

Journal of Innovative Research in Science, Engineering

and Technology, Vol. 2, Issue 6.

[2] Ghaffari, A. Chaibakhsh, and S. Shahhoseini ,( October

2012) , Neuro-Fuzzy Modeling of Heat Recovery Steam

Generator, International Journal of Machine Learning

and Computing, Vol. 2, No. 5.

[3] Ade Haryanto, Arjon Turnip, and Keum-Shik Hong,(

2009) Parameter dentification of a Superheater Boiler

System Based on Wiener-Hammerstein Model using

Maximum Likelihood Method , Proceedings of the 7th

Asian Control Conference.

[4] Ali Chaibakhsh , Ali Ghaffari, S. Ali A. Moosavian

,(2007), A simulated model for a once-through boiler by

0 500 1000 1500 2000 2500 30000

200

400

600

800

1000

1200

1400

1600

1800

2000

tem

p(de

gC)

0 500 1000 1500 2000 2500 3000-1

-0.5

0

0.5

1

1.5

2x 10

4

Time(sec)

tem

p(de

gC)

0 50 100 150 200 250 300 350 400 450 5000

100

200

300

400

500

600

700

Time(sec)

tem

p(de

gc)

0 50 100 150 200 250 300 350 400 450 5000

100

200

300

400

500

600

Time(sec)

tem

p(de

g)

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

1000

2000

3000

4000

5000

6000

Time(sec)

tem

p(de

gC)

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

1000

2000

3000

4000

5000

6000

Time(sec)

tem

p(de

gc)



parameter adjustment based on genetic algorithms

ELSEVIER, pp: 1029-1051.

[5] S.R.Vaishnav, Z.J.Khan, (October 24-26, 2007) , Design

and Performance of PID and Fuzzy Logic Controller with

Smaller Rule Set for Higher Order System,WCECS

2007.

Documents

Outlet Temperature Control of Superheated Steam Using Intellegent Controller and Advanced Controller