Comparison of Various Water Level Controllers' Models for Boiler Drum

International Journal of Mechanical Engineering and Research

Volume 3 Issue 1 (Page, 29-35), ISSN: 2277-8128

Copyright © 2014 IJWRE, All Right Reserved 29

Comparison of Various Water Level Controllers’ Models for

Boiler Drum

Brijendra kr. Maurya1, Saurabh kr. Bajpai

2, Abhishek kr. Singh

3

1. Assistant Professor Department of Electronics and Communication Engineering Shobhit University, Saharanpur, India.

2. Assistant Professor in Department of Electrical Engineering in Shobhit University, Saharanpur, India.

3. Lecturer in Department of Mechanical Engineering in Shobhit University, Saharanpur, India.

ABSTRACT:

In this paper we have design various models for water level

controller for boiler drum. First model is based on PID, Second

model based on Fuzzy (mamdani) and third model based on Fuzzy

(Sugeno) and then compared with the help of Simulink. Drum Level

Control Systems are used extensively throughout the process

industries and the Utilities to control the level of boiling water

contained in boiler drums on process plant and help provide a

constant supply of steam. The purpose of the drum level controller is

to bring the drum up to level at boiler start-up and maintain the

level at constant steam load.

Key Words-Boiler Drum Level control, PID tuning, Fuzzy

Inference System (FIS), Fuzzy Logic, MATLAB.

I. Introduction

Drum Level Control Systems are used extensively throughout the

process industries and the Utilities to control the level of boiling

water contained in boiler drums on process plant and help provide

a constant supply of steam. The purpose of the drum level

controller is to bring the drum up to level at boiler start-up and

maintain the level at constant steam load. A dramatic decrease in

this level may uncover boiler tubes, allowing them to become

overheated and damaged. An increase in this level may interfere

with the process of separating moisture from steam within the

drum, thus reducing boiler efficiency and carrying moisture into

the process or turbine. Boiler drum water level control is critical

to secure operation of the boiler and the steam turbine. The

functions of this control module can be broken down into the

following:

i) Operator adjustment of the set point for drum level. ii) Compensation for the shrink & swell effects. iii) Automatic control of drum level. iv) Manual control of the feed water valve.

v) Bump less transfer between auto and manual modes. vi) Indication of drum level and steam flow.

vii) Indication of feed water valve position and feed water

flow. viii) Absolute/deviation alarms for drum level.

The most basic and pervasive control algorithm used in the

feedback control is the Proportional Integral and Derivative (PID)

control algorithm.PID control is a widely used control strategy to

control most of the industrial automation processes. The three

element PID control system is introduced to regulate the drum

level with the fixed PID parameters. The control is not ideal

because the gains and time constants of the system response

change significantly with the change in steam load and

disturbances. Therefore, some other controller is

required to improve the performance of drum level control

system [1].The followings of the paper includes, Section II

describing the types and design of boiler. In Section III

development of virtual laboratory is explained. In Section IV

result and comparison between the boiler performances is

discussed.

II. Boiler Drum Level Control

In the process industries, boiling water to make steam is a very

important procedure. The control of water level is a major

function in this process and it is achieved through a water steam

interface established in a cylindrical vessel called the drum

which is usually lying on its side and located near the top of the

boiler. Maintaining the correct water level in the drum is critical

for many reasons. A water level that is too high causes flooding

of the steam purification equipment; resulting in the carryover of

water and impurities into the steam system. A water level that is

too low results in a reduction in efficiency of the treatment and

recirculation function.

It can even result in tube failure due to overheating from lack of

cooling water on the boiling surfaces. Normally drum level is

expected to be held within 2 to 5cm of the set-point with some

tolerance for temporary load changes. There are several components affecting its operation. Under

boiling conditions, steam supporting field products such as

bubbles exist below the water/steam level interface [12]. These

bubbles have volume and therefore displace water to create a

misrepresentation of the true water level in the drum. Another

effect upon drum level is pressure in the drum. Because steam

bubbles compress under pressure (if the drum pressure changes

due to load demands), the steam bubbles expand or contract

respective to these pressure changes. A higher steam demand

will cause the drum pressure to drop, and the steam bubbles to




expand to give the appearance of a water level higher than it truly

is. This fictitious higher water level causes the feed water input to

be shut down at a time when more water is really required. A

surge in water level as a result of the drum pressure decreasing is

called 'swell'. A water level decrease due to drum pressure

increase is called 'shrink'. Providing tight water level control in a

drum is accomplished by utilizing one of three types of drum

level control: single-element, two-element, or three-element [1].

II. A. Single element drum level control Single-level element control uses only the level measurement and

the feed water valve. The controller responds to a proportional

signal from the drum level transmitters by generating a

proportional output to the boiler feed water valve when needed.

This approach is often used when starting up a boiler and there is

no steam flow or when a flow meter has failed. The drawback of

this strategy is that the level is subject to uncontrolled

disturbances from the steam header and the feed water. For

example, if the feed water header pressure rises, the feed water

flow to the boiler also increases. Without a feed water control

loop, this situation would be uncorrected until the level changes.

In addition, the installed characteristics of the feed water valve

may compromise level control performance over a large

operating range.

II. B. Double element drum level control The two-element level control adds the steam flow as a feed

forward element to the level controller output. A steam mass flow

rate signal is used to control the feed water flow so that feed

water demand can be adjusted immediately in response to load

changes. The level controller is used to correct any imbalance

between the steam mass flow out of and the feed water mass flow

into the drum. This approach delivers more effective drum level

control than a single element. It is well suited for use on a single boiler with a single feed water

pump using a constant feed water pressure. A potential weakness

is that the installed characteristics of the feed water valve may

compromise level control performance over a large operating

range. In addition, steam feed forward may need to be

characterized when using this approach. .

II. C.Three elements drum level control Three-element level control as shown in Fig.1 is the most

common boiler drum level control strategy. A feed water flow

loop slave is added to the two-element strategy. Three-element

level control linearized the feed water flow with respect to the

steam flow and the level controller output. The control loop now

requests volumetric flow change, not just a change in the valve

position. This strategy attempts to compensate for changes or

disturbances in steam flow and feed water flow based on the

principle that flow in equals flow out. The installed characteristics

of the feed water valve are no longer an issue because the flow

controller can compensate. Using this approach, the steam feed

forward element can be a simple gain without requiring

characterization [1].

Fig. 1 Three element boiler drum level control

III. Control Strategies and Simulation Control strategies are necessary for any system to perform

accurately. Some of these are given below. The simulation

results are shown here for different control strategies. The

relationship between the feed water flow rate and drum level for

the boiler process are expressed by the following equations [3].

The process function, valve function and disturbance function is

shown below. Gp(s) = [0.25(-s+1)] / [s (2s+1)]……..…. (1)

Gv(s) = 1/ [0.15s+1]...…...……......…....... (2)

Gd(s) = [-0.25(-s+1)]/ [s(s+1) (2s+1)]….... (3)

IV. Simulation & Result

Simulink block diagram of boiler System without Controller

Figure 2: Simulink block diagram of boiler system

Output Response of without Controller

Figure 3: Output Response of system




V. PID Controller A Proportional-Integral-Derivative (PID) controller is a general

feedback control loop mechanism widely used in industrial

process control systems. A PID controller corrects the error

between a measured process variable and the desired set point by

calculating the value of error. The corrective action can adjust the

process rapidly to keep the error minimal. If u (t) is the control

signal, y (t) is the measured output and r (t) is the desired output,

and tracking error e (t) = r (t) − y (t), a PID controller has the

general form. d

u (t ) K P e (t ) K I e(t ) dt K D dt e (t ) Where KP, KI, KD are proportional gain, Integral gain and

Derivative gain, e (t) = error signal. PID controller separately calculate the three parameters i.e. the

proportional, the integral, the derivative values. The proportional

value determines the reaction to the current error. The integral

value determines the reaction based on the sum of recent errors as

past error. The derivative value determines the reaction based on

the rate at which the error has been changing as a future error. By

tuning these three constants in the PID controller algorithm, the

controller can provide control action designed for specific process

control requirements. Some applications may require only one or

two parameters of the PID controller to provide the appropriate

control on system. A PID controller will be called a PI, PD, P or I

controller in the absence of the respective control actions. This is

achieved by setting the gain of undesired control outputs to zero.

PI controllers are very common, since derivative action is very

sensitive to measurement noise and the absence of an integral

value may prevent the system from reaching its target value due

to control action [2].

Block Diagram of boiler System with PID

Controller

Figure 4: Simulink Block Diagram of PID controller Where KP = 1.280, KI = 0.0036, KD = 1.84

PID Controller output response

Figure 5: Output Response of PID Controller

Fuzzy Logic Controller After being mostly viewed as a controversial technology for two

decades, fuzzy logic has finally been accepted as an emerging

technology since the late 1980s. This is largely due to a wide

array of successful applications ranging from consumer

products, to industrial process control, to automotive

applications [1]. Fuzzy logic is closer in spirit to human thinking

and natural language than conventional logical systems [2].

Classical control theory is based on the mathematical models

that describe the physical plant under consideration. The essence

of fuzzy control is to build a model of human expert who is

capable of controlling the plant without thinking in terms of

mathematical model [3].Fuzzy systems are very useful in two

general contexts: (1) in situations involving highly complex

systems whose behaviors are not well understood, and (2) in

situations where an approximate, but fast, solution is warranted

[4]. Fuzzy logic was put forward earliest in 1965 by L.A. Zadeh.

One of the primary applications of fuzzy logic was subway

system in Sendai city of Japan. The applied result showed that

fuzzy logic control was superior to traditional control. But

finding out the correct rule set and determining the essence and

range of fuzzy variables is time consuming work. Such as in

subway system of Sendai, to obtain correct input sets, the

engineers spent several months. Similarly, in water level boiler

system field today, there is a long way to find out a mature

expert fuzzy control model which must need plenty of project

experience [5]. The rest of the paper is organized as follows

gives the difference between Mamdani-type and Sugeno-type

FIS. And shows the development of Mamdani (MAM)-type

FIS.And also, shows the development of Sugeno-type FIS.

Development of Mamdani-Type FIS Boiler system is first developed using mamdani fuzzy model. It

consists of two inputs from water flow and feed water valve. The

system has one output that controls the water level of the boiler.

Each of the inputs has seven




gaussian membership functions as shown in Figs.7 and 8. The

output i.e. water level is taken in range from 0 to 1 and have

seven gaussian membership functions shown in Fig. 9. The rules

viewer included for the water level of the boiler system are

described in below. Fuzzy logic is extension of binary logic. It

uses partial truth values instead of completely true or completely

false .They have a value that shows the degree of truth in the

range 0 to1.0 represents absolute false and 1 represents

completely true[2][5]. Fuzzy logic controller converts the

intelligent knowledge into an automatic control action. It handles

information in systematic way. Fuzzy logic is widely used in very

complex and highly nonlinear system. [3]

Figure 6: Block diagram of Fuzzy Inference System The FLC implemented here is a two-input &

single-output. The two inputs are error, and error rate. Both input

variables are classified into three fuzzy levels in this

implementation based on the resolution and real-time

requirements needed. Subsets for Inputs and Output: Input 1(Water Error): Membership Functions for input1: NB (Negative Big) NM (Negative Medium) NS (Negative Small) ZO (Zero) PS (Positive Small), PM (Positive Medium) PB (Positive Big)

Membership functions for input1: Error

Figure 7: Membership function for error

Input 2 (Water Error Rate): Membership Functions for input2: NB (Negative Big) NM (Negative Medium) NS (Negative Small) ZO (Zero) PS (Positive Small), PM (Positive Medium) PB (Positive Big)

Membership Functions for input2:

Change of Error

Figure 8: Membership function for error rate

Membership Functions for output:

Figure 9: Membership function for fuzzy output

Rule viewer: This Rule Viewer provides an animation of how the rules are fired during simulation.

Figure 10: Rule Viewer




Surface Viewer: In this paper this plot is generated by the fourteen nine rules that accounted for both error and change in error.

Figure11: Surface View

Block Diagram of FLC (Mamdani)

Figure 12: Simulink block diagram of FLC Where, KP= 0.5892, KD = 0.9115, Gain = 4.48

Fuzzy Logic Controller (Mamdani type) output Response

Figure 13: Output water level response

Development of Sugeno-Type FIS: For development of water level of boiler system using Sugeno-

type model, the initial steps are same as Mamdani-type model. It

also takes inputs from water flow and feed water and produces an

output signal that controls the water level of boiler system. Inputs

water flow and feed water have seven gaussian membership

functions over the range of -1 to 1.The output water level can

only be either constant or linear in this FIS, so seven membership

functions for the output are NB (Negative Big), NM (Negative

Medium), NS (Negative Small), ZO (Zero), PS (Positive Small),

PM (Positive Medium), PB (Positive Big) which are constant and

are shown figure below. The output in Sugeno-type FIS can only

be in

range of 0-1. The rule base for Sugeno-type FIS is same as for Mamdani-type FIS.

Output of membership functions of Sugeno: Water Error &chance of water error same as mamdani and different only output using constant given-

Figure 14: Output of member function Rule viewer:

Figure 15: Rule viewer for FLC (Sugeno) Surface viewer:

Figure 16: Surface viewer of FLC (Sugeno)

FLC (Sugeno) Block Diagram

Figure 17: Simulink block diagram of FLC




Where, KP = 0.5892, KD = 0.9149, Gain = 4.48

FLC (Sugeno) Output Response:

Figure 18: Output water level response

Mamdani-Type FIS vs. Sugeno-Type FIS Mamdani method is widely accepted for capturing expert

knowledge. It allows us to describe the expertise in more

intuitive, more human-like manner. However, Mamdani-type FIS

entails a substantial computational burden. On the other hand,

Sugeno method is computationally efficient and works well with

optimization and adaptive techniques, which makes it very

attractive in control problems, particularly for dynamic non linear

systems. These adaptive techniques can be used to customize the

membership functions so that fuzzy system best models the data.

The most fundamental difference between Mamdani-type FIS and

Sugeno-type FIS is the way the crisp output is generated from the

fuzzy inputs. While Mamdani-type FIS uses the technique of

defuzzification of a fuzzy output, Sugeno-type FIS uses weighted

average to compute the crisp output. The expressive power and

interpretability of Mamdani output is lost in the Sugeno FIS since

the consequents of the rules are not fuzzy [7]. But Sugeno has

better processing time since the weighted average replace the time

consuming defuzzification process. Due to the interpretable and

intuitive nature of the rule base, Mamdani-type FIS is widely used

in particular for decision support application. Other differences

are that Mamdani FIS has output membership functions whereas

Sugeno FIS has no output membership functions. Mamdani FIS is

less flexible in

system design in comparison to Sugeno FIS as latter can

be integrated with ANFIS tool to optimize the outputs.

Comparison performance Simulink block diagram of PID and FLC (mamdani & Sugeno):

Figure 19: Comparison performance of PID and FLC

Comparison output response of water level:

Figure 20: Comparison output response of PID and FLC

Table1: Comparison of different parameters for step input

Without PID FLC FLC

Controllers controller (Mamdani) (Sugeno)

Parameters

Peak time tp 12.25 8.04 5.15 4.80

(sec) Settling time 28.25 11.24 5.65 5.05

ts (sec)

Rise time tr 8.25 3.69 3.15 3.05

(sec)

Peak 15.89 2.83 0 0 Overshoot Mp

(%)

Peak 6.25 0 0 0 undershoot

(%) Integral 6.348 3.398 3.222 2.991

Absolute

Error (IAE)

(%)

Integral 4.173 2.76 2.696 2.68 Square Error

(ISE) (%) Absolute 0.0114 0.00028 0.00038 0.00034

Error (%)




IV Conclusions: This paper presents a comparative study of

performance of PID and FLC. Based on the results and the

analysis, a conclusion has been made that FLC provides a better

control action. As per Comparison of different parameters for step

input in table1FLC is based on Sugeno is superior then other type

of water level controller model.

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for unstable process, in: Proc. of the IEEE International Conf. on

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pp.121–124. [8] Luis E. Zarate, Peterson Resende, & M. Benjamin,” A Fuzzy

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Logic Controller – Part 1,” IEEE Transactions on System, Man

and Cybernetics, vol.20, No 2 (1990). [10] Gang Feng, “A Survey on Analysis and Design of Model-

Based Fuzzy Control Systems,” IEEE transactions on fuzzy

systems, vol. 14, no. 5, October 2006. [11] Boiler Control Systems Engineering, 2nd Edition, by Jerry Gilman. [12] Sathyanathan, Dr V T. "Bi Drum and Single Drum Boiler Compared".

Author Profile : Brijendra kumar maurya presently working as Assistant Professor in Department of Electronics&Communication Engineering in

Shobhit University, Saharanpur. His current area of interest in Control System, Digital Electronics, Network Analysis and Synthesis, Automatic Control System, Signal System, Electromagnetic Field Theory, Analog Integrated Circuits Electronic Circuits.

Saurabh kumar Bajpai presently working as Assistant Professor in Department of Electrical Engineering in Shobhit University. He published several papers in

National & International journal. His current area of interest in control system, power system, Electric Machine, FACTS.

Abhishek kumar Singh presently working as lecturer

in Department of Mechanical Engineering in Shobhit

University. His current area of interest in

thermodynamic, heat transfer, fluid mechanics, and engineering

mechanics.

Comparison of Various Water Level Controllers' Models for Boiler Drum

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