This document presents a comparative study of various control techniques, including PID, PID with feed-forward, and artificial neural networks, applied to a heat exchanger system. It describes the heat exchanger system and components, including a shell and tube heat exchanger, air to open valves, and thermocouples. It also discusses the need for efficient control in process plants. The document outlines the control techniques to be studied and includes block diagrams and equations of the heat exchanger system model. Results will be compared to determine the best performing control method.

1. Comparative Study of Various
Control Techniques using a Heat
Exchanger System
UNDER THE GUIDANCE OF
DR. PRERNA GAUR
SUBMITTED BY:
SUDHAKAR GUPTA (506/IC/09)
SUMIT BHAGAT (508/IC/09)
SURBHI MIDHA (512/IC/09)

2. Problem Statement
 Comparative Study of the various control
techniques (PID, PID with feed-forward
and Artificial Neural Networks) using a
standard heat exchanger system

3. Heat Exchangers
 Heat exchangers are devices that are used to transfer
thermal energy between two fluid streams at
different temperatures without mixing the two
streams.
 There are several different types of Heat Exchangers:
1. Shell-and-tube
2. Double pipe
3. Plate type
4. Spiral tube

4. Shell and Tube Heat Exchanger
 Heat is transferred from one fluid to the other through
the tube walls.
 In order to transfer heat efficiently, the transfer area is
chosen to be as large as possible.

5. Air to Open Valve (Fail Close)
 Air to Open valves are held closed by a spring, and
open only upon the application of air pressure (a
control signal).
 In case of a failure in the plant, it is important that
the valves fail in a safe mode.
 In the case of HE, fail close valve is desirable as it
would remain closed and not let the steam overheat
the material in the tank.

6. Thermocouple
 Consists of two dissimilar metals, joined together at one
end.
 When the junction of the two metals is heated, a
corresponding voltage is produced.

7. Need for Efficient Control
 To satisfy stringent performance requirements of
process plants
 Strict product quality specifications of industries
 Increased difficulty of operation in modern plants
because of the trend toward complex and highly
integrated processes.
 Increased emphasis placed on safe and efficient
plant operation

8. Control Techniques
P
 PI
 PID
 ANN

9. Proportional Controller
C(t)=Kc*e(t) + Cs
Actuating o/p is proportional to the error.
Kc = Proportional gain of controller
 Proportional Band (PB)=100/Kc
 The larger the gain Kc, the higher the sensitivity of controller’s actuating signal to
deviation e.

10. Proportional Integral Controller
C(t)= Kc*e(t) + Kc/Ʈ*ʃe(t) dt + Cs
It is know as proportional plus reset controller.
Ʈ is integral time constant or reset time in minutes.
 It eliminate forced oscillations and steady state error resulting in
operation of P controller.
 But introducing integral mode has a negative effect on speed of
the response and overall stability of the system.

11. Proportional Integral Derivative Controller

12. Proportional Integral Derivative Controller
C(t)= Kc*e(t) + Kc/Ʈ1*ʃe(t) dt + KcƮ2 (de/dt) + Cs.
 With presence of the derivative term, PID anticipates what error will be in the immediate
future and applies a control action which is proportional to the current rate of change in
the error.
 PID controller has all the necessary dynamics: fast reaction on change of the controller
input (D mode), increase in control signal to lead error towards zero (I mode) and
suitable action inside control error area to eliminate oscillations (P mode).
 Major Drawbacks:
a) For a response with constant non zero error it gives no control action since de/dt=0
b) For noisy response with almost zero error it can compute large derivatives and thus
yield large control action, although it is not required.

13. Artificial Neural Networks
 An artificial neuron network (ANN) is a computational
model based on the structure and functions of biological
neural networks. Information that flows through the
network affects the structure of the ANN because a
neural network changes - or learns, in a sense - based on
that input and output.
 An ANN is typically defined by three types of
parameters:
 The interconnection pattern between different layers of
neurons
 The learning process for updating the weights of the
interconnections
 The activation function that converts a neuron's
weighted input to its output activation.

14. The System

15. Experimental Data
PROPERTY VALUE
Exchanger response to the steam flow gain 50° C/(kg/sec)
Time constants 30 sec
Exchanger response to variation of process fluid flow 1° C/(kg/sec)
gain
Exchanger response to variation of process 3° C/° C
temperature gain
Control valve capacity 1.6 kg/sec of
steam
Time constant of control valve 3 sec
The range of thermocouple 50° C to 150° C
Time constant of thermocouple 10 sec

16. Model of the System Based on Data
DESCRIPTION FUNCTION
TRANSFER FUNCTION OF PROCESS
GAIN OF VALVE 0.133
TRANSFER FUNCTION OF VALVE
GAIN OF I/P CONVERTOR 0.75
TRANSFER FUNCTION OF FLOW
DISTURBANCE
TRANSFER FUNCTION OF TEMPERATURE
DISTURBANCE
TRANSFER FUNCTION OF THERMOCOUPLE

17. Block Diagram

18. Equations
 G(s) = 0.75 * [0.133/(3s+1)] * [50 / (30s+1)]
 H(s) = 0.16/(10s+1)
 The characteristic equation (1+G(s)H(s) =0) in this case
is :
 Taking Kc = 2.1586 (As per value obtained by tuning)
we have the roots as:
p1 = -0.3564
p2 = -0.0551+i*0.0738
p3 = -0.0551-i*0.0738

19. Simulink Model

20. Results

21. Results

22. The Road Ahead
 What are we currently working on?
 We are currently studying the performance for a PID
Feedback Controller.
 What do we plan to do further?
 We plan to control the process by implementing a
PID with Feed-forward Controller as well as a neural
network based architecture, and compare the results
obtained using these three control techniques.