EMPIRICAL MODELLING AND FUZZY CONTROL SIMULATION OF A HEAT EXCHANGER

Aheat exchanger is one of the most important systems that have been installed in
many process plants. It is a device that transfers heat from liquid to another without
allowing them to mix. In order to ensure its smooth operation, modelling and
simulation can be made so that its performance can beanalyzed and improved.
At Process Control Lab, there is no simulation model for laboratory-scale heat
exchanger pilot plant. Most ofthe time, the plant is being used for ordinary laboratory
practice and the performance of this plant is not being analyzed. This project is
therefore conducted to study the plant behavior and to optimize its performance by
simulating it withnewtype of controller.
The first goal of this project is to model the heat exchanger pilot plant by using
empirical modelling method. It will yield the plant transfer function, GP that can be
used for temperature controller analysis. Besides empirical modelling, mathematical
modelling is also being carried out to study the heat exchanger behavior. By having
the model, there is an alternative way to obtain forecasted data and result without
extra cost.
The second part of this project is to analyze the model temperature controller
performance. Two controllers are being compared, namely PID and Fuzzy Logic
Controller. First, PID controller is tested to yield the best tuning parameters for
control valve. Ziegler-Nichols and fine tuning method is used to serve this purpose.
Next, the data from PI controller simulation is fed into ANFIS toolbox in MATLAB
for adaptive learning process. The FIS generated by ANFIS is based on Takagi-
Sugeno fuzzy model. The FIS which is subsequently used by the Fuzzy Logic
Controller will imitate the PI controller performance and perform based on range of
data it has been trained before by ANFIS toolbox. Finally, the comparison between
both controllers is concluded where Fuzzy Logic Controller is successfully imitating
the PI controller with slightly better performance in terms of rise time, settling time
and overshoot percentage.