Modelling and control of heat exchanger by using bio-inspired algorithm
Heat exchanger is a heat transfer device that is used for transfer of thermal energy between two or more fluids available at different temperature. Based on the previous study referred, the same data of heat exchanger had been used but different types of model were used to find the structural...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/1621/1/24p%20NUR%20ATIQAH%20DAUD.pdf http://eprints.uthm.edu.my/1621/2/NUR%20ATIQAH%20DAUD%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1621/3/NUR%20ATIQAH%20DAUD%20WATERMARK.pdf |
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| Summary: | Heat exchanger is a heat transfer device that is used for transfer of thermal energy
between two or more fluids available at different temperature. Based on the
previous study referred, the same data of heat exchanger had been used but
different types of model were used to find the structural model of heat exchanger.
The main objective of this study is to obtain structural model using ARMAX
equation and optimize the value of PID parameters. In this study, data from heat
exchanger experiment was used to determine the parameter of ARMAX equation
and by using GA and PSO, all the parameters were optimized. Transfer function
obtained will be used for plant modelling. Validation test used to validate between
normalised data input and error. Validation test used were autocorrelation and
cross-correlation. Finally, applying PID controller onto plant modelling to
optimize the value of Kp, Ki and Kd. The analysis shows that MSE value produce
from GA is 0.0035473 while PSO‟s MSE value is 0.0043595. ARMAX
parameters were obtained by using GA and PSO with 4 inputs (a0, a1, a2, and a3)
and 4 outputs (b0, b1, b2 and b3). For GA, the inputs are -0.000214, -0.000728, -
0.0020, and -0.000804 while the outputs are -1.0000, -0.1783, -0.1473 and 0.3248.
For PSO, the inputs are 0.0104, -0.0122, -0.0067 and 0.0118 while the outputs are
-0.4274, -0.1256, -0.1865 and-0.2614. From the validation test, GA produced
smoother and effective result compared to PSO with less noise exists. By attaching
PID controller, all the parameters value (Kp, Ki, and Kd) was optimized. For GA,
the parameters are -0.5567, -54.1127 and 0.0005. For PSO, the parameters are -
0.2846, -56.4346 and 0.0010. Even though both algorithms produced different
simulation results, both of them succeed to reduce the result before attaching PID
controller. As a conclusion, GA produces better result compared to PSO. |
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