Estimation on gas density internal model control (IMC) controller using partial least squares
This research was carried out to develop a gas density control model using Aspen Plus with Internal Model Control (IMC) method application for data generation purpose and to analyze on the process estimation using Partial Least Squares (PLS) regression. In making this process, the Air Flow Pressure...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2009
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| Online Access: | http://umpir.ump.edu.my/id/eprint/745/ http://umpir.ump.edu.my/id/eprint/745/ http://umpir.ump.edu.my/id/eprint/745/1/MUHAMMAD_ASRAF_B_ABD_RAHIM.pdf |
| Summary: | This research was carried out to develop a gas density control model using Aspen Plus with Internal Model Control (IMC) method application for data generation purpose and to analyze on the process estimation using Partial Least Squares (PLS) regression. In making this process, the Air Flow Pressure Temperature (AFPT) pilot plant is use as the case study. The AFPT pilot plant is a process control training system (PCTS) that uses only air to simulate gas, vapor or steam. This AFPT pilot plant is a scale-down Real Industrial Process Plant built on 5ft X 10ft steel platform, complete with its own dedicated control panel. The AFPT pilot plant can be use to control the gas density by manipulating the pressure, flow, and temperature of the plant. This AFPT pilot plant then will be simulating using Aspen Plus to develop a gas density control model. The model will be run in steady-state and dynamic mode. In dynamic mode, the controller for all the parameters to control the gas density is putted. This entire controller then will be tune using the Internal Model Control (IMC) method in order to get its best performance. After the simulation is done, the gas density data generated from the simulation will be compared with the actual (experiment) data for validation of the data. The data shows that the error between the two data is less than 5%, meaning that the data generated from the simulation is valid. Then, this data will be use to develop a process estimator model using Partial Least Squares (PLS) method. After the estimation model is done, the mean squares error (MSE) between the estimated data and actual data is 0.001584743. This shows that the Partial Least Squares can be use as the estimator model for gas density control purpose and the estimation model developed is reliable. |
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