MODELING AND DESIGN OF OPTIMAL CONTROL OF BATCH TYPE DISTILLATION COLUMN SYSTEM WITH LINEAR QUADRATIC TRACKING APPROACH
Today, the chemical industry is quite numerous and massive. One application of the chemical industry that is still widely used today is distillation. Distillation is a process of separation or separation of a solution. One type of distillation based on the mode of operation is batch distillation....
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| 格式: | Theses |
| 語言: | Indonesia |
| 在線閱讀: | https://digilib.itb.ac.id/gdl/view/60925 |
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| 總結: | Today, the chemical industry is quite numerous and massive. One application of
the chemical industry that is still widely used today is distillation. Distillation is a
process of separation or separation of a solution. One type of distillation based on
the mode of operation is batch distillation. Batch distillation is the process of
separating the solution only once (not repeated). The mathematical model of this
system does not meet the theory of superposition, or is nonlinear. The application
of control theory that is currently being carried out is applied to linear systems.
One of the challenges in performing operations on the distillation column is the
optimization problem. The optimization problem on the batch distillation column is
divided into two, namely column optimization and optimal control. In this thesis,
we will implement Linear Quadratic Tracking (LQT) to fulfill the optimal control
problem. Therefore, system modeling was carried out using several methods,
namely the nonlinear function approach of ARX using the Neural Network (NARXNN), approximation to the linear form of ARMA using the Neural Network (ARMANN), and the direct approach (ARMA-direct method). Based on the test results on
the model obtained from the identification process, LQT is proven to be able to
produce optimization in the batch distillation process. This is based on the resulting
output response, because it has been able to track the given setpoint. In addition,
the control parameters obtained from solving the Riccati Equation have succeeded
in ensuring the stability of the system because all closed-loop eigenvalues are
located within the unity circle. As for system modeling, the best identification
process is using NARX-NN based on the MSE value generated during testing with
the parameters of the number of input and output delays selected. However, for
control purposes, the approximation to the linear form of ARMA using a Neural
Network gives a smaller MSE value than the direct approach for the number of
input and output delays of 2 and 3. obtained using the ARMA-NN method is used
as a mathematical model. |
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