At each simulation initialization step, using a
communication system between HYSYS and MATLAB, the
bottom and top composition, the feed flow and composition
are sent from the process to the control structure, in
MATLAB. Here, using these data the model parameters (time
constants and gain) are determined and loaded in controller.
Using these model parameters, the two controlled variables
(reflux flow and bottom product flow) are computed and sent
at each sampling instant to the process, in HYSYS.
In order to outline the benefits of this IMC method, which
will be further referred as Nonlinear Adaptable Internal Model
Control (NAIMC), a linear method was implemented, also.
This linear IMC method which uses a general linear process
model will be further referred as non-Adaptable Internal
Model Control (nAIMC).
The dynamic system behavior analysis consisted of
modifying the compositions setpoint and the controllers tuning
parameter.
The proposed top and bottom composition controllers (Fig.
6) have implemented the process model represented as sets of
linear transfer functions (5) with the model parameter values
from table 1.
At each simulation initialization step, using acommunication system between HYSYS and MATLAB, thebottom and top composition, the feed flow and compositionare sent from the process to the control structure, inMATLAB. Here, using these data the model parameters (timeconstants and gain) are determined and loaded in controller.Using these model parameters, the two controlled variables(reflux flow and bottom product flow) are computed and sentat each sampling instant to the process, in HYSYS.In order to outline the benefits of this IMC method, whichwill be further referred as Nonlinear Adaptable Internal ModelControl (NAIMC), a linear method was implemented, also.This linear IMC method which uses a general linear processmodel will be further referred as non-Adaptable InternalModel Control (nAIMC).The dynamic system behavior analysis consisted ofmodifying the compositions setpoint and the controllers tuningparameter.The proposed top and bottom composition controllers (Fig.6) have implemented the process model represented as sets oflinear transfer functions (5) with the model parameter valuesfrom table 1.
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