The present study proposes a new PI controller tuning
method using
extended predictive control (EPC). The PI controller parameter values
are calculated using the EPC controller output and its closed-loop
response. This provides a simple and an effective tuning strategy
which results in an improved closed-loop response compared to conventional
tuning methods. The tuning methodology is applicable for single input
single output and multi input multi output stable processes. Simulation
and experimental results reveal the efficacy of the method under plant
uncertainty conditions.
This paper addresses
the energy consumption of distillation process
via an actuator, which is a challenging problem in process industries.
Precise control action would enhance energy consumption and improve
the productivity. This paper is an experimental validation of EPC-PI
control algorithm and analysis of distillate purity of a lab-scale
distillation column. The PI control scheme uses closed-loop data of
extended predictive controller (EPC) that has been performed through
off-line simulation. The performance of control method is compared
with different schemes such as Hägglund’s one-third
rule and Skogestad’s overshoot method. The issue of integral
windup in the multivariable process is addressed in the aspect of
optimal energy consumption. The energy consumption calculations are
made with respect to power utility of actuators throughout the process.
The distillate product of post-controller implementation is processed
to qualitative analysis using UV spectroscopy. Performance index is
carried out via integral time absolute error (ITAE) by perturbing
plant parameters up to 30% uncertainty.
Batch process plays
a very crucial and important role in process
industries. The increased operational flexibility and trend toward
high-quality, low-volume chemical production has put more emphasis
on batch processing. In this work, nonlinearities associated with
the batch reactor process have been studied. ARX and NARX models have
been identified using open-loop data obtained from the pilot plant
batch reactor. The performance of the batch reactor with conventional
linear controllers results in aggressive manipulated variable action
and larger energy consumption due to its inherent nonlinearity. This
issue has been addressed in the proposed work by identifying the nonlinear
model and designing a nonlinear model predictive controller for a
pilot plant batch reactor. The implementation of the proposed method
has resulted in smooth response of the manipulated variable as well
as reactor temperature on both simulation and real-time experimentation.
Batch reactors are
large vessels in which chemical reactions take
place. They are mostly found to be used in process control industries
for processes such as reactant mixing, waste treatment of leather
byproducts, and liquid extraction. Modeling and controlling of these
systems are complex due to their highly nonlinear nature. The Wiener
neural network (WNN) is employed in this work to predict and track
the temperature profile of a batch reactor successfully. WNN is different
from artificial neural networks in various aspects, mainly its structure.
The brief methodology that was deployed to complete this work consisted
of two parts. The first part is modeling the WNN-based batch reactor
using the provided input–output data set. The input is feed
given to the reactor, and the reactor temperature needs to be maintained
in line with the optimal profile. The objective in this part is to
train the neural network to efficiently track the nonlinear temperature
profile that is provided from the data set. The second part is designing
a generalized predictive controller (GPC) using the data obtained
from modeling the reactor to successfully track any arbitrary temperature
profile. Therefore, this work presents the experimental modeling of
a batch reactor and validation of a WNN-based GPC for temperature
profile tracking.
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