Abstract:
One of the key element of many industrial
process plants is a crude oil distillation
system. This system requires heat for the
vaporization of a mixture of feed to the
distillation column in vapor form. In the
existing process of distillation, an abnormal
change of heat is exhibited due to improper
control and monitoring of disturbances
affecting the plant. This would result for
undesired loss of product and product
purity to be reduced. The parameters that
are expected to be considered in the
analysis, model and control of a distillation
system described under the proposed study
are inlet feed temperature, distillation
column temperature, feed composition,
internal liquid and vapor composition, feed
flow rate, reboiler temperature and an
external reflux temperature to the
distillation tower.
Controlling distillation column parameters
with Adaptive model predictive control
allows for the determination of the
predicted future instant values of the plant
outputs. Using such control technique, the
controlled plant outputs such as the
temperature of distillation column, feed
preheater, re boiler and the upper reflux is
properly controlled and their parameters
are estimated using recursive least squares
approach in the entire process adaptation
mechanism. From the analysis and
optimization work made on the proposed
system, the efficiency of the plant outputs in
tracking their corresponding set point has
improved based on the value of the
transient system parameters as well as the
value of relative volatility of feed mixture to
the column. As per the finding in the
analysis of the process, 95.4% and 93.5%
improvement on the set point tracking and
to the amount of evaporation liquid feed has
been obtained respectively. On the other
hand an improvement on transient
parameters has been achieved to all plant
outputs. As per the result obtained from the
analysis, the peak overshoot, settling time
and peak time of the system response has
found to be less than 40% including the
effect of measured disturbance to the plant.
Hence, entire process variable optimization
has been performed using the parameters of
the model predictive controller to provide
the proper degree of stability. Finally the
proposed method of study has compared
with other control strategies through which
the performance of the proposed design has
been ensured
