Modeling and inferential control studies are carried out on a reactive batch
distillation system for the esterification reaction of ethanol with acetic acid to
produce ethyl acetate. A dynamic model is developed based on a previous study
done on a batch distillation column. The column is modified for a reactive system
where Artificial Neural Network Estimator is used instead of Extended Kalman
Filter for the estimation of compositions of polar compounds for control purposes.
The results of the developed dynamic model of the column is verified theoretically
with the results of a similar study. Also, in order to check the model
experimentally, a lab scale column (40 cm height, 5 cm inner diameter with 8
trays) is used and it is found that experimental data is not in good agreement
with the models&rsquo / . Therefore, the model developed is improved by using different
rate expressions and thermodynamic models (fi-fi, combination of equations of
state (EOS) and excess Gibbs free energy (EOS-Gex), gama-fi) with different
equations of states (Peng Robinson (PR) / Peng Robinson - Stryjek-Vera (PRSV)),
mixing rules (van der Waals / Huron Vidal (HV) / Huron Vidal Original (HVO) /
Orbey Sandler Modification of HVO (HVOS)) and activity coefficient models (NRTL
/ Wilson / UNIQUAC). The gama-fi method with PR-EOS together with van der
Waals mixing rule and NRTL activity coefficient model is selected as the best
relationships which fits the experimental data. The thermodynamic models / EOS,
mixing rules and activity coefficient models, all are found to have very crucial
roles in modeling studies.
A nonlinear optimization problem is also carried out to find the optimal operation
of the distillation column for an optimal reflux ratio profile where the
maximization of the capacity factor is selected as the objective function.
In control studies, to operate the distillation system with the optimal reflux ratio
profile, a control system is designed with an Artificial Neural Network (ANN)
Estimator which is used to predict the product composition values of the system
from temperature measurements. The network used is an Elman network with
two hidden layers. The performance of the designed network is tested first in
open-loop and then in closed-loop in a feedback inferential control algorithm. It is
found that, the control of the product compositions with the help of an ANN
estimator with error refinement can be done considering optimal reflux ratio
profile.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12608344/index.pdf |
| Date | 01 May 2007 |
| Creators | Bahar, Almila |
| Contributors | Ozgen, Canan |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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