Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
State estimation and inferential control for a reactive batch distillation column
Date
2010-03-01
Author
Bahar, Almila
Özgen, Canan
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
69
views
0
downloads
Cite This
An optimal reflux ratio profile is obtained for a reactive batch distillation system utilizing the capacity factor as the objective function in a nonlinear optimization problem. Then, an Artificial Neural Network (ANN) estimator system, which utilizes the use of several ANN estimators, is designed to predict the product composition values of the distillation column from temperature measurements inferentially. The network used is an Elman network with two hidden layers. The designed estimator system is used in the feedback inferential control algorithm, where the estimated compositions and the reflux ratio information are given as inputs to the controller to see the performance of the ANN. In the control law, a scheduling policy is used and the optimal reflux ratio profile is considered as pre-defined set-points. it is found that, it is possible to control the compositions in this dynamically complex system by using the designed ANN estimator system with error refinement whenever necessary
Subject Keywords
Reactive Distillation
,
Artificial Neural Networks
,
State Estimation
,
Optimization
,
Batch Column
URI
https://hdl.handle.net/11511/31119
Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
DOI
https://doi.org/10.1016/j.engappai.2009.11.003
Collections
Graduate School of Natural and Applied Sciences, Article
Suggestions
OpenMETU
Core
State estimation of induction motor using unscented Kalman filter
Akin, B; Orguner, Umut; Ersak, A (2003-06-25)
In this paper, a new estimation technique, unscented Kalman filter (UKF) is applied to state observation in field oriented control (FOC) of induction motor. UKF, a recent derivative-free nonlinear estimation tool, is used for estimating rotor speed and fluxes using sensed stator current and voltages. In the simulations, UKF, whose several intrinsic properties suggest its use over EKF in highly nonlinear systems, turned out to be very similar to EKF in flux estimates. The simulation results also show that UK...
Temperature dependence of the damping constant and the order parameter close to the lambda phase transitions in ammonium halides
Yurtseven, Hasan Hamit (Elsevier BV, 2006-10-01)
This study gives our calculation for the damping constant, using the expressions derived for an Ising pseudospin-phonon coupled system in the ammonium halides (NH4Cl and NH4Br). For this calculation of the damping constant, we use the temperature dependence of the order parameter calculated from the molecular field theory. We predict here the damping constants for the v(5) (174 cm(-1)) and v(5) (177 cm(-1)) Raman modes of NH4Cl and NH4Br, respectively, below T-lambda and compare them with our observed bandw...
Diffusion Resistances and Contribution of Surface Diffusion in TAME and TAEE Production Using Amberlyst-15
Doğu, Timur; Boz, Nezahat; MÜRTEZAOĞLU, KIRALİ; DOĞU, GÜLŞEN (2003-01-01)
Effective diffusivities and adsorption equilibrium constants of methanol, ethanol and 2-methyl-2-butene (2M2B), in Amberlyst 15, were evaluated from batch adsorption experiments. Moment expressions derived for different models involving diffusion resistances in the macropores and within the gel-like micrograins were used for the evaluation of effective diffusion coefficients. Contribution of surface diffusion to diffusion flux within the macropores was found to be quite significant. Also, it was found that ...
An artificial neural network estimator design for the inferential model predictive control of an industrial multi-component distillation column
Bahar, Almila; Özgen, Canan; Department of Chemical Engineering (2003)
An inferential control methodology, that utilizes an artificial neural network (ANN) estimator for a model predictive controller, is developed for an industrial multi-component distillation column. In the column, propane and butane is separated from a mixture of propane, n-butane, i-butane, and i-pentane with a top product purity of 96% propane and a bottom product purity of 63% n- butane. Dual composition control of the column must be used in a multivariable model predictive controller for an efficient ope...
State estimator design for multicomponent batch distillation columns
Yildiz, U; Gurkan, UA; Ozgen, C; Leblebicioğlu, Mehmet Kemal (Elsevier BV, 2005-05-01)
In the control of batch distillation columns, one of the problems is the difficulty in monitoring the compositions. This problem can be handled by estimating the compositions from readily available online temperature measurements using a state estimator. In this study, a state estimator that infers the product composition in a multicomponent batch distillation column (MBDC) from the temperature measurements is designed and tested using a batch column simulation. An extended Kalman filter (EKF) is designed a...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
A. Bahar and C. Özgen, “State estimation and inferential control for a reactive batch distillation column,”
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
, pp. 262–270, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31119.