State estimation and inferential control for a reactive batch distillation column

Bahar, Almila
Özgen, Canan
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


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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...
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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
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: