DocumentCode :
2768290
Title :
Design of State Estimators for the Inferential Control of an Industrial Distillation Column
Author :
Bahar, Almíla ; Güner, Evren ; Ozgen, Canan ; Halici, Ugur
Author_Institution :
Middle East Tech. Univ., Ankara
fYear :
0
fDate :
0-0 0
Firstpage :
1112
Lastpage :
1115
Abstract :
In the control of distillation columns, on-line composition measurements offer challenges. In this study, in order to predict the product compositions in an industrial multi-component distillation column from available on-line temperature measurements, two state estimators, an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS), are developed and tested by using an unsteady-state column simulator. A model predictive controller (MPC) is used with the developed estimators individually for the dual composition control of the column. The performances of the developed inferential control system utilizing the estimators are found to be satisfactory considering both set-point tracking and disturbance rejection cases.
Keywords :
distillation equipment; fuzzy neural nets; fuzzy reasoning; predictive control; production engineering computing; state estimation; temperature measurement; adaptive neuro-fuzzy inference system; artificial neural network; disturbance rejection; industrial distillation column; inferential control; model predictive controller; on-line composition measurements; on-line temperature measurements; set-point tracking; state estimators; unsteady-state column simulator; Adaptive systems; Artificial neural networks; Control systems; Distillation equipment; Electrical equipment industry; Industrial control; Predictive models; State estimation; System testing; Temperature measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
Type :
conf
DOI :
10.1109/IJCNN.2006.246814
Filename :
1716225
Link To Document :
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