Journal of Automation and Control
ISSN (Print): 2372-3033 ISSN (Online): 2372-3041 Website: Editor-in-chief: Santosh Nanda
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Journal of Automation and Control. 2014, 2(2), 49-56
DOI: 10.12691/automation-2-2-3
Open AccessArticle

ANFIS Based Identification and Control of Distillation Process

M. Shahbazian1, H. Jazayerirad1 and M. Ebnali1,

1Department of Instrumentation and Automation Engineering, Ahwaz Faculty of Petroleum Engineering, Ahwaz, Iran

Pub. Date: May 26, 2014

Cite this paper:
M. Shahbazian, H. Jazayerirad and M. Ebnali. ANFIS Based Identification and Control of Distillation Process. Journal of Automation and Control. 2014; 2(2):49-56. doi: 10.12691/automation-2-2-3


Typical production objectives in distillation process require the delivery of products whose compositions meets certain specifications. The distillation control system, therefore, must hold products composition as near the set points as possible in the faces of upsets. Since product quality cannot be measured easily and economically online, the control of product quality is often achieved by maintaining a suitable tray temperature near its set point. Tray temperature control method, however, is not a proper option for a multi-component distillation column, because the tray temperature does not correspond exactly to the product composition. To overcome this problem, secondary measurements can be used to infer the product quality and adjust the values of the manipulated variables. In this paper we have used a novel adaptive network fuzzy inference system (ANFIS) based inferential control approach for distillation process. ANFIS is used for identifying the distillation process and building two composition estimators to estimate the compositions of the bottom and sidestream products. The developed estimators are tested and results show that the predictions made by ANFIS structure are in good agreement with the results of simulation by ASPEN HYSYS process simulation package. In addition inferential control by implementation of ANFIS based online composition estimators is more superior to traditional tray temperature control method because of less integral time absolute error.

distillation column process identification adaptive network fuzzy inference system inferential control

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