Fuzzy logic control for a petroleum separation process

Authors:
R. F. Liao;C. W. Chan;J. Hromek;G. H. Huang;L. He
Affiliations:
Faculty of Engineering, University of Regina, Regina, Saskatchewan, Canada S4S 0A2;Faculty of Engineering, University of Regina, Regina, Saskatchewan, Canada S4S 0A2;Faculty of Engineering, University of Regina, Regina, Saskatchewan, Canada S4S 0A2;Faculty of Engineering, University of Regina, Regina, Saskatchewan, Canada S4S 0A2;Faculty of Engineering, University of Regina, Regina, Saskatchewan, Canada S4S 0A2
Venue:
Engineering Applications of Artificial Intelligence
Year:
2008

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Abstract

This paper presents an automated fuzzy logic controller (FLC) that can be used to improve oil quality produced from a separation process. Automated FLCs based on fuzzy logic are proven to be effective solutions for complex, nonlinear, or uncertain systems such as oil battery configurations. A crucial variable in quality control of crude oil separation is the gas-liquid ratio (GLR), and adjustments on this variable directly control the quality of oil produced during the separation process. The FLC controls the pressure and liquid levels within an oil separator and both variables are associated with the GLR. The objective of this study is to design and develop an automated fuzzy logic process controller for an oil battery based on the experience of petroleum engineers, who monitor and control the equipment of the production and separation facility. This paper discusses design and implementation of the FLC system including data fuzzification, knowledge base creation, and the defuzzification processes. This FLC system has been tested at a real-world petroleum site in Canada. The results indicated that the developed artificial intelligence-aided control system could improve output oil quality by reducing the difference between actual and desired GLR.