An Artificial Intelligence Method for Energy Efficient Operation of Crude Distillation Units under Uncertain Feed Composition

Muhammad Amin Durrani, Iftikhar Ahmad, Manabu Kano and Shinji Hasebe
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Muhammad Amin Durrani: School of Chemical and Materials Engineering, National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan
Iftikhar Ahmad: School of Chemical and Materials Engineering, National University of Sciences and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan
Manabu Kano: Department of Systems Science, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
Shinji Hasebe: Department of Chemical Engineering, Kyoto University, Nishikyo-ku, Kyoto 615-8510, Japan

Energies, 2018, vol. 11, issue 11, 1-12

Abstract: The crude distillation unit (CDU) is one of the most energy-intensive processes of a petroleum refinery. The composition of crude is subject to change on regular basis. The uncertainty in crude oil composition causes wastage of a substantial amount of energy in the CDU operation. In this study, a novel approach based on a multi-output artificial neural networks (ANN) model was devised to cope with variations (uncertainty) in crude composition. The proposed method is an extended version of another method of cut-point optimization based on hybridization of Taguchi and genetic algorithm. A data comprised of several hundred variations of crude compositions and their optimized cut point temperatures, derived from the hybrid approach, was used to train the ANN model. The proposed method was validated on a simulated CDU flowsheet for a Pakistani crude, i.e., Zamzama. The proposed method is faster and computationally less expensive than the hybrid method. In addition, it can efficiently predict optimum cut point temperatures for any variant of the crude composition.

Keywords: crude distillation unit; energy efficiency; Taguchi method; genetic algorithm; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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