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

Author

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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)

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.

Suggested Citation

  • Muhammad Amin Durrani & Iftikhar Ahmad & Manabu Kano & Shinji Hasebe, 2018. "An Artificial Intelligence Method for Energy Efficient Operation of Crude Distillation Units under Uncertain Feed Composition," Energies, MDPI, vol. 11(11), pages 1-12, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2993-:d:179891
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    References listed on IDEAS

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