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Lookup NU author(s): Funmi Osuolale, Dr Jie ZhangORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
This paper presents a methodology for optimising the exergy efficiency of atmospheric distillation unit without trading off the products qualities and process throughput. The presented method incorporates the second law of thermodynamics in data driven models. Bootstrap aggregated neural networks (BANN) are used for enhanced model accuracy and reliability. The standard error of the individual neural network predictions is taken as the indication of model prediction reliability and is incorporated in the optimization objective function. The economic analysis of the recoverable energy (sum of internal and external exergy losses) reveals the energy saving potential of the proposed method, which will aid the design and operation of energy efficient atmospheric distillation columns.
Author(s): Osuolale FN, Zhang J
Publication type: Article
Publication status: Published
Journal: Computers & Chemical Engineering
Year: 2017
Volume: 103
Pages: 201-209
Print publication date: 04/08/2017
Online publication date: 31/03/2017
Acceptance date: 29/03/2017
Date deposited: 30/03/2017
ISSN (print): 0098-1354
ISSN (electronic): 1873-4375
Publisher: Elsevier
URL: https://doi.org/10.1016/j.compchemeng.2017.03.024
DOI: 10.1016/j.compchemeng.2017.03.024
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