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Modelling and Optimisation of a Crude Oil Hydrotreating Process Using Neural Networks

Lookup NU author(s): Wissam Muhsin, Dr Jie ZhangORCiD, Dr Jonathan LeeORCiD



This is the final published version of a conference proceedings (inc. abstract) that has been published in its final definitive form by Italian Association of Chemical Engineering, 2016.

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This paper presents a study on the data-driven modelling and optimisation of a crude oil hydrotreating process using bootstrap aggregated neural networks. Hydrotreating (HDT) is a chemical process that can be widely used in crude oil refineries to remove undesirable impurities like sulphur, nitrogen, oxygen, metal and aromatic compounds. In order to enhance the operation efficiency of HDT process for crude oil refining, process optimisation should be carried out. To overcome the difficulties in building detailed mechanistic models, Bootstrap aggregated neural network models are developed from process operation data. In this paper, a crude oil HDT process simulated using Aspen HYSYS is used as a case study. It is shown that bootstrap aggregated neural network gives more accurate and reliable predictions than single neural networks. The neural network model based optimisation results are validated on HYSYS simulation and are shown to be effective.

Publication metadata

Author(s): Muhsin WAS, Zhang J, Lee J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 19th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction (PRES 2016)

Year of Conference: 2016

Pages: 211-216

Online publication date: 27/08/2016

Acceptance date: 02/04/2016

Date deposited: 09/08/2018

ISSN: 2283-9216

Publisher: Italian Association of Chemical Engineering


DOI: 10.3303/CET1652036

Library holdings: Search Newcastle University Library for this item

Series Title: Chemical Engineering Transactions

ISBN: 9788895608426