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Inferential estimation of kerosene dry point in refineries with varying crudes

Lookup NU author(s): Dr Jie ZhangORCiD



A bootstrap aggregated model approach to the estimation of product quality in refineries with varying crudes is proposed in this paper. The varying crudes cause the relationship between process variables and product quality variables to change, which makes product quality estimation by soft-sensors a difficult problem. The essential idea in this paper is to build an inferential estimation model for each type of feed oil and use an on-line feed oil classifier to determine the feed oil type. Bootstrap aggregated neural networks are used in developing the on-line feed oil classifier and a bootstrap aggregated partial least square regression model is developed for each data group corresponding to each type of feed crude oil. The amount of training data in crude oil distillation is usually small and this brings difficulties for classification and estimation modelling. In order to enhance model reliability and robustness, bootstrap aggregated models are developed. The inferential estimation results of kerosene dry point on both simulated data and industrial data show that the proposed method can significantly improve the overall inferential estimation performance. (C) 2012 Elsevier Ltd. All rights reserved.

Publication metadata

Author(s): Zhou C, Liu QY, Huang DX, Zhang J

Publication type: Article

Publication status: Published

Journal: Journal of Process Control

Year: 2012

Volume: 22

Issue: 6

Pages: 1122-1126

Print publication date: 23/04/2012

Date deposited: 05/06/2014

ISSN (print): 0959-1524

ISSN (electronic): 1873-2771

Publisher: Elsevier Ltd


DOI: 10.1016/j.jprocont.2012.03.011


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Funder referenceFunder name
UK Department for Innovation, Universities and Skills under the UK/China Fellowship for Excellence programme
2012CB720500National Basic Research Program of China