Publication detail

Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries

TENG, S. HOW, B. LEONG, W. TEOH, J. CHEE, A. MOTAVASEL, R. LAM, H.

Original Title

Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries

Type

journal article in Web of Science

Language

English

Original Abstract

Integrated refineries and industrial processing plant in the real-world always face management and design difficulties to keep the processing operation lean and green. These challenges highlight the essentiality to improving product quality and yield without compromising environmental aspects. For various process system engineering application, traditional optimisation methodologies (i.e., pure mix-integer non-linear programming) can yield very precise global optimum solutions. However, for plant-wide optimisation, the generated solutions by such methods highly rely on the accuracy of the constructed model and often require an enumerate amount of process changes to be implemented in the real world. This paper solves this issue by using a special formulation of correlation-based principal component analysis (PCA) and Design of Experiment (DoE) methodologies to serve as statistical process optimisation for industrial refineries. The contribution of this work is that it provides an efficient framework for plant-wide optimisation based on plant operational data while not compromising on environmental impacts.

Keywords

Principal Component Analysis, Design of experiment, Plant-wide optimisation, Statistical process optimisation, PASPO, Big data analytics

Authors

TENG, S.; HOW, B.; LEONG, W.; TEOH, J.; CHEE, A.; MOTAVASEL, R.; LAM, H.

Released

10. 7. 2019

Publisher

Elsevier

Location

Oxford, England

ISBN

0959-6526

Periodical

Journal of Cleaner Production

Year of study

225

Number

1

State

United Kingdom of Great Britain and Northern Ireland

Pages from

359

Pages to

375

Pages count

17

URL

http://www.sciencedirect.com/science/article/pii/S0959652619309825

Full text in the Digital Library

http://hdl.handle.net/11012/195644

BibTex

@article{BUT156780,
  author="Sin Yong {Teng} and Bing Shen {How} and Wei Dong {Leong} and Jun Hau {Teoh} and Adrian Siang Cheah {Chee} and Roxana Zahra {Motavasel} and Lam {Hon Loong}",
  title="Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries",
  journal="Journal of Cleaner Production",
  year="2019",
  volume="225",
  number="1",
  pages="359--375",
  doi="10.1016/j.jclepro.2019.03.272",
  issn="0959-6526",
  url="http://www.sciencedirect.com/science/article/pii/S0959652619309825"
}

Documents

MPRA_paper_94058-pages-deleted.pdf