Detail publikačního výsledku

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.

Originální název

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

Anglický název

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

Druh

Článek WoS

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

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

Klíčová slova v angličtině

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

Autoři

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

Rok RIV

2019

Vydáno

10.07.2019

Nakladatel

Elsevier

Místo

Oxford, England

ISSN

0959-6526

Periodikum

Journal of Cleaner Production

Svazek

225

Číslo

1

Stát

Spojené státy americké

Strany od

359

Strany do

375

Strany počet

17

URL

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

Plný text v Digitální knihovně

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"
}

Dokumenty

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