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.
Czech title
Optimalizace statistických procesů (PASPO) pro zlepšování procesů v rafinériích
English title
Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries
Type
journal article in Web of Science
Language
en
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.
Czech abstract
Větší petrochemické závody čelí v průmyslové praxi novým problémům s návrhem i efektivním řízením. Zvyšuje se totiž důraz na jejich energetickou efektivitu i ohleduplnost vůči životnímu prostředí. Tradiční optimalizační metody přináší obvykle velmi přesná řešení pro celou řadu úloh procesního inženýrství. Pro optimalizaci rozsáhlejšího průmyslového závodu však tyto metody nedostačují. Jsou totiž závislé na přesnosti navrženého modelu a v zájmu optimalizace často vyžadují nadměrné zásahy do procesu. Článek řeší tento problém pomocí speciální formulace tzv. analýzy hlavních komponent (PCA) a technikou plánovaných experimentů (DoE). Tyto postupy jsou základem statistické optimalizace procesů pro průmyslové rafinérie. Přínosem práce je popis nové efektivní metody pro optimalizaci celého závodu založenou na provozních datech a zaměřenou na ochranu životního prostředí.
English 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 in English
Principal Component Analysis, Design of experiment, Plant-wide optimisation, Statistical process optimisation, PASPO, Big data analytics
Released
10.07.2019
Publisher
Elsevier
Location
Oxford, England
ISSN
0959-6526
Volume
225
Number
1
Pages from–to
359–375
Pages count
17
BIBTEX
@article{BUT156780,
author="Vítězslav {Máša} and Sin Yong {Teng} and Bing Shen {How} and Pavel {Kuba} 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",
year="2019",
volume="225",
number="1",
month="July",
pages="359--375",
publisher="Elsevier",
address="Oxford, England",
issn="0959-6526"
}