• Zainab Kayat Group Technical Solution, PETRONAS, Kuala Lumpur, Malaysia
  • A Rahim Othman Group Technical Solution, PETRONAS, Kuala Lumpur, Malaysia
  • Hemadevi Balasubramaniam Group Technical Solution, PETRONAS, Kuala Lumpur, Malaysia


PETRONAS developed a uniquely fast and exact automated fuel blending system that has accelerated the release of quality certification and achieved the perfect gasoline product blend for a Malaysian Refinery. Blending is a critical production process. Up to 13 different hydrocarbon streams from various feedstocks must be precisely blended to produce petroleum products such as Unleaded Gasoline RON 95. The ratio between flow velocities of different blending component streams is of great importance to ultimately achieve the target quality of the product. The ratio is determined based on the individual quality of the blending component. The underlying concept of this innovation is based on the strategy of an online blending with intelligent and fast chemometrics multivariate data analytics to make the necessary adjustment in the ratio of the blending components to meet the product quality. One of the integral parts of chemometrics multivariate data analytics is a spectroscopic analyser equipped with calibration models which enable multivariate prediction. The integration of the spectroscopic analyser and customised blend property control is an authoritarian system whereby it provides real-time data on fuel quality and enables online blending monitoring and control to happen seamlessly. PETRONAS has crafted an automated and integrated online blending system. This innovation has resulted in a more innovative product blending with zero re-blend and minimal quality giveaway in line with Industry 4.0 Best Practice.

Keywords: Chemometrics multivariate modelling, online blending, spectroscopy


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How to Cite
KAYAT, Zainab; OTHMAN, A Rahim; BALASUBRAMANIAM, Hemadevi. SMART PRODUCT BLENDING SYSTEM TO MAXIMISE QUALITY. Platform : A Journal of Science and Technology, [S.l.], v. 4, n. 1, p. 3-7, may 2021. ISSN 2637-0530. Available at: <>. Date accessed: 21 may 2022.