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Article

Partial Least Squares Regression of Oil Sands Processing Variables within Discrete Event Simulation Digital Twin

1
Department of Mining and Materials Engineering, McGill University, 3610 University Street, Montreal, QC H3A 0C5, Canada
2
National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada
3
Corem, 1180 Rue de la Minéralogie, Quebec, QC G1N 1X7, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Samintha Perera
Minerals 2021, 11(7), 689; https://doi.org/10.3390/min11070689
Received: 30 April 2021 / Revised: 8 June 2021 / Accepted: 22 June 2021 / Published: 26 June 2021
Oil remains a major contributor to global primary energy supply and is, thus, fundamental to the continued functioning of modern society and related industries. Conventional oil and gas reserves are finite and are being depleted at a relatively rapid pace. With alternative fuels and technologies still unable to fill the gap, research and development of unconventional petroleum resources have accelerated markedly in the past 20 years. With some of the largest bitumen deposits in the world, Canada has an active oil mining and refining industry. Bitumen deposits, also called oil sands, are formed in complex geological environments and subject to a host of syn- and post-depositional processes. As a result, some ores are heterogeneous, at both individual reservoir and regional scales, which poses significant problems in terms of extractive processing. Moreover, with increased environmental awareness and enhanced governmental regulations and industry best practices, it is critical for oil sands producers to improve process efficiencies across the spectrum. Discrete event simulation (DES) is a computational paradigm to develop dynamic digital twins, including the interactions of critical variables and processes. In the case of mining systems, the digital twin includes aspects of geological uncertainty. The resulting simulations include alternate operational modes that are characterized by separate operational policies and tactics. The current DES framework has been customized to integrate predictive modelling data, generated via partial least squares (PLS) regression, in order to evaluate system-wide response to geological uncertainty. Sample computations that are based on data from Canada’s oil sands are presented, showing the framework to be a powerful tool to assess and attenuate operational risk factors in the extractive processing of bitumen deposits. Specifically, this work addresses blending control strategies prior to bitumen extraction and provides a pathway to incorporate geological variation into decision-making processes throughout the value chain. View Full-Text
Keywords: discrete event simulation; digital twin; modes of operation; geological uncertainty; multivariate statistics; partial least squares regression; oil sands; bitumen extraction; bitumen processability discrete event simulation; digital twin; modes of operation; geological uncertainty; multivariate statistics; partial least squares regression; oil sands; bitumen extraction; bitumen processability
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MDPI and ACS Style

Wilson, R.; Mercier, P.H.J.; Patarachao, B.; Navarra, A. Partial Least Squares Regression of Oil Sands Processing Variables within Discrete Event Simulation Digital Twin. Minerals 2021, 11, 689. https://doi.org/10.3390/min11070689

AMA Style

Wilson R, Mercier PHJ, Patarachao B, Navarra A. Partial Least Squares Regression of Oil Sands Processing Variables within Discrete Event Simulation Digital Twin. Minerals. 2021; 11(7):689. https://doi.org/10.3390/min11070689

Chicago/Turabian Style

Wilson, Ryan, Patrick H.J. Mercier, Bussaraporn Patarachao, and Alessandro Navarra. 2021. "Partial Least Squares Regression of Oil Sands Processing Variables within Discrete Event Simulation Digital Twin" Minerals 11, no. 7: 689. https://doi.org/10.3390/min11070689

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