Abstract
This article gives the brief overview of the important sectors in the petroleum industry and also discusses the application of machine learning in those sectors for automation. This article also highlights the key issues in the downstream sector where adulteration is the primary concern faced by the retailer and consumer. The review of existing applications of ML in the petroleum industry is studied, and the new open-research challenges are discussed. The aim of this article is to show the new directions of research in the less explored downstream sector of the petroleum industry for automation.
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Hemachandiran, S., Aghila, G., Siddharth, R. (2022). Automation to Find Adulteration in Downstream Petroleum Monitoring Using Machine Learning: An Overview. In: Natarajan, S.K., Prakash, R., Sankaranarayanasamy, K. (eds) Recent Advances in Manufacturing, Automation, Design and Energy Technologies. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-4222-7_48
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DOI: https://doi.org/10.1007/978-981-16-4222-7_48
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