Compatibility of Crude Oil Blends─Processing Issues Related to Asphaltene Precipitation, Methods of Instability Prediction─A Review
Industrial & Engineering Chemistry Research
Processing crude oil of variable composition, especially due to the need to process crude oil blends obtained from various sources, presents a tremendous process challenge. This is mainly due to the destabilization of the colloidal system manifested mostly by the precipitation of the asphaltene fraction. The precipitation of asphaltenes from crude oil is a serious problem during extraction, transport, and processing. In the latter case, engineers and scientists have spent a lot of time determining what mechanisms are conducive to the occurrence of this phenomenon. On the one hand, there was a scientific curiosity about the principles of the nanoworld (nanoscale) of asphaltene molecules that determine their stability, and on the other hand, the willingness of process engineers in refineries to maintain the equipment in the best condition and maximize plant profits. Over the years, many methods have been developed to assess the stability of asphaltenes in crude oils and their blends, starting with methodologies based on the separation of a complex mixture into basic groups of compounds with similar properties (SARA) to sophisticated numerical models on an increasingly better understanding of interactions between molecules under changing conditions. In the former case, the basic instruments available in every laboratory are used whereas in the latter case technically advanced measurement systems capable of reproducing the real conditions of crude oil processing are employed. This paper reviews the methods of determining the stability of crude oils and their blends along with a critical assessment of their effectiveness.
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© 2022 The Authors. Published by American Chemical Society
Bambinek, Krzysztof; Przyjazny, Andrzej; and Boczkaj, Grzegorz, "Compatibility of Crude Oil Blends─Processing Issues Related to Asphaltene Precipitation, Methods of Instability Prediction─A Review" (2022). Natural Sciences Publications. 17.