| Summary: | Throughout the hydrocarbon supply chain, process optimisation is driven by the desire to maximise profit margins. In the global refining marketplace, the biggest cost is crude oil and to improve margins increasing use of non-conventional crude oils (also called opportunity crudes) lowers the cost of the crude blend. Opportunity crudes are selected based on market forces, for example in North America, the production booms in shale oil and tar sands have provided ample amounts of new low-cost oils which refineries are buying and processing. However, as these oils are new to the marketplace many refineries have never processed them before which brings about challenges. These are mainly a lack of understanding of the quality of the crude oil being processed (shale oils for example can come from many thousands of wells) and how these oils interact with the more conventional refinery feedstocks (such as Brent or West Texas Intermediate). The Eng.D project was carried out in collaboration with Intertek Group plc, a multinational corporate organisation consisting of more than 42,000 employees in over 1,000 locations in over 100 countries across the globe, and was aimed at developing solutions to address crude oil processing problems. The issues covered over the course of the project fall into the areas of: enhancing understanding of crude oil quality, addressing issues of hydrocarbon blend stability because of blending and better utilisation of process data to promote efficiency and facilitate process troubleshooting. As such, the Eng.D project was firstly concerned with developing a robust chemometric model, based on Near Infrared spectra, for use in a major Asian refinery. Once built and tuned this model was ultimately used to predict physical properties (such as density, sulphur content and distillation properties) of every crude oil delivery and also online in the refinery for frequent prediction of crude oil blend properties. The second project was then aimed at solving refinery issues of the deposition of undesirable material (such as wax and asphaltenes) in pipes and process units. The research carried out during the course of the Eng.D project resulted in a patented approach to characterise these issues and provide refineries strategies to mitigate the problems. This approach is not just limited to crude oils but can be applied to any blended hydrocarbon streams and detects the precipitation of undesirable material using Near Infrared spectroscopy and microscopy. This ii approach has now been applied to solving problems of blending crude oils in refineries and offshore, heavy fuel oils, shale oils and marine fuels. Finally, the application of smart data analytics in an upstream installation was investigated. The objective of this application was to provide a customer with process troubleshooting for a historical recurring pump failure issue. To achieve this, the root cause of the issue first needed to be identified and then a solution developed.
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