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 Technology

Near infrared spectra prediction

of hydrocarbons Intertek describes how its chemometric modelling software can help refiners to optimise production. ITH THE CONTINUED increase in the use of opportunity oils, there has never been a greater need for the fullest understanding of the quality of every crude delivery. However, although it is imperative to maintain assay data, carrying out full suites of laboratory tests on every crude delivery would be a costly and impractical exercise. Conversely, sub-optimal operation through varying crude quality will result in erosion of margins. So what is the solution? Intertek possesses a suite of software (Interpret) used for a variety of applications throughout the hydrocarbon supply chain. This software has been successfully applied online, at-line and in the laboratory for many applications. Interpret is composed of several modules, each one designed to meet different industrial challenges. The Interpret module InBlend is a chemometric modelling software based on near infrared (NIR) spectra and currently used in applications ranging from pipeline modelling, through refineries (feedstock quality monitoring and blend optimisation) to fuels blending (gasoline, jet, diesel etc.). The NIR spectrum is used to predict crude oil composition from a tuned chemometric model. Obtaining composition by NIR prediction is a very quick method, typically an InBlend prediction takes a few minutes and predicts many properties including, but not limited to, API gravity, sulphur, total acid number (TAN), pour point, true boiling point (TBP) curve and TBP fraction properties such as specific gravity and molecular weight. Intertek also possesses a database of global crudes containing circa.1,200 crude oils with NIR spectra and associated TBP, API, sulphur, pour point and TAN. When used in conjunction with InBlend for crude modelling applications, the database allows customers to enter into projects with a working chemometric model, which can then be tuned to specific applications.

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As well as predicting the properties of a crude oil sample purely from a NIR spectrum, this case study also highlights the advantages that the technology can give refiners. It achieves this by analysing the variation of a given crude type in this case with the sanitised name “XX”. The case study focuses on four samples of XX delivered to a refinery in chronological order over a period of eight months. Table 1 shows the crudes being scrutinised in this case study. The normal measured values are API gravity and wt% sulphur. Table 1

Case study In this case study InBlend is applied in a large refinery for quality monitoring. The application included setting up the model to predict the properties of crude deliveries and benchmarking this against expected crude quality.

SampleID

API

Sulphur

565°C+

Sample_01 – 05/09/11

32.8

0.9

7.6

Sample_02 – 06/11/11

33.8

0.8

6.0

Sample_03 – 19/01/12

37.9

0.3

11.6

Sample_04 – 23/02/12

33.0

0.8

6.2

Table 1: Four samples of type XX with their API, sulphur and the cut 565oC+ from TBP

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Issue 7 2016

Figure 1: Aggregate plots of all example crudes showing API aggregate (top) and 565oC+ aggregate (bottom)

Profile for Alain Charles Publishing

Oil Review Middle East 7 2016  

Oil Review Middle East 7 2016