NIR Technology for Gasoline Blending: The Risk-Free Approach
The Easy
ir Gasoline program provides operating data on the projected performance of an NIR gasoline application in an on-line blender environment.
Easyir Gasoline allows refiners to test the capability of NIR for gasoline property measurement, while eliminating the performance and financial risks of an on-line project investment.
The laboratory approach of its program significantly reduces the initial investment while allowing a stepwise upgrade path to a process installation if performance is found to be satisfactory.
For a monthly fee, ABB will provide the equipment, training and chemometric modeling expertise to prove the performance of NIR in your gasoline blender.
At the end of the program, you will have a definitive NIR performance guarantee that will minimize investment risk in any future on-line NIR blender projects. This will also allow you to calculate project payback with real performance data.
In this way, with Easyir Gasoline, the refiner can successfully implement an on-line NIR blender project in a stepwise, risk-free manner.
What streams & properties can be investigated in an Easyir Gasoline Program?
The streams can include all finished gasoline products including seasonal variations. Blendstocks can also be included if requested.
Properties that can be investigated include all finished gasoline products including RON, MON, RVP, ASTM Distillation, Aromatics, Olefins, Oxygenates, Benzene, E200, E300, among other parameters.
The defined scope is determined between ABB and the refiner at the outset of the project.
What are the benefits of Easyir Gasoline?
- Eliminate financial & performance risks associated with conventional NIR gasoline blender projects.
- Low initial investment in the implementation of gasoline blender NIR technology.
- Stepwise upgrade path from lab to process blender implementation.
- Obtain a definitive NIR performance guarantee.
- Calculate payback of your blender optimization project with real analytical performance data.