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The Importance of An Effective Ethanol Uplift Model

By Kai Y. Wan

“How to establish a good ethanol uplift model?” is becoming an increasingly common question when it comes to gasoline blending. With the mandatory addition of 10% ethanol to a majority of gasoline products, accurate prediction of the final fuel properties is more crucial than ever. With years of experience in blending optimization projects, at Trindent we have developed specific expertise in this area, and here are some insights:

Ethanol Uplift Model Best Practices

Each refinery has its configurations that are developed as a result of the molecules they are making. Therefore, picking the right independent variables to start the development of the model is critical. It starts with understanding the principle of what Octane Number (ON) is, and how this property is related to the chemical molecules inside the gasoline. Typically, a refinery can use as much as 7-8 independent variables, and around 10-15 iterations before finding the perfect model which is not only accurate but also makes chemical sense. The complexity of the model is also important as you want to make it sophisticated enough to provide the accuracy you want, but also simple enough to integrate into the existing system with ease.

Managing Component Distribution

Once a model is developed – the next question would be – how do we optimize it? In addition to adjusting the ON of the neat blend stock (which is often the only approach most refineries take), refineries may take advantage of smart component distribution between different grades, and component sales and maximize the overall benefit of ON uplift.

Going Beyond ON

While a lot of focus on the ethanol model is about ON, do not forget that other properties are affected by the addition of ethanol, too. Therefore, a mature ethanol model system would consider other key properties such as RVP, distillation, and V/L. A good ethanol model system would allow a refinery to achieve method repeatability level giveaway for all their constrained parameters, and maximize the benefit from production cost management.

The author of this blog, Kai Y. Wan is an Engagement Manager at Trindent Consulting.