Sunday, June 26, 2016

Robust Prediction of Gasoline Properties Using Spectroscopic Methods Dynamically Corrected with Lab Data

Spectroscopic methods for measuring in-line blended gasoline have many advantages over conventional on-line analyzers, such as very fast, very precise measurements, simultaneous multiple parameter measurements, and very high reliability, at a reasonable cost. Spectroscopic analysis methods in this paper refer only to FTIR, Raman, and Magnetic Resonance Analysis.

The major shortcomings of using spectroscopic methods or any inferential model-based  parameter determination are:
·       Inaccurate inferential model predictions of parameters of interest, e.g. gasoline octane values,
·       Lack of a practical, how-to guide to build simple but reasonable inferential (chemometric) models.
·       Lack of a simple methodology to dynamically correct the predicted parameter value of an imperfect inferential model by comparing it with a credible value, e.g. Lab

This paper addresses these problems with a robust solution and scheme for reliable gasoline (or any fuel) blend property parameter prediction (chemometric) model development method, and dynamic correction of the model parameter prediction with Lab data.

The scheme is based on well-known principles of inferential model-based prediction of process stream quality in place of a real parameter measuring device, e.g. octane knock engine. The model output is corrected periodically with Lab data to within the ASTM reproducibility of that parameter.

The described approach has been used since 1960’s with the advent of practical process control computers to implement composition control without using on-line property analyzers [1], and later to validate octane knock engines and NIR-type spectroscopic analyzers since 1986 [2, 3], and is a derivative of tank quality integration used in in-line blend property control since its inception in 1965 [4].

The scheme is valid for:

·       * Any fuel blending (gasoline, diesel, bunker), or any mixture of liquids
·     *   Any parameter for which an inferential model can be developed, e.g. AKI, RVP, etc.
·       * In-line fuel blending scheme, either rundown blending or component tank blending
     Get a copy of full paper at


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