Approach to Accurate Octane Number Calculation for Gasoline Blending

Abstract

The octane number of gasoline is one of the most important measures of gasoline quality to predict accurately the octane ratings of blending gasolines. This measured on a scale that ranges from that equivalent to isooctane (octane number of 100) to that of n-heptane (octane number of zero) octane no is effected by the saturates, aromatics, and olefins contents of gasoline. We take it as a standard and measure octane number by comparison with this standard. The accurate octane blending method will optimize the blending of gasoline components, when gasoline components are blended together, we will calculate the octane number of the blend with different octane number of the component or if the four components are of equal octane number. The blend octane number may be greater than, equal to or less than that calculated from the volumetric average of the octane numbers of the blend components, which indicates nonlinear blending. Blending would be linear if octane number of a blend was equal to that predicted by summing the octane numbers of the components in proportion to their concentrations. In practices, the discrepancies between the octane numbers of blends and the linearly predicted values have been correlated by specific empirical equations and these have been used to correct the linear predictions.

Optimizing temperature and introducing new process arrangements for elevating clay's longevity based on the known poisons in the separation process of trace olefins from aromatics

“…Gas Chromatography (GC) (CP-3800, Varian, USA) was used to determine feed and product compositions and this analysis is based on ASTM D-5134 standard with the Cpsil CB column (height and diameter of 50 m and 0.5 μm, respectively). 50,51 The GC-Cp-wax52 column (height and diameter of 25 m and one μm, respectively) was used to attain morpholine and N-formyl morpholine (NFM) contents of the feed and products. The GC condition is as follows: oven Temp.…”

A Methodology for Designing Octane Number of Fuels Using Genetic Algorithms and Artificial Neural Networks

“…Therefore, the models have evolved through the years to incorporate the complexity of component interactions as presented in eq 11, developed in 2019. 41 x a…”

“…53 Another ANN model has been presented with improved accuracy as its Root Mean Squared Error of Prediction (RMSEP) is less than 0.2 RON. 41 The model utilizes volumetric concentrations of the most employed refinery streams with their RON as an input to the model. Moreover, there have been multiple attempts to fashion a model that utilizes different features to predict the blending composition.…”

“…Until now, the industry has relied on empirical linear programming (LP) correlations that are heavily dependent on the engineering experience . However, researchers have designed models to predict the blended fuel properties and blending characteristics ,− and these models recognize the nonlinearity of the fuel blending mechanism. There are five common methods in the literature: Linear method, Ethyl method, Stewart method, Zahed method, and Twu method .…”

Prediction of Gasoline Blend Ignition Characteristics Using Machine Learning Models

“…In the fuel design stage, the simplest approach is to use linear tools. The first studies that included nonlinear interactions were based on graphical methods. , More recently, calculation methods based on the use of blending octane numbers have been proposed, as well as correlations that include deviation terms to account for synergistic and antagonistic effects . Multivariate statistical analysis based on near-infrared (NIR) spectral features has been used to perform online RON prediction of full range gasolines. − …”