Molecular Representation of the Petroleum Gasoline Fraction

Abstract

The computer-aided reconstruction of gasoline composition is an active area of petroleum and petrochemical research as a result of the demand for molecular-level management of the petroleum feed streams. To that end, in this work, a molecular compositional model based on a predefined representative molecular set was built that allows for the conversion of conventional bulk property data to an approximate molecular composition. The selection of representative molecules was based on their presence in gasoline molecular compositional measurement and their potential contribution to the key physical properties. Around 170 hydrocarbons and heteroatom species were chosen as predefined identities of molecules that can exist in a gasoline sample. The physical property data of all of the representative molecules were collected, and suitable mixing rules for the gasoline range stream were applied for the accurate prediction of bulk properties. The approximate concentration of representative molecules was obtained through fitting the predicted bulk property to the measured data. The methodology was verified through intensive tests on various gasoline samples, including straight-run naphtha, catalytic cracking gasoline, coking gasoline, and reformates. The modeling was also accomplished in a sequential order using basic to advanced measurements to find the optimum number of measurements required for detailed composition evaluation on various feedstocks. The propagation of error in the experimental measurement and prediction method on composition has been evaluated.

Molecular Reconstruction of Naphtha via Limited Bulk Properties: Methods and Comparisons

“…Naphtha belongs to the light oil group; it contains up to 400 species. 26 Although naphtha can be represented by these prespecified molecules, it is still hard to identify the contents of all these 400 components. Research works are focused on reducing the number of prespecified molecules.…”

“…The results showed that the prediction of this method is effective. Cui et al 26 proposed a library containing 89 hydrocarbons and 81 heteroatom-containing species in their molecular representation of the petroleum gasoline fraction. They introduced a gamma probability density function instead of the entropy maximization to regulate the content relations between components.…”

“…The former one means all of the bulk properties are satisfied exactly or within a certain error range, as shown in the Shannon entropy maximization method, 31,33,34 while the latter one means all of the bulk properties should be satisfied as much as possible, as performed in the literature, with minimizing the deviations between the calculated and measured average bulk properties as the objective function. 23,26,27,37,40,42,46,47 maximization without constraints, with exact nonlinear constraints, with exact linear constraints that need to be satisfied, with approximate linear constraints for which the deviations need to be minimized, and with constrained normal distributions in which the property needs to follow a Gaussian distribution while the deviation of its average needs to be minimized. In light of the Shannon entropy maximization method, we consider that when setting properties deviation minimization as the objective function, it is not necessary to set all of the bulk properties in the objective function, but the two choices that single bulk property could be either put as constraints to be satisfied or put as deviations to be minimized can be combined, while this is not explored in the literature.…”

Virtual Soft Sensor of the Feedstock Composition of the Catalytic Reforming Unit

“…As can be seen from the graphs, the distribution within each homologous group (paraffins, iso-paraffins, olefins, naphthenes, and aromatics) did not statistically obey any distribution function. This made it impossible to apply known models [9,[47][48][49][50] based on the assumption of a change in composition in accordance with the known statistical distribution within the homologous group. The unevenness in the composition of raw materials and distribution by homologous groups can also be seen.…”

“…(5) High cost of equipment for the study of these processes, etc. However, taking into account these issues, the use of APC algorithms along with technological developments will certainly increase the efficiency of secondary oil refining processes, as well as bring additional profit to oil refineries [9,10]. Although advanced control systems are based upon mathematical models, it is difficult to obtain accurate mathematical models describing a process in petroleum or a related field [11].…”