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.
The demand for improved gasoline product quality has helped make molecular-level models become more preferred for the modern refinery. Building the molecular compositional model is an essential first step for this quantitative molecular management of gasoline streams. Gas chromatography equipped with flame ion detection (GC-FID) is commonly used in the gasoline detailed hydrocarbon analysis (DHA). The combination of GC-FID analysis and molecular-level modeling is thus very attractive. In the present study, we developed a gasoline compositional model based solely on GC-FID as input. To suppress the negative influence of peak coelution, we developed a statistics-based peak tuning algorithm to obtain individual compound resolution at higher carbon number range. Using the tuned result as input, the molecular-level gasoline compositional model was built by estimating the quantitative percentages of the species in a predefined molecular library (573 molecules). The molecular-level compositional model has good extensibility and can link to the molecule-based physical properties prediction model. The model has been verified via applications on various gasoline samples. The prediction of research octane number for large-scale gasoline samples was also revealed.
Effect of non-ideal mixing on heat transfer phenomena is studied in an anchor agitated vessel processed with viscous Newtonian and non-Newtonian fluids. Influence of critical variables such as rotational speed and properties of the fluid on heat transfer coefficient and heat transfer area has been investigated. Based on the flow pattern generated by an anchor agitator, a multi parameter model for quantifying the extent of non-ideality is developed and the parameters of the model, fraction of well mixed zone and the exchange flow rate are evaluated on the basis of tracer response data. Heat transfer experiments are also conducted under unsteady state conditions using same agitated vessel under similar operating conditions using Castor oil, Castor oil methyl esters (CME) and carboxy methyl cellulose (CMC 0.5 %, 1 %), soap solution as process fluids. Based on the results obtained from this analysis, a commercial scale reactor of a capacity of 20 Kl for saponification of hydrogenated castor oil has been designed using different scaleup rules. Power per unit volume found to give desirable results as it gives acceptable values for heat transfer coefficient and power consumption. Equal power per unit volume gives good mixing and high heat transfer coefficient with slightly higher power consumption and the error involved in heat transfer area calculation is small giving optimum cost of the experimental unit.
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