Computer Program for Optimizing Compounding of High-Octane Gasoline

Kirgina, Maria; Иванчина, Э. Д.; Dolganov, Igor; Chekantsev, Nikita Vitalievich; Кравцов, А. В.; Fu, Fan

doi:10.1007/s10553-014-0486-4

Cited by 10 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The differences in octane number is 30-35 between C5 olefins and C5 paraffin, 50-70 between C6 olefins and C6 paraffin, 36 and 70-80 between C7 olefins and C7 paraffin. 34,37,38 The saturation of C8-C11 olefins is very low, which has little effect on the octane number of gasoline. It means that the octane number can be retained by maintaining the content of C4-C7 olefins.…”

Section: Reactivity Of Olefins With Different Carbon Numbersmentioning

confidence: 99%

Competition between reactive adsorption desulfurization and olefin hydrogenation over the NiO/ZnO–Al₂O₃–SiO₂ adsorbent

et al. 2022

New J. Chem.

View full text Add to dashboard Cite

show abstract

Section: Reactivity Of Olefins With Different Carbon Numbersmentioning

confidence: 99%

Competition between reactive adsorption desulfurization and olefin hydrogenation over the NiO/ZnO–Al₂O₃–SiO₂ adsorbent

et al. 2022

New J. Chem.

View full text Add to dashboard Cite

show abstract

“…At the Department of Chemical Technology of Fuel and Chemical Cybernetics of Tomsk Polytechnic University the mathematical model for calculation of knock characteristics of gasoline was developed, taking into account the contribution of intermolecular interactions in non-additivity of octane numbers of blending. Based on the developed mathematical model computer modeling system for calculating high-octane gasoline blending process "Compounding" was designed [4][5][6].…”

Section: сOmputer Modeling System "Compounding"mentioning

confidence: 99%

Optimization of High-Octane Gasoline Production

Kirgina

Maylin

Иванчина

et al. 2014

AMR

View full text Add to dashboard Cite

The authors described a new approach to calculation of gasoline blending formulations taking into account the non-additivity of octane numbers and the composition of the raw materials with the use of the modeling system Compounding. Gasoline blending formulations developed for "Achinsk petroleum refinery" (Russia) meeting all environmental and technical standards. Applying the developed modeling system each refinery can choose necessary formulation of gasoline production taking into account composition and available flows. This will allow them to reduce the cost of gasoline and make the products more competitive.

show abstract

“…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. − …”

Section: Introductionmentioning

confidence: 99%

“…5,6 More recently, calculation methods based on the use of blending octane numbers have been proposed, 7 as well as correlations that include deviation terms to account for synergistic and antagonistic effects. 8 Multivariate statistical analysis based on near-infrared (NIR) spectral features has been used to perform online RON prediction of full range gasolines. 9−11 The use of Machine Learning (ML) algorithms has also been demonstrated in previous studies to predict gasoline autoignition behavior both on the individual component and the blend level.…”

mentioning

confidence: 99%

Prediction of Gasoline Blend Ignition Characteristics Using Machine Learning Models

et al. 2021

View full text Add to dashboard Cite

Research Octane Number (RON), among other autoignition related properties, is a primary indicator of the grade of spark-ignition (SI) fuels. However, in many cases, the blending of various gasoline components affects the RON of the final fuel product in a nonlinear way. Currently, the lack of precise predictive models for RON challenges the accurate blending and production of commercial SI fuels. This study compares popular Machine Learning (ML) algorithms and evaluates their potential to develop state-of-the-art models able to predict key SI fuel properties. Typical gasoline composition was simplified and represented by a palette of seven characteristic molecules, including five hydrocarbons and two oxygenated species. Ordinary Least Squares (OLS), Nearest Neighbors (NN), Support Vector Machines (SVM), Decision Trees (DT), and Random Forest (RF) algorithms were trained, cross-validated, and tested using a database containing 243 gasoline-like fuel blends with known RON. Best results were obtained with nonlinear SVM algorithms able to reproduce synergistic and antagonistic molecular interactions. The Mean Absolute Error (MAE) on the test set was equal to 0.9, and the estimator maintained its accuracy when alterations were performed on the training data set. Linear methods performed better using molar compositions while predictions on a volumetric basis required nonlinear algorithms for satisfactory accuracy. Developed models allow one to quantify the nonlinear blending behavior of different hydrocarbons and oxygenates accounting for those effects during fuel blending and production. Moreover, these models contribute to a deeper understanding of the phenomena that will facilitate the introduction of alternative gasoline recipes and components.

show abstract

Computer Program for Optimizing Compounding of High-Octane Gasoline

Cited by 10 publications

References 2 publications

Competition between reactive adsorption desulfurization and olefin hydrogenation over the NiO/ZnO–Al₂O₃–SiO₂ adsorbent

Competition between reactive adsorption desulfurization and olefin hydrogenation over the NiO/ZnO–Al₂O₃–SiO₂ adsorbent

Optimization of High-Octane Gasoline Production

Prediction of Gasoline Blend Ignition Characteristics Using Machine Learning Models

Contact Info

Product

Resources

About

Computer Program for Optimizing Compounding of High-Octane Gasoline

Cited by 10 publications

References 2 publications

Competition between reactive adsorption desulfurization and olefin hydrogenation over the NiO/ZnO–Al2O3–SiO2 adsorbent

Competition between reactive adsorption desulfurization and olefin hydrogenation over the NiO/ZnO–Al2O3–SiO2 adsorbent

Optimization of High-Octane Gasoline Production

Prediction of Gasoline Blend Ignition Characteristics Using Machine Learning Models

Contact Info

Product

Resources

About

Competition between reactive adsorption desulfurization and olefin hydrogenation over the NiO/ZnO–Al₂O₃–SiO₂ adsorbent

Competition between reactive adsorption desulfurization and olefin hydrogenation over the NiO/ZnO–Al₂O₃–SiO₂ adsorbent