Systematic performance evaluation of gasoline molecules based on quantitative structure-property relationship models

Cai, Guangqing; Liu, Zhefu; Zhang, Linzhou; Shi, Quan; Zhao, Suoqi; Chen, Xu

doi:10.1016/j.ces.2020.116077

Cited by 19 publications

(7 citation statements)

References 54 publications

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“…A comparison between experimental and calculated data is displayed in For high-quality gasoline, iso-paraffins are the ideal component for gasoline blending. 53 Figure 4C shows that the iso-paraffins first increase and then decrease. It is because the reaction rate for cracking is relatively slow when the temperature is low.…”

Section: Parameter Regressionmentioning

confidence: 97%

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A molecular kinetic model incorporating catalyst acidity for hydrocarbon catalytic cracking

et al. 2023

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This work built a molecular-level kinetic model for hydrocarbon catalytic cracking, incorporating the catalyst acidity as the parameter to estimate reaction rates. The ndecane and 1-hexene co-conversion catalytic cracking process was chosen as the studying case. The molecular reaction network was automatically generated using a computer-aided algorithm. A modified linear free energy relationship was proposed to estimate the activation energy in a complex reaction system. The kinetic parameters were initially regressed from the experimental data under several reaction conditions. On this basis, the product composition was evaluated for three catalytic cracking catalysts with different Si/Al. The Bronsted acid and Lewis acid as the key catalyst properties were correlated with kinetic parameters. The built model can calculate the product distribution, gasoline composition, and molecular distribution at different reaction conditions for different catalysts. This sensitive study shows that it will facilitate the model-based optimization of catalysts and reaction conditions according to product demands.

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Section: Parameter Regressionmentioning

confidence: 97%

“…Meanwhile, n‐paraffins, which have a negative contribution to the octane number, are also efficiently converted. For high‐quality gasoline, iso‐paraffins are the ideal component for gasoline blending 53 . Figure 4C shows that the iso‐paraffins first increase and then decrease.…”

Section: Molecular‐level Kinetic Modelmentioning

confidence: 99%

A molecular kinetic model incorporating catalyst acidity for hydrocarbon catalytic cracking

et al. 2023

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“…In order to explore the relationship between the molecular properties and structures more intuitively, another quantitative structure–property relationship (QSPR) method using molecular descriptors to predict the molecular properties was proposed . At present, high-precision prediction models for various basic properties, such as the boiling point, density, and flash point, have been developed. − Although the QSPR method has a good fitting effect, it is cumbersome and difficult to associate with the SOL method. In recent years, the method of using functional groups to predict the molecular properties has been increasingly favored by researchers. − The functional groups method fully combines the advantages of the simplicity and directness of the GC method and the accuracy of the QSPR method in the structural description and describes the molecular structure by setting limited functional groups that can be directly described on the molecular structural features.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Physicochemical Properties of Molecules in Petroleum Based on Structural Increments

Hou

Qin

et al. 2023

Ind. Eng. Chem. Res.

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The structure-oriented lumping (SOL) method has been widely applied to molecular-level modeling and optimization in the refining industry. Calculating the physicochemical properties of products obtained by the SOL models can effectively guide the evaluation of product quality and optimization of the process. In this study, the SOL method was improved by extending the structural increments from 22 to 38 to describe the molecules in petroleum more accurately. According to the basic law that the properties and structures of molecules are inseparable, the structural increments with similar physical meanings were combined into a functional group to reduce the dimension to 11 characteristic parameters to describe the molecular structure, and a property prediction method that can be combined with the molecularlevel refining models based on the optimized SOL method was constructed. In addition, the accuracy of the selection and definition of the 11 functional groups was verified by analyzing and discussing the effects of functional groups on the properties, which, in turn, ensured the accuracy of the property prediction models. Compared with other property prediction methods, this method can be combined with the SOL method to provide reliable predictions of the boiling point, critical temperature, critical volume, critical pressure, density, cetane number, motor octane number, and research octane number of molecules.

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“…For example, models have been developed for oil production (Li and Horne, 2003;Irisarri et al, 2016;Hutahaean et al, 2017) and oil transformation (Farrusseng et al, 2003;Wang et al, 2019). These models, however, have not been entirely successful (Cai et al, 2021). Therefore, they have been constantly evolving and have now included artificial intelligence techniques.…”

Section: Introductionmentioning

confidence: 99%

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

Bokhimi

2021

Front. Chem. Eng.

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We describe the use of artificial intelligence techniques in heterogeneous catalysis. This description is intended to give readers some clues for the use of these techniques in their research or industrial processes related to hydrodesulfurization. Since the description corresponds to supervised learning, first of all, we give a brief introduction to this type of learning, emphasizing the variables X and Y that define it. For each description, there is a particular emphasis on highlighting these variables. This emphasis will help define them when one works on a new application. The descriptions that we present relate to the construction of learning machines that infer adsorption energies, surface areas, adsorption isotherms of nanoporous materials, novel catalysts, and the sulfur content after hydrodesulfurization. These learning machines can predict adsorption energies with mean absolute errors of 0.15 eV for a diverse chemical space. They predict more precise surface areas of porous materials than the BET technique and can calculate their isotherms much faster than the Monte Carlo method. These machines can also predict new catalysts by learning from the catalytic behavior of materials generated through atomic substitutions. When the machines learn from the variables associated with a hydrodesulfurization process, they can predict the sulfur content in the final product.

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Systematic performance evaluation of gasoline molecules based on quantitative structure-property relationship models

Cited by 19 publications

References 54 publications

A molecular kinetic model incorporating catalyst acidity for hydrocarbon catalytic cracking

A molecular kinetic model incorporating catalyst acidity for hydrocarbon catalytic cracking

Predicting the Physicochemical Properties of Molecules in Petroleum Based on Structural Increments

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

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