Mathematical Modeling Sulfuric Acid Catalyzed Alkylation of Isobutane with Olefins

“… 15 Ivashkina et al also employed the DFT calculation and developed a mathematical model in a STRATCO reactor to define the influence of feedstock compositions on product distribution and RON of alkylate oil. 16 Besides, Liu et al established a correlation model of the ionic liquid catalyzed alkylation process in STR, and the effects of different operating conditions on the product distribution and RON were associated effectively in this model. 6 These mathematical models demanded plenty of foundational data based on thermodynamic characteristics, reaction kinetics factors, reaction mechanisms, and reactor characteristics, which were difficult to be acquired entirely for complex alkylation processes.…”

Section: Introductionmentioning

confidence: 99%

“…Nurmakanova et al calculated the thermodynamic characteristics and reaction kinetics factors using density functional theory (DFT) to build a mathematical model of isobutane alkylation with olefins catalyzed by H 2 SO 4 in a hollow horizontal cylinder, which was used to predict the product distribution of alkylation caused by the changes in the feedstock compositions . Ivashkina et al also employed the DFT calculation and developed a mathematical model in a STRATCO reactor to define the influence of feedstock compositions on product distribution and RON of alkylate oil . Besides, Liu et al established a correlation model of the ionic liquid catalyzed alkylation process in STR, and the effects of different operating conditions on the product distribution and RON were associated effectively in this model .…”

Section: Introductionmentioning

confidence: 99%

Optimized Artificial Neural Network for Evaluation: C4 Alkylation Process Catalyzed by Concentrated Sulfuric Acid

et al. 2021

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In this work, an artificial neural network was first achieved and optimized for evaluating product distribution and studying the octane number of the sulfuric acid-catalyzed C4 alkylation process in the stirred tank and rotating packed bed. The feedstock compositions, operating conditions, and reactor types were considered as input parameters into the artificial neural network model. Algorithm, transfer function, and framework were investigated to select the optimal artificial neural network model. The optimal artificial neural network model was confirmed as a network topology of 10-20-30-5 with Bayesian Regularization backpropagation and tan-sigmoid transfer function. Research octane number and product distribution were specified as output parameters. The artificial neural network model was examined, and 5.8 × 10 –4 training mean square error, 8.66 × 10 –3 testing mean square error, and ±22% deviation were obtained. The correlation coefficient was 0.9997, and the standard deviation of error was 0.5592. Parameter analysis of the artificial neural network model was employed to investigate the influence of operating conditions on the research octane number and product distribution. It displays a bright prospect for evaluating complex systems with an artificial neural network model in different reactors.

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“…To date, there is no unsteady mathematical model of H 2 SO 4 -catalysed alkylation of isobutane with alkenes based on industrial data [30].…”

Section: Introductionmentioning

confidence: 99%

Nonsteady-state mathematical modelling of H₂SO₄-catalysed alkylation of isobutane with alkenes

Ivashkina

Иванчина

Dolganov

et al. 2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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H2SO4-catalysed isobutane alkylation with alkenes is an important industrial process used to obtain high-octane alkylate. In this process, the concentration of H2SO4 is one of the main parameters. For alkylation, sulphuric acid containing 88%–98% monohydrate is typically used. However, only a H2SO4 concentration of 95%–96% enables alkylate with the maximum octane number to be obtained. Changes in H2SO4 concentration due to decontamination are the main cause of process variations. Therefore, it is necessary to maintain the reactor acid concentration at a constant level by regulating the supply of fresh catalyst and pumping out any spent acid. The main reasons for the decrease in the H2SO4 concentration are accumulation of high-molecular organic compounds and dilution by water. One way to improve and predict unsteady alkylation processes is to develop a mathematical model that considers catalyst deactivation. In the present work, the formation reactions of undesired substances were used in the description of the alkylation process, indicating the sensitivity of the prediction to H2SO4 activity variations. This was used for calculation the optimal technological modes ensuring the maximum selectivity and stability of the chemical–technological system under varying hydrocarbon feedstock compositions.

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Mathematical Modeling Sulfuric Acid Catalyzed Alkylation of Isobutane with Olefins

Cited by 14 publications

References 9 publications

Electric production of high-quality fuels via electron beam irradiation under ambient conditions

Electric production of high-quality fuels via electron beam irradiation under ambient conditions

Optimized Artificial Neural Network for Evaluation: C4 Alkylation Process Catalyzed by Concentrated Sulfuric Acid

Nonsteady-state mathematical modelling of H₂SO₄-catalysed alkylation of isobutane with alkenes

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Mathematical Modeling Sulfuric Acid Catalyzed Alkylation of Isobutane with Olefins

Cited by 14 publications

References 9 publications

Electric production of high-quality fuels via electron beam irradiation under ambient conditions

Electric production of high-quality fuels via electron beam irradiation under ambient conditions

Optimized Artificial Neural Network for Evaluation: C4 Alkylation Process Catalyzed by Concentrated Sulfuric Acid

Nonsteady-state mathematical modelling of H2SO4-catalysed alkylation of isobutane with alkenes

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Nonsteady-state mathematical modelling of H₂SO₄-catalysed alkylation of isobutane with alkenes