Optimization of a Fluid Catalytic Cracking Kinetic Model by Improved Particle Swarm Optimization

Sun, Shiyuan; Yan, Haike; Meng, Fandong

doi:10.1002/ceat.201800500

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…A program written in FORTRAN was employed to calculate the kinetic parameters. The program used an algorithm based on the fourth‐order Runge‐Kutta method to solve the ordinary differential equation 22, 23. This program is combined with a two‐swarm cooperative particle swarms optimization 24, 25, also written in FORTRAN, for calculating the kinetic parameters of best fit.…”

Section: Resultsmentioning

confidence: 99%

Lumped Kinetic Modeling Method for Fluid Catalytic Cracking

Zhao

Sun

2020

Chem Eng & Technol

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A new fluid catalytic cracking (FCC) lumped kinetic modeling method was put forward to improve the prediction accuracy of the FCC kinetic model. This model divided the feedstock into three lumps and the products into six lumps. The parameters of the kinetic model were obtained by combining the fourth-order Runge-Kutta integral method and the two-swarm cooperative particle swarm optimization algorithm. Compared with dividing the feedstock according to the group composition and the distillation range, the average relative error between the experimental data and the calculated values proves the higher fitting accuracy of the new lumped method. The kinetic model established by the method exhibits better adaptability to different feedstock and fewer requirements for feedstock analysis data.

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Section: Resultsmentioning

confidence: 99%

Lumped Kinetic Modeling Method for Fluid Catalytic Cracking

Zhao

Sun

2020

Chem Eng & Technol

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“…Many other tools can be integrated into the PSO code for the improvement of the identification quality, depending on the application. ,,,, …”

Section: Methodsmentioning

confidence: 99%

“…It consists of exploiting the swarm intelligence that relies on the independent evolution of particles and their interactions in a biological-type system. Although this algorithm is also well-adapted to the optimization of large problems, it has been rarely used for the optimization of kinetic models. − Ding et al proved the better optimization performances of PSO compared to GA when applied for the three-component parallel reaction mechanism of biomass pyrolysis. However, they used only one set of parameters (further referred to as the optimization “strategy”), which limits the comparison.…”

Section: Introductionmentioning

confidence: 99%

On the Potential of the Particle Swarm Algorithm for the Optimization of Detailed Kinetic Mechanisms. Comparison with the Genetic Algorithm

Rassy¹,

Delaroque

Sambou

et al. 2021

J. Phys. Chem. A

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This work investigates the potential of the particle swarm algorithm for the optimization of detailed kinetic mechanisms. To that end, empirical analysis has been conducted to evaluate the efficiency of this algorithm in comparison with the genetic algorithm. Both algorithms are built on evolutionary processes according to which a randomly defined population will evolve, over the iterations, towards an optimal solution. The genetic algorithm is driven by crossover and mutation operators and by a selection method. The PSO approach is based on the experience of each individual and on the group experience to control the direction of its evolution. The success of the application of an algorithm can be sensitive to the choice of operators and the relative importance attributed to them. Therefore, to make the comparison as rigorous as possible, about a dozen strategies were proposed for each algorithm and the performances were evaluated. A degraded version of the GRI-Mech 3.0 mechanism (i.e. containing some of the kinetic constants randomly modified) was generated and then optimized by the two evolutionary algorithms to recover the predictive character of the original mechanism. The results show that, for the majority of the proposed strategies, PSO is more efficient than the GA, whereas the latter is generally much more used for the optimization of detailed kinetic mechanisms.

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“…However, this approach fails to consider how group characteristics of the feedstock as well as the hydrocarbon reactivity influence the yield of coke and products, which is important when predicting the composition of products considering the catalyst deactivation. Although some models consider the hydrocarbon type content in the feedstock, they may not predict the hydrocarbons groups of the gasoline, which requires forecasting the research octane number (RON) [20,21].…”

Section: Introductionmentioning

confidence: 99%

A Model of Catalytic Cracking: Product Distribution and Catalyst Deactivation Depending on Saturates, Aromatics and Resins Content in Feed

et al. 2021

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The problems of catalyst deactivation and optimization of the mixed feedstock become more relevant when the residues are involved as a catalytic cracking feedstock. Through numerical and experimental studies of catalytic cracking, we optimized the composition of the mixed feedstock in order to minimize the catalyst deactivation by coke. A pure vacuum gasoil increases the yields of the wet gas and the gasoline (56.1 and 24.9 wt%). An increase in the ratio of residues up to 50% reduces the gasoline yield due to the catalyst deactivation by 19.9%. However, this provides a rise in the RON of gasoline and the light gasoil yield by 1.9 units and 1.7 wt% Moreover, the ratio of residue may be less than 50%, since the conversion is limited by the regenerator coke burning ability.

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Optimization of a Fluid Catalytic Cracking Kinetic Model by Improved Particle Swarm Optimization

Cited by 6 publications

References 31 publications

Lumped Kinetic Modeling Method for Fluid Catalytic Cracking

Lumped Kinetic Modeling Method for Fluid Catalytic Cracking

On the Potential of the Particle Swarm Algorithm for the Optimization of Detailed Kinetic Mechanisms. Comparison with the Genetic Algorithm

A Model of Catalytic Cracking: Product Distribution and Catalyst Deactivation Depending on Saturates, Aromatics and Resins Content in Feed

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