Analysis of the particle swarm algorithm in the optimization of a three-phase slurry catalytic reactor

► PSO was employed to optimize the productivity of a three-phase catalytic slurry reactor. ► The reactor was represented by a multivariable non-linear rigorous mathematical model. ► Four sets of PSO parameters values suggested by the literature were evaluated. ► PSO was efficient and robust to solve...

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Published inComputers & chemical engineering Vol. 35; no. 12; pp. 2741 - 2749
Main Authors Mariano, Adriano Pinto, Costa, Caliane Bastos Borba, de Toledo, Eduardo Coselli Vasco, Melo, Delba Nisi Cosme, Filho, Rubens Maciel
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier Ltd 14.12.2011
Elsevier
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ISSN0098-1354
1873-4375
DOI10.1016/j.compchemeng.2011.06.001

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Abstract ► PSO was employed to optimize the productivity of a three-phase catalytic slurry reactor. ► The reactor was represented by a multivariable non-linear rigorous mathematical model. ► Four sets of PSO parameters values suggested by the literature were evaluated. ► PSO was efficient and robust to solve the constrained optimization problem. ► Optimization effectiveness was independent of the values of the PSO parameters. The Particle Swarm Optimization (PSO) method was employed to optimize an industrial chemical process characterized by being difficult to be optimized by conventional deterministic methods. The chemical process is a three phase catalytic slurry reactor (tubular geometry) in which the reaction of the hydrogenation of o-cresol producing 2-methyl-cyclohexanol is carried out. The optimization problem was formulated considering as input variables the operating conditions of the reactor and as objective function the maximization of productivity, subject to the environmental constraint of conversion. The process was represented by a multivariable non-linear rigorous mathematical model and in order to solve the optimization problem, the performance of the PSO algorithm was evaluated considering four sets of parameters values suggested by the literature. PSO demonstrated to be efficient and robust to solve the constrained optimization problem, independently of the values of the PSO parameters. The solution of the rigorous mathematical model of the reactor was associated with a high computational burden, and although the PSO algorithm presented high rate of convergence, the attempt to make possible the optimization in a timeframe suitable to real time applications failed because the algorithm lost robustness (fraction of the number of runs the algorithm reached the optimization goal) when run with a reduced number of function evaluations. Therefore, if this type of application is desired, simplified mathematical models with fast and simple numerical methods must be preferred.
AbstractList The Particle Swarm Optimization (PSO) method was employed to optimize an industrial chemical process characterized by being difficult to be optimized by conventional deterministic methods. The chemical process is a three phase catalytic slurry reactor (tubular geometry) in which the reaction of the hydrogenation of o-cresol producing 2-methyl-cyclohexanol is carried out. The optimization problem was formulated considering as input variables the operating conditions of the reactor and as objective function the maximization of productivity, subject to the environmental constraint of conversion. The process was represented by a multivariable non-linear rigorous mathematical model and in order to solve the optimization problem, the performance of the PSO algorithm was evaluated considering four sets of parameters values suggested by the literature. PSO demonstrated to be efficient and robust to solve the constrained optimization problem, independently of the values of the PSO parameters. The solution of the rigorous mathematical model of the reactor was associated with a high computational burden, and although the PSO algorithm presented high rate of convergence, the attempt to make possible the optimization in a timeframe suitable to real time applications failed because the algorithm lost robustness (fraction of the number of runs the algorithm reached the optimization goal) when run with a reduced number of function evaluations. Therefore, if this type of application is desired, simplified mathematical models with fast and simple numerical methods must be preferred.
► PSO was employed to optimize the productivity of a three-phase catalytic slurry reactor. ► The reactor was represented by a multivariable non-linear rigorous mathematical model. ► Four sets of PSO parameters values suggested by the literature were evaluated. ► PSO was efficient and robust to solve the constrained optimization problem. ► Optimization effectiveness was independent of the values of the PSO parameters. The Particle Swarm Optimization (PSO) method was employed to optimize an industrial chemical process characterized by being difficult to be optimized by conventional deterministic methods. The chemical process is a three phase catalytic slurry reactor (tubular geometry) in which the reaction of the hydrogenation of o-cresol producing 2-methyl-cyclohexanol is carried out. The optimization problem was formulated considering as input variables the operating conditions of the reactor and as objective function the maximization of productivity, subject to the environmental constraint of conversion. The process was represented by a multivariable non-linear rigorous mathematical model and in order to solve the optimization problem, the performance of the PSO algorithm was evaluated considering four sets of parameters values suggested by the literature. PSO demonstrated to be efficient and robust to solve the constrained optimization problem, independently of the values of the PSO parameters. The solution of the rigorous mathematical model of the reactor was associated with a high computational burden, and although the PSO algorithm presented high rate of convergence, the attempt to make possible the optimization in a timeframe suitable to real time applications failed because the algorithm lost robustness (fraction of the number of runs the algorithm reached the optimization goal) when run with a reduced number of function evaluations. Therefore, if this type of application is desired, simplified mathematical models with fast and simple numerical methods must be preferred.
Author Melo, Delba Nisi Cosme
de Toledo, Eduardo Coselli Vasco
Filho, Rubens Maciel
Mariano, Adriano Pinto
Costa, Caliane Bastos Borba
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Issue 12
Keywords PSO
Multiphase reactors
Optimization
Variable conditions
Fourier transformation
Evolutionary algorithm
Hydrogenation
Modeling
Goal programming
Function evaluation
Chemical reactor
Function maximization
Convergence rate
Swarm intelligence
Robustness
Deterministic approach
Productivity
Sensitivity analysis
Tubular reactor
Catalytic reactor
Particle suspension
Real time
Conversion
Particle swarm optimization
Real time system
Operating conditions
Constrained optimization
Non linear model
Objective function
Non linear effect
Multivariable system
Slurries
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Snippet ► PSO was employed to optimize the productivity of a three-phase catalytic slurry reactor. ► The reactor was represented by a multivariable non-linear rigorous...
The Particle Swarm Optimization (PSO) method was employed to optimize an industrial chemical process characterized by being difficult to be optimized by...
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SubjectTerms Algorithms
Applied sciences
Catalysis
Catalysts
Chemical engineering
Exact sciences and technology
Experimental design
Inventory control, production control. Distribution
Mathematical analysis
Mathematical models
Mathematical programming
Mathematics
Multiphase reactors
Operational research and scientific management
Operational research. Management science
Optimization
Probability and statistics
PSO
Reactors
Sciences and techniques of general use
Statistics
Title Analysis of the particle swarm algorithm in the optimization of a three-phase slurry catalytic reactor
URI https://dx.doi.org/10.1016/j.compchemeng.2011.06.001
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Volume 35
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