Plantwide Controller Tuning Using a Multiobjective Genetic Algorithm

Phimister, James R.; Fraga, Eric S.; Seider, Warren D.

doi:10.1016/s1474-6670(17)44955-x

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…The least fit members are most likely to be removed from the population. In this paper, the rankselective GA is the same as the multiobjective, rankselective GA employed by Phimister and co-workers, 32 but only one objective function is involved herein. Genome Representation.…”

Section: Appendix B: Genetic Algorithmmentioning

confidence: 99%

Semicontinuous, Pressure-Swing Distillation

Phimister

Seider

1999

Ind. Eng. Chem. Res.

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The merits of semicontinuous, pressure-swing, azeotropic distillation are examined. In continuous operation, two columns at different pressures are fed streams having compositions on opposite sides of the azeotropic compositions. The distillates, which approach the azeotropic compositions at high and low pressure, are cycled between the columns. In contrast, semicontinuous operation involves only a single distillation column, which has lower investment costs and, when the mixture to be separated is changed, shorter downtimes. An optimal-control algorithm is employed to determine desirable campaigns, and to schedule pressure switch-over policies. Simulation results for the dehydration of tetrahydrofuran, involving a pressure-sensitive azeotrope, indicate that switch-over between steady states occurs quickly with on-spec product removed during 93% of the campaign. The column achieves production rates near 89% of the maximum throughput of a single column in the continuous process and shows superior performance when compared to reverse-batch operation.

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Section: Appendix B: Genetic Algorithmmentioning

confidence: 99%

Semicontinuous, Pressure-Swing Distillation

Phimister

Seider

1999

Ind. Eng. Chem. Res.

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“…The genetic algorithm (GA) is a method of minimizing or maximizing functions using the principles of biological evolution and has been used for optimizations as diverse as kinetic model fitting [45][46][47] and chemical plant design. 48,49 Methods Generation of "Gold Standard" Datasets. Under different experimental, physiological, or pathological conditions, the sticking probabilities between aggregates of platelets and neutrophils may differ.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we employ the genetic algorithm (GA) to solve the “inverse problem” of deconvoluting the functional form of the sticking probability from timeseries measurements of component size distributions of platelet−neutrophil aggregates. The genetic algorithm (GA) is a method of minimizing or maximizing functions using the principles of biological evolution and has been used for optimizations as diverse as kinetic model fitting − and chemical plant design. , …”

Section: Introductionmentioning

confidence: 99%

Regression of Multicomponent Sticking Probabilities Using a Genetic Algorithm

Laurenzi

Bartels

Diamond

2006

Ind. Eng. Chem. Res.

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A genetic algorithm (GA) was developed for the purpose of regressing composition-dependent aggregation kernels from time series of experimentally measured component or size distributions. The GA evolves initially random populations of kernel models in accordance with the principles of microevolution. To test the robustness of the GA, functionally diverse kernelsincluding one describing the shear-mediated aggregation of blood cellswere constructed. The stochastic time evolution of their corresponding aggregation processes were then simulated under physiological conditions via Monte Carlo. The GA successfully regressed the kernels underlying these “gold standard” datasetswhere we employ the term in the sense of a “trusted reference”from these simulation results, reproducing the multicomponent kernels to a maximum relative deviation of less than 9% over their entire composition ranges. Finally, ramifications of these cases pertinent to experimental design are considered, including the effects of extreme initial population ratios for multicomponent aggregation experiments with extreme population ratios.

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Plantwide Controller Tuning Using a Multiobjective Genetic Algorithm

Cited by 2 publications

References 4 publications

Semicontinuous, Pressure-Swing Distillation

Semicontinuous, Pressure-Swing Distillation

Regression of Multicomponent Sticking Probabilities Using a Genetic Algorithm

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