Population-distributed stochastic optimization for distillation processes: Implementation and distribution strategy

[Display omitted] •Population-distributed stochastic optimization for distillation processes.•Reduce the computing time of stochastic optimization by more than 70%.•A framework calls multiple simulators to evaluate the population in stochastic optimization.•Analyze the reason for the loss of paralle...

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Veröffentlicht in:Chemical engineering research & design Jg. 168; S. 357 - 368
Hauptverfasser: Lyu, Hao, Cui, Chengtian, Zhang, Xiaodong, Sun, Jinsheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Rugby Elsevier Ltd 01.04.2021
Elsevier Science Ltd
Schlagworte:
Benzene
Computing time
Distillation
Distillation process optimization
Distilled water
Efficiency
Evolutionary computation
Heat pumps
Heat transfer
Message passing
Optimization
Parallel processing
Parallel/distributed algorithm
Pool model
Population-distributed stochastic optimization
Pumps
Robustness (mathematics)
Simulators
Stochastic models
Studies
Toluene
Vacuum distillation
Xylene
Parallel/distributed algorithm
Pool model
Population-distributed stochastic optimization
Distillation process optimization
ISSN:0263-8762, 1744-3563
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Zusammenfassung:[Display omitted] •Population-distributed stochastic optimization for distillation processes.•Reduce the computing time of stochastic optimization by more than 70%.•A framework calls multiple simulators to evaluate the population in stochastic optimization.•Analyze the reason for the loss of parallel efficiency. Stochastic optimization is inefficient, although it shows robustness against local optimum and can guarantee high-quality solutions. Parallel computation can be a promising way to improve the efficiency of stochastic optimization. However, the common environments do not support calling multiple simulators through the win32com interface, which hinders parallel computation. As a countermeasure, this study proposes a population-distributed differential evolution (DDE) framework, which combines multiple optimizers through the shared message passing medium. The framework distributes the population into groups (subpopulations) on different threads by a pool model, which can make full use of a multi-core CPU and significantly accelerate the computation. Moreover, we considered both the synchronously and asynchronously distributed differential evolution. Three case studies (benzene/toluene/xylene conventional distillation, acetone/methanol/water extractive distillation, and heat pump assisted dividing-wall column separating benzene/toluene/xylene) are optimized to show the superior performance of the DDEs. The parallel framework can reduce the computing time by ∼70% on a 4-core CPU, which is a significant improvement. DDEs cause some parallel efficiency loss, which is 5–10% and 10–20% for ADDE and SDDE, respectively. Further, based on time consumption analysis, we explain the reasons for the efficiency loss.
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ISSN:0263-8762
1744-3563
DOI:10.1016/j.cherd.2021.02.023