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A machine learning-fueled modelfluid for flowsheet optimization
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DOI：10.1016/j.compchemeng.2025.109486.png)
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Abstract
En 中文
• Develops a machine learning-fueled modelfluid for distillation-based flowsheet optimization. • Selects modelfluid features from vapor–liquid equilibrium. • Integrates the modelfluid into a gradient-based continuous process fluid optimization workflow. • Demonstrates application on the example of entrainer selection. • Method accurately ranks real entrainers for azeotropic distillation.
Keywords:
Process fluid optimization
Fluid modeling
Machine learning
Property prediction methods
Process optimization
Entrainer distillation
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