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Batch Distillation Model

Batch Distillation Model. By: Jason Hixson Jennifer Potter Wayne Johnson. dddddd. Objective: To develop a batch distillation model that can accurately adjust a batch process to provide constant distillate composition.

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Batch Distillation Model

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  1. Batch Distillation Model By: Jason Hixson Jennifer Potter Wayne Johnson

  2. dddddd • Objective: To develop a batch distillation model that can accurately adjust a batch process to provide constant distillate composition. • Problem: Batch distillation will have a variable bottoms concentration and minimum reflux ratio, therefore requiring a variable system operating line.

  3. x, y Diagram • Shows Vapor Liquid Equilibrium (VLE) • Van Laar Fit • Determines Theoretical Stages Perry’s Data Van Laar Model

  4. x, y Diagram • xB = Bottoms Composition • xD = Distillate Composition • 3 Theoretical Stages Required xB=0.24 xD=0.85

  5. Effect of Bottoms Composition • Decreasing bottoms composition with time. xD=0.73 xB=0.05

  6. Influence of Operating Lines xB=0.35 xB=0.25 xD=0.77 xB=0.13 xd=0.77

  7. Method • Solve x, y Diagram for Binary Mixture • Determine minimum Reflux Ratio (RD) • Vary RD by: • Setting theoretical stages = Actual Stages • Setting Distillate composition = constant

  8. Model Requirement Input: • Binary Mixture • Heat • Volume • Pressure • Trays • D & B Composition Output: • x, y Diagram • RD vs. BM • xB vs. BM • D vs. BM

  9. User Interface (UI) • 12 previously solved mixtures. • Van Laar Fit available for any binary mixture data “other”.

  10. “Other” Model UI Requirements: • x, y, T Data • Antoine Constants • Log Based • Physical Properties • Specific Volume • Heat of Vaporization

  11. Precautions • Requires Accurate Fit • Azeotrope Complications

  12. Model Output

  13. Model Verification • 12 tray distillation column. • Power=2.7 kW • Reflux varies to keep reflux temperature constant.

  14. Model Results Controller Gain = 1 %/°C

  15. Model Results Controller Gain = 9 %/°C

  16. Conclusions • Model accurately predicts required reflux ratio. • Useful for column design and optimization. • Future work – To vary column reflux ratio based on model output.

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