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

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Presentation on theme: "Batch Distillation Model By: Jason Hixson Jennifer Potter Wayne Johnson."— Presentation transcript:

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 x B = Bottoms Composition x D = Distillate Composition 3 Theoretical Stages Required x B =0.24x D =0.85

5 Effect of Bottoms Composition Decreasing bottoms composition with time. x B =0.05 x D =0.73

6 Influence of Operating Lines x B =0.35x B =0.25x D =0.77x B =0.13 x d =0.77

7 Method Solve x, y Diagram for Binary Mixture Determine minimum Reflux Ratio (R D ) Vary R D 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 R D vs. B M x B vs. B M D vs. B M

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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