Modeling & control of Reactive Distillation

Jianjun Peng

Supervisors: Dr. Edgar Dr. Eldridge

02/19/2001 TWMCC 2

Outline

• Background information

• Research objectives

• Modeling

• Experimental plans

• Conclusions

02/19/2001 TWMCC 3

Reactive Distillation: Example

) :s(azeotrope CB, BABAC

A+B

Conventional process

C

C

A, B

BA

reactor

E

A,B,E A,B,E

A, B

A

C

B

Reactive distillation

Reactivesection

Reactivesection

02/19/2001 TWMCC 4

The Good vs. the Bad

• The good + higher conversion + reduced capital cost, energy

• The bad - more difficult to design & control - poor understanding of the process

02/19/2001 TWMCC 5

AspenPlus Simulations

• AspenPlus Radfrac

• Equilibrium model

• Tert-Amyl Methyl Ether (TAME) system

• Steady state

• Objective: How different is reactive distillation comparing to ordinary distillation?

02/19/2001 TWMCC 6

Reflux Ratio Influence

0.978

0.979

0.98

0.981

0.982

0.983

0.984

0.985

0.986

0.987

0 1 2 3 4 5 6 7 8 9

Reflux Ratio

TAM

E c

om

po

siti

on

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Pressure Influence

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

0 1 2 3 4 5 6 7 8 9

pressure, atm

TAM

E c

ompo

sitio

n

02/19/2001 TWMCC 8

Product Rate Influence

0

0.2

0.4

0.6

0.8

1

0 0.05 0.1 0.15 0.2

product / feed

conversion

TAME composition

02/19/2001 TWMCC 9

Research Objectives

• Dynamic model - for the purpose of control

• Model predictive control - PID may not be adequate

• Controller implementation - pilot plant with Delta V control system

• Experimental validation

02/19/2001 TWMCC 10

Equilibrium Models

• Vapor-liquid equilibrium at each stage(section for packed column)

• Tray efficiency or HETP

• One mass balance for each stage

02/19/2001 TWMCC 11

Rate-based Models

• Mass transfer equations

• Vapor-liquid equilibrium only at interface

• Transport properties - mass transfer coefficients - heat transfer coefficients

• NO tray efficiency or HETP

02/19/2001 TWMCC 12

Rate-based Models(2)

N, E N, E

Vapor Liquid Catalyst

Vk+1

yi,k+1

Vk

yi,k

Lk-1

xi,k-1

Lk

xi,k

QVk

fVi,k

QLk

fLi,k

02/19/2001 TWMCC 13

Equilibrium or Rate-based?

Equilibrium models Rate-based models

- Not rigorous + Rigorous

? Tray efficiency or HETP ? Mass transfer

+ Simple - Complicated

02/19/2001 TWMCC 14

Model Comparison

•Jin-Ho Lee etc. (1998) - individual efficiency hard to predict - rate-based model is preferred

•R. Baur (2000) - smaller window for multiplicity in rate-based model - rate-based model is preferred

•No experimental validation

•No details about mass transfer

•No details about the behavior of reactive distillation

02/19/2001 TWMCC 15

Modeling

• Mass transfer - Maxwell-Stefan equations - Overall mass transfer? - Empirical mass transfer coefficients

• Reaction - heterogeneous or pseudo-homogeneous?

• Dynamics - vapor holdup? - energy holdup?

02/19/2001 TWMCC 16

Model Assumptions

• Overall mass transfer

• Pseudo-homogeneous reaction

• Pseudo-steady state energy balances

• negligible vapor holdup

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Model Solution

• Aspen Custom Modeler (ACM)* custom models* built-in DAE solvers* built-in property models* integrated PID controllers* modeling language

02/19/2001 TWMCC 18

Simulation Plans

• Comparison with equilibrium model

• Comparison with more rigorous rate-based model (Sebastien Lextrait)

• Parameter influence - reflux ratio, boil-up ratio, pressure, feed composition

• Dynamic response - feed, reflux ratio, boil-up ratio

02/19/2001 TWMCC 19

Experimental Plans

• 6 inch reactive distillation pilot plant

• TAME system• Experiments

- steady state - dynamic - controller implementation

Catalytic Packing

StructuredPacking

Reactive DistillationColumn

6 in. diameter34 ft. T-T

Backcracking Reactor

02/19/2001 TWMCC 20

Future Work

• Solving the model with ACM

• Simulations and comparisons

• Experiments: steady state and dynamic

• MPC and NMPC implementation

02/19/2001 TWMCC 21

Concluding Remarks• Reactive distillation is advantageous,

but poorly understood.• A dynamic rate-based model has been

developed.• Future contributions

– Solving the rate-based dynamic model– Controller development using simulations– MPC implementation on Delta V– Experimental validation