Index

1. UPC and my Thesis work presentation

2. Complex distillation columns with energy savings

3. The work

3.1 Design

3.2 Dynamic aspects

3.3 Control

4. Conclusions and future work

Universitat Politècnica de Catalunya (UPC).

•Founded in 1971, it has:–9 schools and faculties (Industrial Engineering)–8 technical colleges–7 associate schools–38 departments (Chemical Engineering)–21 diplomas, 8 degrees: 30.000 students last year–44 Ph.D. programs: 149 thesis during 1996-1997–budget 1998: 260,00 M$can

The Chemical Engineering Department

• 90 teachers and researchers

• 95 Ph.D. students

• Main goals:– chemical process optimisation, security and

accident modelisation, reactors, water technology, fluid-particle systems, alimentary technology, waste treatment, contaminants analysis, environmental studies, molecular engineering, polymer synthesis and structure.

The thesis work• Title: Energy optimisation in complex

distillation columns

• Objective: study complex designs for energy savings already described to bring them closer to implementation– design, operation and control

• Status:– Petlyuk Column: centre of my studies till now– some design, some control, some operation– 60% of work done

The Petlyuk Column origin

• Wright (1949) proposed a promising design alternative for separating ternary mixtures

• Petlyuk (1965) studied the scheme theoretically

• Most important literature since Petlyuk: Fidkowski and Krolikowski / Glinos and Malone / Triantafyllou and Smith / Kaibel / Wolf and Skogestad

The Petlyuk Column structure

A B C

A + B

A

B

C

B + C

P R E F R A C T IO N A T O R

M A I N C O L U M N

Conventional designs

1

17

2

4

6

8

10

12

14

16

T1

1

20

2345678910111213141516171819

T2

S1

S4

S5

S3

S2

1

17

2

4

6

8

10

12

14

16

T1

1

20

2345678910111213141516171819

T2

S1

S4

S5

S3

S2

INDIRECT TRAIN DIRECT TRAIN

Distillation process in a Petlyuk Column

First column direct train

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

tray number

mol

ar fr

actio

n X-benzene

X-toluene

X-oxylene

Y-benzene

Y-toluene

Y-oxilene

Petlyuk feed column

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

1 2 3 4 5 6 7 8 9 10

tray number

mol

ar fr

actio

n

X-benzene

X-toluene

X-oxylene

Y-benzene

Y-toluene

Y-oxilene

Petlyuk Column features• No more than one component is stripped out

in each section, key components A and C:– reversibility during mixing of streams in feed

location (pinch zone)– no remixing effect

• Thermal coupling– no thermodynamic losses in heat exchanges of

prefractionator reboiler and condenser– reversibility during mixing of streams at ends of

columns

Reported 30% of energy savings

The Divided Wall ColumnThermodynamical equivalence in only one shell

A B C

A

B

C

V A P O RS P L IT

L I Q U I DS P L IT

Extension to other multicomponent distillations

A B C D

A

B

C

D

Distinguishing features

• n(n-1) sections required for an n-component separation

• Only one condenser and one reboiler

• Key components in each column are not two adjacent ones, but the ones with extreme volatility

Design of the Petlyuk Column

• Degrees of freedom– design: number of trays per section and feed

trays– operation: flowrates or flowrate ratios. Two

extra DOF used to optimise the process

• Main design decision: separation to be carried out by the prefractionator.– Two levels of specification:

• two specified variables

• three specified variables

Work presented at AIChE Meeting, Los Angeles, 1997

Short-cut methods facing multicomponent systems

Most of numerical correlations used by short-

cut methods solve distillation columns based on required recoveries of

just key components

Ability to play only with two

recoveries

Importance of all three prefractionator recoveries over the global economic performance of a complex distillation column

Proposed design heuristic method

• Decision of A and C recoveries. Design following short-cut indications (simplified model). Rigorous simulations.

• Change of feed tray to minimise the larger vapour flow between flows at COL2 bottom and COL3 top

• Repeat till vapour flows are equal

• Change recoveries of A and C

Balance between prefractionator and main column and between upper and down main column

Simplified model of the Petlyuk ColumnWork presented at Congreso Mediterraneo de Ingenieria Quimica, 1996

A B C

A

B

B

C

P R E F R A C T IO N A T O R

COL2

COL3

A + B

B + C

Determination of mixtures that take major profit of the Petlyuk Column

• Case study with pro-II simulations: – Studied separations:

• different quantities of B in feed (+33%, 33%, -33%)

• different Easy Separation Index (<1, 1, >1)

– Savings compared to the best train of columns:• more B in feed, more savings (23%, 20 %, 14%)

• more savings when ESI is close to 1 (34%)

Dynamic behaviour • SPEEDUP model

• Neural Network simulation

• MATLAB model– linearised model: transfer functions

• Model approximations

– constant relative volatility throughout the column, equimolar overflow, no heat losses equilibrium in each plate, constant pressure, liquid and vapour flow dynamics, tray hydraulics...

Dynamic features• Interaction

• Speed, magnitude and shape of response: stiff

sim2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

2.4

4.9

7.4

9.8

12

.3

14

.8

17

.3

19

.8

22

.3

24

.8

27

.3

29

.8

32

.3

temps

co

mp

os

icio

ns

"MIDDLE_PROD_ "X_OUT1(1)"-

"MIDDLE_PROD_ "X_OUT1(2)"-

"MIDDLE_PROD_ "X_OUT1(3)"-

"MIX_LIQ_FEED. "X_IN1(1) "-

"MIX_LIQ_FEED. "X_IN1(2) "-

"MIX_LIQ_FEED. "X_IN1(3) "-

"REBOIL. "X_OUT(1) "-

"REBOIL. "X_OUT(2) "-

"REBOIL. "X_OUT(3) "-

"SPLIT_TANK. "X_OUT1(1) "-

"SPLIT_TANK. "X_OUT1(2) "-

"SPLIT_TANK. "X_OUT1(3) "-

Neural Network simulation - MPC?

• The used NN– three layer– feedforward with autoregressive neurones

connected to the output

• Sampling frequency from lowest time constant of all outputs: C in feed to B in sidestream, 6 min

• Training of the NN– PRBS signal applied to all inputs (until 3

manipulated variables and 3 disturbances)

Work presented at III Congresso de Redes Neuronais, 1997

NN forecasting example

8.00E-018.20E-018.40E-018.60E-018.80E-019.00E-019.20E-019.40E-019.60E-019.80E-011.00E+00

1 74 147

220

293

366

439

512

585

658

731

804

877

time intervals of 0.1 hour

botto

m p

rodu

ct p

urity

Netw ork output:past/future

SPEEDUP data

9.20E-01

9.25E-01

9.30E-01

9.35E-01

9.40E-01

9.45E-01

9.50E-01

9.55E-01

9.60E-01

863 868 873 878 883 888 893 898 903

time intervals of 0.1 hour

bo

tto

m p

rod

uct

pu

rity

Netw ork output:past/future

SPEEDUP data

902 patterns

20000 epochs

3, 6, 1 neurons

Sigm., linear

shift param. = 1

autoregressive param. = 1

9.15E-01

9.20E-01

9.25E-01

9.30E-01

9.35E-01

9.40E-01

9.45E-01

1 2 3 4 5 6 7 8 9 10 11 12 13 14

time intervals of 0.1 hour

bo

tto

m p

rod

uct

pu

rity

fo

reca

st

0.315

0.32

0.325

0.33

0.335

0.34

0.345

0.35

0.355

inp

ut

pro

file

fo

r fo

reca

stin

g

Neural netw ork forecast

SPEEDUP data

Molar fraction of A in feed

Molar fraction of B in feed

Molar fraction of C in feed

Control problem• Control product compositions

– 3 composition specifications (holes in some operation regions)

– inventory control

• Control to minimise energy consumption

• Robustness?

• Linearity far from nominal steady state?

• Disturbances rejection and set point changes achievement?

Descentralised control

• Skogestad: acceptable control seems feasible (no energy control, linear model)

• Study of descentralised control with MATLAB models

Tyreus method:– Design and test inventory control

• 7 control valves - 5 steady state DOF = 2 inventory loops

– Design composition control– Design optimisation control (energy minimisation)

Work presented at CHISA ’98

Diagonal control for the Petlyuk Column

Control of A, B, and C purity:

• For each inventory control (D-B, L-B, D-B)– Transfer function– MRI, CN, Intersivity Index

• For the decided control structure: D,B; L, S, V– Chose one pairing

• For the decided pairing: L-A, S-B, V-C– BLT tuning procedure:

• controller gains: 0.74, -2.33, 0.65

• controller reset times: 14.16 for all loops

(L-A, S-B, V-C) Controlled system MATLAB simulation

0 500 1000 1500 2000 2500 3000 3500 40000.965

0.97

0.975

0.98

0.985

0.99

0.995

Set point change in A purity example

No instability problem was found, better tunning can be achieved

MIMO feedback control

• Controllability analysis in frequency domain– bandwidth– RGA, CN, singular values– stability (Nyquist plots)– poles and zeros

• MIMO robustness

Self-optimising control

• Published works from NTNU

• Problem: once the minimum is located, control is required to keep the operating point at the minimum when disturbances are loaded

• Solution: Improve robustness with feedback control to careful selected outputs

• Require: measurable output variable which when kept constant keeps minimum energy consumption (self-optimising control)

Work to be presented at PRES, 1999

Studied controlled variables for indirect energy minimisation

•For each candidate, sensitivity to disturbances in feed composition and liquid fraction is computed:

–heavy key fraction in vapour leaving top of prefractionator

–middle component recovery in prefractionator–main column flow balance–Temperature profile symmetry–others

•The best?

Conclusions

• A design method

• Mixture characterisation for Petlyuk Column

• Dynamic features

• NN are able to simulate the Petlyuk Column

• Diagonal control works in our simplified model

• Self-optimising control fits the Petlyuk Column

Future work

• Better characterisation of mixtures fitting different complex distillation columns

• Other designs to compare with. Energy integration

• Robustness for different nominal steady-states

• HYSYS dynamic rigorous simulations

• Design and control together

• NN simulation into Model Predictive Control