Centre for Process Integration © 2012

From Energy to Capital and Economic Optimisation

Robin Smith

University of Manchester

• Objective

• Develop a methodology for

optimising the design or operation

of heat-integrated processes in

order to:

- Maximise profit

- Maximise yield of valuable products

- Maintain product quality

1. Energy and Capital Cost Targets

Capital Energy Trade-Offs

The correct setting for ∆Tmin is economic.

T

H

2

∆Tmin2

T

1

H

∆Tmin1

COST

Total

Capital

OPT

1 2

Energy

∆Tmin

We can set energy cost targets

as a function of ∆Tmin.

Energy

Cost

∆Tmin

But what about capital cost ?

CAPITAL COST

Area

No Units

No ShellsMaterials of

Construction

Equipment

Type

Pressure

Rating

intervals

Network Area

Amin = ∑ ∑

We can set overall area targets

1

∆TLM

q

hstreams

T

H

7

6

5

4

3

2

1

.

Number of Units

Nabove = Sabove - 1

PINCH

Nbelow = Sbelow - 1

NMER = (Sbelow - 1) + (Sbelow -1)

S = number of streams including utilities

We can also account for

• No of shells

• Materials of Construction

• Equipment Type

• Pressure Rating

Trading Off Energy and Capital Cost Targets

∆Tmin ∆Tmin

∆Tmin∆Topt

Capital

Cost

Energy

Cost

Cost

Trading off Energy and Capital Cost Targets

• Only applies to new design

• Capital cost unreliable, even in new design� Network structure is necessary for more

reliable cost estimates

BETTER APPROACH FOR CAPITAL COST

• Automated design for new networks

BUT

2. Heat Exchanger Network Retrofit

BUT, what about Retrofit of Heat Recovery Networks?

How can we tackle the retrofit problem more

effectively?

E14 E10E11E13 E12

DESALTER

FURNACE

FLASH

E7 E8 E9

E6

E2 E1E3

E5 E4

CIT

Vac.Res.

Atm.MPA

Atm.2SS

Atrm.TPA

Vac.PA

Vac.PA

Atm.3SS

CrudeOil

E14 E10E11E13 E12

DESALTER

FURNACE

FLASH

E7 E8 E9

E6

E2 E1E3

E5 E4

CIT

Vac.Res.

Atm.MPA

Atm.2SS

Atrm.TPA

Vac.PA

Vac.PA

Atm.3SS

CrudeOil

INFEASIBLE REGION

2 mods

3 mods

Retrofit Approach

ExistingHENAexist

Eexist

0 mods

1 mod

Search for structural changes with best cost-effective designs

A

E

Stochastic Optimization can be used to Optimize Structural Options

Simulated annealing moves

Repipe HX

ResequenceHX

Add newHX

Add/changestream split

AND the continuous variables

• Gives the designer greater control over the complexity of the retrofit design

• Allows a range of retrofit options to be identified

• Because of the increased control allows a more practical approach to retrofit

This Approach to Retrofit

BUT....

• Obtaining cost effective retrofits is still our biggest challenge

• Need to minimize piping modifications and civil engineering

Retrofit of a crude oil distillation preheat train

Case Study

Existing Heat Exchanger Network

Hx no. Area(m2)

1 3262 333

3 10

4 5

5 167

6 150

7 305

8 317

9 16

10 38

11 20

12 15

13 154

14hu 118

15 39

16hu 26

17hu 14

18 23

19hu 5

21hu 722 164

23cu 45

24cu 1,151

25cu 55

26cu 119

27cu 234

28cu 82

Total 3,939

1210864

119753

2

1

M1

1314

13

13

11

11

12

12

M2

15182221

1

1

2

2

7

7

8

8

M4

5

5

6

6

M5

15

15

19

3

3

18

18

25

26

16

4

4

22

22

27

24

23

17

9

9

10

10

M3

28

Optimized Heat Exchanger Network

4

4

4 121086

11753

2

1

1314

13

13

11

11

12

12

M2

15182221

1

1

2

2

7

7

8

8

M4

5

5

6

6

M5

15

15

19

3

3

18

18

25

26

16

22

22

27

24

23

17

9

9

10

10

M3

28

4

4

Additional area

NEW HX

Repipe

4

4

Summary of Case Study

Hot Utility (MW)

Existing Optimised

99.5 96.4

Saving

3.1

Total operating Cost(106 £/yr)

7.46 6.101.36

(18.2 %)

Cold Utility (MW) 83.8 78.8 5.0

Additional Area (m2) - 1022 -

� Problem, too much additional area requiredthroughout the network

• Retrofit design often involves too many modifications to the network equipment and structure.

• Practical implementation of the additional area in an existing network can be difficult due to topology, safety and downtime constraints.

Problems in Retrofit Design

• Heat exchanger networks retrofit usually requires increasing heat exchanger area in various places in the network.

• Can be expensive to add additional area (especially pipework and civil engineering).

Retrofit of HENs

• Need to minimize piping modifications and civil engineering, exploit new heat transfer technology, etc.

Overcoming Retrofit Problems

New Technologies

Tube Inserts(©HiTran)

• Heat transfer enhancement is one way of increasing the heat transfer in heat exchange equipment.

• General reluctance to adopt HTE due to fear of increased fouling (in practice, it generally decreases fouling)

• It can give heat exchangers a higher heat transfer coefficient, and make exchangers smaller, therefore cheaper and can give greater energy efficiency for processes.

Heat Transfer Enhancement (HTE)

Crude Oil Fouling Research [University of Bath]

Foulin

g r

esis

tance [m

2K

/ W

* 1

04]

Time [hr]

Tube with matrix Element velocity 0.5m/sTwall = 218 °C

Time [hr]

Tube without matrix Elementvelocity 0.5m/sTwall = 216 °C

Arabian light crude oil

Advantageous effects:• Lower tube-wall temperature for same duty• Reduction in fluid volume which is heated above bulk temperature• Reduction in wall fluid residence time• Suppression of nucleation at the surface• Increased shear rate at the wall (higher removal rate)

Benefit of Applying HTE in HENsRetrofit

• Implementation of HTE is relatively simple.

• This means reduced civil and pipe work costs.

• The duration of the modifications also decreases, which means the modifications may be implemented during a normal shut down period to avoid production losses.

• Implementation of HTE is generally much cheaper than additional area.

Further Improvements to Performance

How can we increase the performance further?

Simultaneous process and heat recovery optimization

3. Towards Profit Optimisation

Heat-integrated distillation system optimisation

Develop a methodology for optimising the operating conditions of a crude oil distillation system

ANN model to simulate distillation unit

Model to simulate HEN

Heat-integrated distillation system optimisation

Distillation system with current operating conditions and fixed

structure

Distillation system with optimised operating conditions

SIMULATED ANNEALING ALGORITHM

Distillation model

HEN model

Economic model

Distillation system simulation

Penalty functions

Objective function

Product quality specsSystem feasibility

• SA parameters

• Upper and lower bounds of optimisation variablesDistillation system

parameters

Case study

Existing column and HEN- Light crude (Bombay crude)

- Allow product flow rates to change

- Keep product specifications

Case study - Crude oil distillation model

ANN model results – Validation (Bombay crude)

Profit Optimization

Objective function

Net profit = Product value – cost of crude oil feed

– annualized capital cost – operating costs

Constraints• Hydraulic limits of distillation columns• maximum pump-around flow rate increase• Product Specifications• Product boiling curve properties of both atmospheric

column and vacuum column• Product flow rate of vacuum column• Steam flow rates

Constraints

Distillation

•Product T5% and T95% TBP points variation of less than ±10°C from the base case values.

•Calculated column diameters less or equal than existing diameters

HEN

•Minimum temperature approach ≥ 25°C

•Stream energy balance constraint

•No topology modifications

•Calculated area less or equal than installed area

Operational optimisation results

Item Base caseMinimisation of

energy consumption

Maximisation of net profit

Hot utility (MW) 46.6 44.4 44.2

Cold utility (MW) 74.7 73.6 72.5

Hot utility costs (MM$/y) 7.0 6.6 6.6

Cold utility costs (MM$/y) 0.4 0.4 0.4

Utility costs (MM$/y) 7.4 7.0 7.0

Operational optimisation results

Item Base caseMinimisation of

energy consumption

Maximisation of net profit

Product revenue (MM$/y) 2 879.2 2 879.2 2 894.2

Crude oil costs (MM$/y) 2 866.9 2 866.9 2 866.9

Steam cost (MM$/y) 1.7 1.7 1.7

Utility costs (MM$/y) 7.4 7.0 7.0

Total profit (MM$/y) 3.2 3.6 18.5

Summary

• Given a combination of process and HEN model, profit optimisation possible

�Operational optimisation (detailed HEN model)

�Retrofit (detailed HEN model)

�New design (automated design or HEN target)

• Especially effective in retrofit if products arebottlenecked

• Such profit optimization has been appliedindustrially

Acknowledgments

• Lluvia M. Ochoa-Estopier, Megan Jobson (University of Manchester)

• Martin Gough

(CalGavin Ltd)

• Financial support is gratefully acknowledged from the EC FP7 project “Efficient Energy Integrated Solutions for Manufacturing Industries – EFENIS”, Grant Agreement No 282789.

Thank you!