This document discusses applying heat integration and sequencing techniques to optimize small-scale distillation systems for energy efficiency. It analyzes two distillation sequences - direct and indirect - for separating a phenol derivative mixture. The indirect sequence is found to have lower total annualized cost due to greater heat recovery potential, reducing energy consumption by 13% compared to the direct sequence. Heat integration leads to 5% lower capital costs and 35-40% less energy usage for both sequences.

1. APPLICATIONOFHEAT INTEGRATIONAND
SEQUENCINGINTHE DESIGNOFENERGY
EFFICIENTSMALLSCALEDISTILLATIONSYSTEMS
Manish Sharma
Senior Engineer-Process

2. Distillation Systems
• Separation of multi-component mixture into useful products
• Sequential or simultaneous separation
• Energy intensive : 70-80% of overall process energy
requirement
• Operating conditions have to be carefully selected to obtain
desired product purity and recovery
• High capital cost
• Typical Small scale distillation systems have low feed flow rate
and not more than 3-4 main products
• Number of columns may be arranged in different
arrangements to separate useful products. These
arrangements are called Distillation Sequences.

3. Distillation Sequencing
• Structural alternatives for the separation of multicomponent mixtures
• Simple sequence
• Direct
• Indirect
• Complex sequence
• Thermally coupled columns
• Divided wall columns
• Exploring alternative designs can save on capital and operating cost.

4. Distillation Sequencing - simple sequence
Feasibility?

5. Heat integration
Composite
Curves
Grand
Composite
Curves
Heat Exchanger Network
Holistic way to
analyze the
heat recovery
potential and
utility targets
for a process.
Process
modifications

6. Column Targeting (CGCC)
Column grand
composite curve

7. Distillation Column Design
Columns are designed
to meet the product
requirement and
optimized using column
targeting tool in Aspen
Plus Radfrac Model.
Aspen plus provides
with shortcut and
rigorous distillation
column models

8. Optimal Heat Integrated
Distillation System
Design - Methodology
Each distillation sequence is
designed using this algorithm and
compared based on TAC (Total
Annualized Cost).

9. Costing
• Capital costs of equipment are calculated using in-house libraries in Aspen
Capital Cost Estimator
• Operating costs are calculated using the following parameters
• Total Annualized Cost of the process is calculated as
TAC =
CAPEX
yr
+ OPEX
With payback period of 3 yrs.
Fixed operating cost
Maintenance 5% of fixed capital
Operating Labour 8% of total operating cost
Supervision 20 % of operating labour
Plant Overheads 50% of operating labour
Variable Operating Cost
Hot oil 3.50e-06 $/kJ
Cooling water 2.12e-7 $/kJ
Power 2.77e-05 $/kJ

10. Case study
• Phenol derivatives feed to be separated into 4 products
• There are 5 possible simple distillation sequences for the system
• Process constraints : temperature range limited between 50 – 180 oC.
Feed conditions Composition Mass
Fraction
Product
Recovery
Product
Purity
Flowrate
(kg/hr)
1250 A 0.28 99.5 99.8
B (Phenol) 0.22 99.9 99.8
Pressure
(torr)
900 C 0.01 99 98.8
D 0.48 98.5 98.6
Temperature (oC) 32 E 0.01 - -

11. Process design
Direct sequence
Indirect sequence
Out of 5 sequences, three are ruled out
due to infeasibility.

12. Process modification – direct sequence
Blocks C1 C2 C3 C4 Heater Energy target
kcal/hr
Number of stages 120 60 80 40 - 910538
Pressure initial [torr] 120 120 120 5 760
Maximum heat recovery
Pinch
Maximum heat recovery
Initial composite curves Final composite curves
Blocks C1 C2 C3 C4 Heater Energy target
kcal/hr
Number of stages 120 60 80 40 - 700414
Pressure final [torr] 110 110 220 2 760

13. Column targeting
0
5
10
15
20
25
30
35
40
0 100000 200000 300000 400000
Stage
Energy deficit (Kcal/hr)
Actual profile
Ideal Profile
0
5
10
15
20
25
30
35
40
0 20000 40000 60000
Stage
Energy deficit (Kcal/hr)
Actual profile
Ideal profile
• C3 column in the previous flow sheet is the most energy consuming unit
• Initial CGCC shows scope of reflux modification and feed preheat
• After multiple process modifications and reflex tuning, column energy
consumption is considerably reduced
• Similar steps are performed for all the columns in both direct and indirect
sequences.
CGCC - Initial CGCC -Final

14. Heat Exchanger Network Design
• Heat exchanger network is designed in Aspen Energy Analyzer for
maximum energy recovery
• 35% less energy consumption than the non-integrated scenario for
direct sequence
The network design is
then validated with
simulation results and
required
modifications are
made in exchanger
area, temperatures
and heat duty.

15. Sequence comparison
• Targeting and design are done for both the sequence
• a. Direct Sequence
• b. Indirect Sequence
• Heat recovery potential is found to be more in Indirect sequence than
direct sequence.
• Indirect sequence has 13% less energy consumption than direct
sequence.
Maximum heat recovery
ba

16. Results - Cost and TAC
• Heat integration leads to 35%and 40% reduction in energy
consumption for direct and indirect sequence respectively.
• Heat integration not only saved on the utility but capital cost as well
for both the schemes.
• A 5% reduction in CAPEX for both sequences are observed upon heat
integration.
• For the given case study, Indirect sequence is found to be better with
3% less TAC than direct sequence.
Scenario CAPEX
(k$)
OPEX
(k$)
TAC
(k$)
No integration 1604 256 791
With integration 1597 205 740
Scenario CAPEX
(k$)
OPEX
(k$)
TAC
(k$)
No integration 1596 237 769
With integration 1588 186 718
Direct Sequence Indirect Sequence