Energy Use in Distillation
Operation: Nonlinear
Economic Effects
IETC 2010
Spring
Meeting
Presenter
Doug White
Principal Consultant
PlantWeb Solutions
Group
Emerson Process
Management
Houston, Texas
2010 IETC Meeting
Distillation Energy Impact

Over 40000 distillation/ fractionation columns in the US
alone

Consume 40% - 60% of the total energy used in
chemical and refining plants

Consume 19% of the total energy used in
manufacturing plants in the US
Reference: Office of Industrial Technology:
Energy Efficiency and Renewable Energy;
US Department of Energy
Washington, DC
“Distillation Column Modeling Tools”
2010 IETC Meeting
Presentation Objectives

Present general approaches to saving energy
in fractionation/ distillation through improved
control

Present techniques for economic analysis that
recognize non-linear character of distillation
operation and effects of product blending
2010 IETC Meeting
Typical Distillation Column
PC
CW
LC
Gas
FC
FC
Feed, F
Reflux,
R
Distillate,
D
TC
FC
LC
Reboiler,
E
Bottoms, B
AC
2010 IETC Meeting
Steam
AR
Traditional Control Benefit Analysis
Specification Limit
Product
Composition
($/ Day
Profit)
Operating Targets
Poor Control
Better Control,
Reduced
Variability
Time
When is this valid? When is it not?
2010 IETC Meeting
Improved Profit
By Changing
Target
Representation of Variability
Gaussian Distribution
Specification
Limit
Mean
Frequency
of
Occurrence
Product
Composition
Time
Composition
2010 IETC Meeting
Effect of Variability – Linear Objective
Function
Expected
Values
Valuation
Function
Product
Value;
$/ Day
Projected
Distribution
Move
Average
Closer
To
Limit To
Increase
Value
Limit
Original
Distribution
Composition
No Benefit For Better Control At Constant Setpoint!
2010 IETC Meeting
Case Study – Debutanizer Column
PC
LC
FC
FC
Feed, F
20,000 BPD
$60/ Bbl
C3 – 25%
nC4 – 25%
nC5 – 25%
nC6 – 25%
Reflux,
R
AR
TC
FC
Steam
15$/MMBTU
LC
Reboiler,
E
Bottoms, B
AC
< 5%C4; $80/ Bbl
> 5%C4; $60/ Bbl
2010 IETC Meeting
Distillate, D
< 3%C5 ;$60/ Bbl
>3%C5; $40/ Bbl
Case Study – Typical Tiered Pricing With
Composition
< 3%C5;
$60/ Bbl
On - Spec
Product
>3%C5;
$40/ Bbl
Off - Spec
Product
< 5%C4;
$80/ Bbl
On - Spec
Product
> 5%C4;
$60/ Bbl
2010 IETC Meeting
Off - Spec
Product
Impact of Material
Balance Variability
Operating Margin – Bottoms Compositional
Change – Constant Reflux – No Control
Variability
15,000
Operating Margin,
$/ Day
10,000
5,000
Top Product
On Spec
Bottom Product
Off Spec
0
-5,000
0.00%
1.00%
2.00%
3.00%
4.00%
Pct C4 in Btms
2010 IETC Meeting
5.00%
6.00%
7.00%
Operating Margin – Control Variability Impact –
Base Case
15,000
Operating Margin,
$/ Day
Initial
Mean
Value 10,000
Spec
5,000
Initial Operating
Target
0
Initial
Variability
-5,000
0.00%
1.00%
2.00%
3.00%
4.00%
Pct C4 in Btms
2010 IETC Meeting
5.00%
6.00%
7.00%
Operating Margin – Improved Control – Reduced
Variability Case
New 15,000
Mean
Value
Increased
Margin
Spec
Operating Margin,
$/ Day
10,000
Improved
Control
Yields Value
At Constant
Setpoint!
5,000
0
-5,000
0.00%
1.00%
2.00%
3.00%
Same Operating
Target
New
Variability
4.00%
Pct C4 in Btms
2010 IETC Meeting
5.00%
6.00%
7.00%
Operating Margin – Optimum Target
Composition Versus Control Performance
10500
Std Dev
Optimum Setpoint
0.0
Operating Margin, $/ Day
10000
0.1
0.2
Optimum
Target For
Composition
Varies with
Control
Performance
and is NOT at
the limit!
9500
9000
8500
0.3
0.4
8000
3
3.5
4
Bottom Composition, %
2010 IETC Meeting
4.5
5
Energy Balance
Control
Distillation – Energy and Margin
Product Value,
$/day
High Energy
Cost, $/day
Low Energy Cost,
$/day
Low Energy
Operating
Margin,
$/ Day
Cost Margin
$/day
High Energy
Cost Margin
$/day
High
Energy
Cost
Optimum
Low
Energy
Cost
Optimum
Min Reflux
High Purity
Specifications
2010 IETC Meeting
Reflux/
Reboiler
Energy Cost versus Reflux Change –
Constant Bottom Composition
25,000
Energy Cost, $/ Day
6
Top Product
Specification
Limit
5
20,000
4
15,000
3
10,000
2
5,000
1
0
0
0.5
0.7
0.9
1.1
1.3
1.5
Reflux/ Feed Ratio
2010 IETC Meeting
1.7
1.9
2.1
Top Product C5+, %
30,000
Operating Margin – Optimum with Varying
Energy Pricing
250,000
Steam Cost, $/ mBTU
Top Product
Specification
Limit
Operating Margin, $/ Day
240,000
5
230,000
15
Optimum
220,000
210,000
Control Target Changes from
Composition To Reflux (Energy)
Depending on Relative Prices
25
200,000
0.5
0.7
0.9
1.1
1.3
1.5
Reflux/ Feed Ratio
2010 IETC Meeting
1.7
1.9
2.1
Non-Linear Objective Functions – Impact of
Variability
For nonlinear relationship, the
expected value of the energy cost is
NOT at the value equivalent to the
median of the composition; It’s
value depends on the standard
deviation of the composition
Energy
Cost
Expected
Value
Probability
Distribution
More Pure
Composition
2010 IETC Meeting
Less Pure
Energy Cost – Effect of Control Variability
27,000
Energy Cost, $/ Day
Initial
Mean
Value
22,000
Reduced
Energy
New
Mean
Value
17,000
Initial
Variability
New
Variability
12,000
1.0
2.0
3.0
4.0
Distillate Composition, C5+, %
2010 IETC Meeting
5.0
6.0
Effect of Blending
Column Product
Shipped Product
Proposition: Since actual specification is on shipped
product rather than column product directly, small
excursions over the specification don’t matter and
can be handled by blending.
Is this correct?
2010 IETC Meeting
Energy Cost – Impact of Control Performance
15,900
15,800
Better Control
Performance
Pays Even
With Blending
Energy, $/ Day
15,700
15,600
15,500
15,400
15,300
15,200
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Distillate Composition Standard Deviation (Constant Mean)
2010 IETC Meeting
0.9
Pressure Effects
Energy Cost – Operating Pressure Impact –
Constant Top and Bottom Product Compositions
Energy Cost, $/ Year
6,000,000
5,000,000
4,000,000
Minimum Pressure
Cooling Water
Minimum Pressure
Air Cooler
3,000,000
80
100
120
140
Condenser Pressure, PSIA
2010 IETC Meeting
160
180
Non – Symmetric
Distributions
High Purity Columns Often Have NonSymmetric Compositional Distributions.
Aromatics Column Data
120
Data
Frequency Results
100
Gumbel
80
Gaussian
60
Gumbel is a two
parameter statistical
distribution which
often fits nonsymmetric data well
40
20
0
0
0.5
1
1.5
Impurity
Composition, %
2010 IETC Meeting
2
Summary – Distillation Economics Conclusions





For practical cases with tiered product pricing the
optimum composition target may not be at the
maximum impurity limit
The optimum energy usage depends on energy
pricing and may be shift from constrained to
unconstrained
Even with product blending there is an incentive for
better control performance
Minimizing pressure continues to have value for
many separations
High purity columns often have non-symmetric
compositional distributions – require special
statistical analysis beyond Gaussian distribution
assumptions
2010 IETC Meeting