Energy Use in Distillation Energy Use in Distillation Operation: Nonlinear Operation: Nonlinear Economic EffectsEconomic Effects

Energy Use in Distillation Energy Use in Distillation Operation: Nonlinear Operation: Nonlinear Economic EffectsEconomic Effects

IETC 2010 SpringMeeting

2010 IETC Meeting

PresenterPresenterPresenterPresenterDoug White

Principal Consultant

PlantWeb Solutions Group

Emerson Process Management

Houston, Texas

2010 IETC Meeting

Distillation Energy ImpactDistillation Energy ImpactDistillation Energy ImpactDistillation 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 EnergyWashington, DC“Distillation Column Modeling Tools”

2010 IETC Meeting

Presentation ObjectivesPresentation ObjectivesPresentation ObjectivesPresentation 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

PC

FC

LC

FC

TC

FC

LC

Feed, F

Bottoms, B

Distillate, D

Reflux,R

Reboiler,E

AC

AR

Steam

GasCW

Typical Distillation ColumnTypical Distillation ColumnTypical Distillation ColumnTypical Distillation Column

2010 IETC Meeting

Traditional Control Benefit AnalysisTraditional Control Benefit AnalysisTraditional Control Benefit AnalysisTraditional Control Benefit Analysis

Improved ProfitBy Changing

Target

Better Control, Reduced

VariabilityPoor Control

ProductComposition

($/ Day Profit)

Specification Limit

Time

Operating Targets

When is this valid? When is it not?

2010 IETC Meeting

Representation of VariabilityRepresentation of VariabilityRepresentation of VariabilityRepresentation of Variability

ProductComposition

Specification Limit

Time

Frequency of

Occurrence

Composition

Mean

Gaussian Distribution

2010 IETC Meeting

Effect of Variability – Linear Objective Effect of Variability – Linear Objective FunctionFunctionEffect of Variability – Linear Objective Effect of Variability – Linear Objective FunctionFunction

LimitProduct Value;$/ Day

Expected Values

Move AverageCloser ToLimit ToIncreaseValue

Composition

OriginalDistribution

ProjectedDistribution

ValuationFunction

No Benefit For Better Control At Constant Setpoint!

2010 IETC Meeting

PC

FC

LC

FC

TC

FC

LC

Feed, F20,000 BPD

$60/ Bbl

Bottoms, B< 5%C4; $80/ Bbl> 5%C4; $60/ Bbl

Distillate, D < 3%C5 ;$60/ Bbl >3%C5; $40/ Bbl

Reflux,R

Reboiler,E

AC

Case Study – Debutanizer ColumnCase Study – Debutanizer ColumnCase Study – Debutanizer ColumnCase Study – Debutanizer Column

AR

C3 – 25%nC4 – 25% nC5 – 25%nC6 – 25%

Steam

15$/MMBTU

2010 IETC Meeting

Case Study – Typical Tiered Pricing With Case Study – Typical Tiered Pricing With CompositionCompositionCase Study – Typical Tiered Pricing With Case Study – Typical Tiered Pricing With CompositionComposition

< 3%C5;

$60/ Bbl

>3%C5; $40/ Bbl

< 5%C4; $80/ Bbl

> 5%C4; $60/ Bbl

On - Spec Product

On - Spec Product

Off - Spec Product

Off - Spec Product

Impact of Material Impact of Material Balance VariabilityBalance VariabilityImpact of Material Impact of Material Balance VariabilityBalance Variability

2010 IETC Meeting

Operating Margin – Bottoms Compositional Operating Margin – Bottoms Compositional Change – Constant Reflux – No Control Change – Constant Reflux – No Control VariabilityVariability

Operating Margin – Bottoms Compositional Operating Margin – Bottoms Compositional Change – Constant Reflux – No Control Change – Constant Reflux – No Control VariabilityVariability

-5 ,000

0

5 ,000

10 ,000

15 ,000

0 .00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00%

P c t C 4 in B tm s

Op

era

tin

g M

arg

in,

$/ D

ay

Top ProductOn Spec

Bottom ProductOff Spec

2010 IETC Meeting

Operating Margin – Control Variability Impact – Operating Margin – Control Variability Impact – Base CaseBase CaseOperating Margin – Control Variability Impact – Operating Margin – Control Variability Impact – Base CaseBase Case

-5 ,000

0

5 ,000

10 ,000

15 ,000

0 .00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00%

P c t C 4 in B tm s

Op

era

tin

g M

arg

in,

$/ D

ay

Spec

Initial Operating Target

Initial Mean Value

Initial Variability

2010 IETC Meeting

Operating Margin – Improved Control – Reduced Operating Margin – Improved Control – Reduced Variability CaseVariability CaseOperating Margin – Improved Control – Reduced Operating Margin – Improved Control – Reduced Variability CaseVariability Case

-5 ,000

0

5 ,000

10 ,000

15 ,000

0 .00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00%

P c t C 4 in B tm s

Op

era

tin

g M

arg

in,

$/ D

ay

Spec

Same Operating Target

NewVariability

NewMean Value

IncreasedMargin

ImprovedControl

Yields ValueAt Constant

Setpoint!

2010 IETC Meeting

Operating Margin – Optimum Target Operating Margin – Optimum Target Composition Versus Control PerformanceComposition Versus Control PerformanceOperating Margin – Optimum Target Operating Margin – Optimum Target Composition Versus Control PerformanceComposition Versus Control Performance

8 0 0 0

8 5 0 0

9 0 0 0

9 5 0 0

1 0 0 0 0

1 0 5 0 0

3 3 .5 4 4 .5 5

B o tto m C o m p o s it io n , %

Op

erat

ing

Mar

gin

, $/

Day

0.0

0.1

0.2

0.3

0.4

Std DevOptimum Setpoint

Optimum Target For

CompositionVaries with

Control Performance and is NOT at

the limit!

Energy Balance Energy Balance ControlControlEnergy Balance Energy Balance ControlControl

2010 IETC Meeting

Low Energy Cost Optimum

Reflux/Reboiler

Operating Margin, $/ Day

Low Energy Cost, $/day

Product Value,$/day

Low Energy Cost Margin $/day

High Energy Cost, $/day

High Energy Cost Margin$/day

High Energy Cost Optimum

Min Reflux High PuritySpecifications

Distillation – Energy and MarginDistillation – Energy and MarginDistillation – Energy and MarginDistillation – Energy and Margin

2010 IETC Meeting

Energy Cost versus Reflux Change – Energy Cost versus Reflux Change – Constant Bottom CompositionConstant Bottom CompositionEnergy Cost versus Reflux Change – Energy Cost versus Reflux Change – Constant Bottom CompositionConstant Bottom Composition

0

5 ,0 0 0

1 0 ,0 0 0

1 5 ,0 0 0

2 0 ,0 0 0

2 5 ,0 0 0

3 0 ,0 0 0

0 .5 0 .7 0 .9 1 .1 1 .3 1 .5 1 .7 1 .9 2 .1

R e flu x / F e e d R a tio

En

erg

y C

ost

, $/ D

ay

0

1

2

3

4

5

6

To

p P

rod

uct

C5+

, %

T o p P r o d u c t S p e c ific a tio nL im it

2010 IETC Meeting

Operating Margin – Optimum with Varying Operating Margin – Optimum with Varying Energy PricingEnergy PricingOperating Margin – Optimum with Varying Operating Margin – Optimum with Varying Energy PricingEnergy Pricing

2 0 0 ,0 0 0

2 1 0 ,0 0 0

2 2 0 ,0 0 0

2 3 0 ,0 0 0

2 4 0 ,0 0 0

2 5 0 ,0 0 0

0 .5 0 .7 0 .9 1 .1 1 .3 1 .5 1 .7 1 .9 2 .1

R e flu x / F e e d R a t io

Op

erat

ing

Mar

gin

, $/

Day

S te a m C o s t, $ / m B T U

5

1 5

2 5

O p tim u m

Top Product Specification

Limit

Control Target Changes from Composition To Reflux (Energy)

Depending on Relative Prices

2010 IETC Meeting

Non-Linear Objective Functions – Impact of Non-Linear Objective Functions – Impact of Variability Variability Non-Linear Objective Functions – Impact of Non-Linear Objective Functions – Impact of Variability 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

Composition Less PureMore Pure

Probability Distribution

ExpectedValue

2010 IETC Meeting

Energy Cost – Effect of Control Variability Energy Cost – Effect of Control Variability Energy Cost – Effect of Control Variability Energy Cost – Effect of Control Variability

1 2 ,0 0 0

1 7 ,0 0 0

2 2 ,0 0 0

2 7 ,0 0 0

1 .0 2 .0 3 .0 4 .0 5 .0 6 .0

D is tilla te C o m p o s itio n , C 5 + , %

En

erg

y C

os

t, $

/ D

ay

NewVariability

NewMean Value

Initial Variability

Initial Mean Value

ReducedEnergy

2010 IETC Meeting

Effect of BlendingEffect of BlendingEffect of BlendingEffect 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 PerformanceEnergy Cost – Impact of Control PerformanceEnergy Cost – Impact of Control PerformanceEnergy Cost – Impact of Control Performance

1 5 ,2 0 0

1 5 ,3 0 0

1 5 ,4 0 0

1 5 ,5 0 0

1 5 ,6 0 0

1 5 ,7 0 0

1 5 ,8 0 0

1 5 ,9 0 0

0 0 .1 0 .2 0 .3 0 .4 0 .5 0 .6 0 .7 0 .8 0 .9

D is tilla te C o m p o s itio n S ta n d a rd D e v ia tio n (C o n s ta n t M e a n )

En

erg

y,

$/

Da

y

Better ControlPerformancePays Even

With Blending

Pressure EffectsPressure EffectsPressure EffectsPressure Effects

2010 IETC Meeting

Energy Cost – Operating Pressure Impact – Energy Cost – Operating Pressure Impact – Constant Top and Bottom Product CompositionsConstant Top and Bottom Product CompositionsEnergy Cost – Operating Pressure Impact – Energy Cost – Operating Pressure Impact – Constant Top and Bottom Product CompositionsConstant Top and Bottom Product Compositions

3 ,0 0 0 ,0 0 0

4 ,0 0 0 ,0 0 0

5 ,0 0 0 ,0 0 0

6 ,0 0 0 ,0 0 0

8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0

C o n d e n s e r P re s s u re , P S IA

En

erg

y C

ost

, $/ Y

ear

M in im u m P r e s s u r eA ir C o o le r

M in im u m P r e s s u r eC o o lin g W a te r

Non – Symmetric Non – Symmetric DistributionsDistributionsNon – Symmetric Non – Symmetric DistributionsDistributions

2010 IETC Meeting

High Purity Columns Often Have Non- High Purity Columns Often Have Non- Symmetric Compositional Distributions. Symmetric Compositional Distributions. Aromatics Column DataAromatics Column Data

High Purity Columns Often Have Non- High Purity Columns Often Have Non- Symmetric Compositional Distributions. Symmetric Compositional Distributions. Aromatics Column DataAromatics Column Data

0

2 0

4 0

6 0

8 0

1 0 0

1 2 0

0 0 .5 1 1 .5 2

Im p u ri ty C o m p o sit io n , %

Fre

qu

ency

Res

ult

s

Data

Gaussian

Gumbel

Gumbel is a twoparameter statistical

distribution which often fits non-

symmetric data well

2010 IETC Meeting

Summary – Distillation Economics - Summary – Distillation Economics - ConclusionsConclusionsSummary – Distillation Economics - Summary – Distillation Economics - ConclusionsConclusions 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