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CHEMCAD helps to reduce product changeover times

Introduction

Being able to produce different products with just one production facility has been an attractive

option for plant operators not just since the focus on raw material change and the transition to

alternative energies has increased. Flexible production is an important part of their

implementation. While the system lifecycles of 20 to 40 years have not changed, the product

lifecycles are shortening. At the same time, the type, quality, price and availability of the resources

required for production in the globalized world are changing with increasing speed. It is therefore

of economic advantage if production can react flexibly to such changes, if necessary even with

modified or new products.

Rigorous computer-assisted simulation of the production processes helps to analyze and evaluate

different raw material product scenarios within a short time. In doing so, thermodynamic and

technical limitations are consistently taken into account. Without the simulation, numerous costly

and time-consuming tests on the production facilities would be required to verify the new

scenarios. During these tests, the facilities can normally not be used for production.

If the different, ideally optimized, individual operating parameters of a production facility are

known for the individual scenarios, losses of production only still occur when changing over from

one product raw material combination to another. This article deals with minimizing the losses of

production caused by the product changeover time with the help of the CHEMCAD process

simulator.

Using a precise example from the oleochemical industry, we will show how optimum stationary

operating points are determined, missing system parameters estimated and trajectories of the

dependent state variables, such as the product concentration, calculated, analyzed and optimized

with CHEMCAD.

Case example oleochemistry

As a processor of natural products and recycled materials, the oleochemical industry traditionally

encounters fluctuating compositions of the source materials for its processes. To create largely

stable feed conditions for downstream processes, a distillation system can be connected upstream

in which the heavy fluctuations in the composition of the oil from renewable feedstock can be

reduced to a defined measure.

Such a distillation system is the object of this case study. Table 1 shows the composition of different

oils from renewable feedstock. We can see that even the pure oils from renewable feedstock offer

a broad spectrum of chemical compositions. When using oil mixtures and when using recycled oils,

additional combination possibilities arise.

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Table 1: Composition of different oils from renewable raw materials

Unsaturated fatty acids Monounsaturated Polyunsaturated

Trivial

name

Caprylic

acid

Capric

acid

Lauric

acid

Myristic

acid

Palmitic

acid

Stearic

acid

Arachidic

acid

Oleic

acid

Linoleic

acid

Linolenic

acid

CAS

number

124-07-

2

334-

48-5

134-

07-7 544-63-8 57-10-3 57-11-4 506-30-9 112-80-1 60-33-3 463-40-1

CHEMCAD

ID 540 545 890 902 912 550 1534 549 548 1529

Oil type C8:0 C10:0 C12:0 C14:0 C16:0 C18:0 C20:0 C18:1 C18:2 C18:3

Sweet

almond oil 7.0% 2.0% 69.0% 17.0%

Coconut oil 8.3% 6.0% 46.7% 18.3% 9.2% 2.9% 6.9% 1.7%

Coconut

butter 25.0% 38.0% 32.0% 3.0%

Olive oil 11.0% 3.6% 75.3% 9.5% 0.6%

Palm oil 0.1% 0.1% 0.9% 1.3% 43.9% 4.9% 39.0% 9.5% 0.3%

Safflower

oil 0.3% 5.5% 1.8% 0.2% 79.4% 12.9%

Still, the spectrum of the individual fatty acids can be adequately narrowed with two distillation

columns switched in series. In this process, the undesired low boilers are separated in the first

distillation, and the undesired high boilers in the second distillation.

Figure 1 shows the flowchart of such a dual-stage distillation system.

Figure 1: Flow chart of a dual-stage distillation system for feed oil conditioning

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We are looking at two different compositions of oils from renewable feedstock, for each of which a

product oil spectrum is to be achieved through distillation. The heavy oil from renewable feedstock

and the limitations for the heavy oil from renewable feedstock are summarized in table 2; the

corresponding data for the light oil from renewable feedstock is contained in table 3.

Table 2: Feed composition and product specification of the first raw material product scenario (heavy oil)

FEED PRODUCT

Quantity 10 m³/h Bottom limit Top limit

C8 Traces - 0.1%

C10 Traces - 0.1%

C12 1.6% - 0.5%

C14 0.9% - 0.5%

C16 10.3% - 60.0%

C18 75.7% 96.0% -

C20 11.5% - 30.0%

Values in weight percent

Table 3: Feed composition and product specification of the second raw material product scenario (light oil)

FEED PRODUCT

Quantity 10 m³/h Bottom limit Top limit

C8 5.0% - 0.1%

C10 10.0% - 2.0%

C12 40.0% 50.0% -

C14 20.0% 15.0% 28.0%

C16 13.0% 6.0% 14.0%

C18 12.0% 4.0% 14.0%

C20 Traces - 0.1%

Values in weight percent

Optimum operating states

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If we fix the column pressures, two free variables remain per column. In this example, the reflux

ratio and the reboiler duty have been used as design variables. Technical limitations for these

variables result, amongst other things, from the available heat exchanger surfaces in the condenser

and in the reboiler. In this case example, we can assume the normal case, meaning that the

capacity of the heat exchangers compared to the load capacity of the column represents the higher

limitation, so that only the limitations of the reboiler duty and of the cooling capacity in the

condenser must be considered for the optimization. These limits and side conditions are compiled

in table 4. For further system characteristics, please refer to table 5.

Table 4: Definition of the optimizer's scenario

Limits and side conditions

Design variable Bottom limit Top limit

Reflux ratio

column 1 (R/D 1) 0.1 20

Reboiler duty

column 1 (QR 1) 0.3 MW 3 MW

Reflux ratio

column 2 (R/D 2) 0.01 20

Reboiler duty

column 2 (QR 2) 50 kW 500 kW

Side condition

Cooling capacity QC1 in the

condenser of column 1 ≤ 3 MW

Cooling capacity QC2 in the

condenser of column 2 ≤ 500 kW

The operating point at which the product flow is at maximum is designated as the optimal

operating state here. The utility costs are therefore neglected vs. the feed costs.

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Table 5: Characteristics of the dual-stage distillation system in figure 1.

Characteristic Column 1 Column 2

Unit ID 4 5

Feed ID 3 5

Top component ID 4 7

Bottom product ID 5 6

Pressure 35 mbar (a) 10 mbar (a)

Number of stages 18 6

Feed tray 12 6

Column model Rigorous (SCDS)

Tray model Equilibrium (EQ)

EQ-thermodynamics UNIFAC

H-thermodynamics Latent heat

The "Process Optimizer" implemented in CHEMCAD is used for optimization. It is able to take up to

120 independent variables and 120 side conditions into account. Besides the sequential SQP

algorithm, CHEMCAD also provides a simultaneous SQP algorithm and a minimization in line with

the "Reduced Gradient" method. Here, simultaneous means that the flow sheet is solved

simultaneously (meaning equation-oriented) and not iteratively. As the flow sheet in question does

not contain any recycling flows, the sequential and the simultaneous SQP algorithm return the

same result. The optimal scenarios introduced here have been developed with the sequential SQP

approach.

The results of the optimization calculations are displayed in figures 2 and 3. In the case of the heavy

oil from renewable feedstock, 38% of the feed mass resp. 48% of the C18 fatty acid is included in

the product; in the case of the light oil from renewable feedstock, 48% resp. 63% of the C12 fatty

acid. The optimum operating parameters are listed in table 6.

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Figure 2: Change of the substance flow composition in the course of the process for the optimized scenario "heavy oil from

renewable feedstock"

Figure 3: Change of the substance flow composition in the course of the process for the optimized scenario "light oil from

renewable feedstock"

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Feed SumpfKolonne 1

KopfKolonne 2

Mas

s fl

ow

in k

g/h

C20 C18 C16 C14 C12 C10 C8

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Feed SumpfKolonne 1

KopfKolonne 2

Mas

s fl

ow

in k

g/h

C20 C18 C16 C14 C12 C10 C8

Bottom Column 1

Top Column 2

Bottom Column 1

Top Column 2

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Table 6: Values of the design variables and status of the side conditions at the respective optima of the operating scenarios.

Scenario

Design variable

Heavy oil from

renewable

feedstock

Light oil from

renewable

feedstock

Reflux ratio

column 1 (R/D 1) 10.8 2.6

Reboiler duty

column 1 (QR 1) 3 MW 2.03 MW

Reflux ratio

column 2 (R/D 2) 0.65 0.01

Reboiler duty

column 2 (QR 2) 286 kW 364 kW

Side condition

Cooling capacity QC1 in

the

condenser of column 1

1.6 MW 0.99 MW

Cooling capacity QC2 in

the

condenser of column 2

500 kW 500 kW

Higher product yields could be achieved in both scenarios if the cooling capacity of the second

column's head condenser were not limited. Therefore, an optimization calculation provides

additional precise indications of the bottlenecks, meaning the system modifications which may

contribute to improving production. In this case, it is to increase the maximum cooling capacity in

the condenser of the 2nd column, for example by lowering the flow temperature in the cooling

water or by adding an additional heat exchanger.

Raw material product changeover

In order to simulate the changeover from the light to the heavy product, the (mass and energy)

storage terms must be considered. The volume of the pipelines is less significant than the volumes

of the individual trays and the heat exchangers (head condenser and reboiler). With this

assumption, the previously considered stationary flow sheet can be converted without changes to a

dynamic flow sheet. However, to correctly reflect the storage behaviour of the columns, additional

assumptions and details are required. For the head condenser and the column bottom with

reboiler, constant volumes can be assumed through stable fill level control. The diameter of the

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column can be calculated with the Sizing Tool integrated in CHEMCAD based on a flooding point

calculation. To calculate the variable liquid fill level on the individual trays, additional geometric

data is required. The CHEMCAD Sizing Tool once again helps to determine these. The geometric

parameters of relevance for the dynamic simulation are compiled for both columns in table 7.

Table 7: Geometric parameters for the dynamic simulation

Characteristic Column 1 Column 2

Unit ID 4 5

Diameter 3.96 m 2.44 m

Bottom distance 0.61 m 0.61 m

Liquid volume in the

condenser/reflux tank 1.0 m³ 0.5 m³

Liquid volume in the

condenser/bottom 2.0 m³ 1.0 m³

Width of bottom drain 0.22 m 0.22 m

Dam height 0.05 m 0.05 m

The easiest conservative strategy for a production changeover is to wait for the stationary state

with the new feed, and then switching over the operating parameters to the optimum parameters

of the new raw material product scenario. The feed changeover starts after 6 minutes and lasts 10

minutes. Figure 4 shows the change in the composition of the feed flow during the changeover. The

temporal progression of the product flow's composition in line with the simple conservative

strategy is illustrated in figure 5. The system is stationary after approximately 250 minutes.

The changeover of the operating parameters is performed with ramps over a period of 30 minutes,

so as not to induce sudden changes of the parameters and to allow the operator to interfere. With

this strategy, the product specification of the light oil is achieved after 318 minutes.

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Figure 4: Temporal progression of the feed flow's composition when changing the oil from renewable feedstock.

Figure 5: Temporal progression of the product flow's composition using a simple conservative strategy.

If the ramps are allowed to already start at the time of the beginning feed changeover, the time

until the product specification is reached is reduced to 213 minutes. Such a strategy is not

uncommon for a planned product changeover. The corresponding progressions of the mass

fractions of the individual components of the product flow are illustrated in figure 6.

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

0 2 4 6 8 10 12 14 16 18 20

Mas

s fr

acti

on

in f

eed

str

eam

Time in minutes

C8 C10 C12 C14 C16 C18 C20

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

0 50 100 150 200 250 300 350 400

Mas

s fr

acti

on

in p

rod

uct

str

eam

Time in minutes

C8 C10 C12 C14 C16 C18 C20

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Figure 6: Temporal progression of the product flow's composition using a simple strategy.

Dynamic optimization

The CHEMCAD "Process Optimizer" can also be used to optimize dynamic processes. In this

example, the time until the product specification is achieved is to be minimized. The target values

of the ramps are used as criteria. Accordingly, the operating parameters are to be changed only

once in accordance with the strategies described above.

But even with this restriction, the product changeover time can be reduced by more than half, to 93

minutes. Once the product specification is reached, the system switches to the optimum operating

parameters.

The progression of the operating parameters is illustrated in figure 7, and the progression of the

product flow's composition is illustrated in figure 8.

Monitoring of the observance of the product specification can be easily visualized with the

CHEMCAD – Excel interface. Figure 9 shows which specification has been reached when, and how

many constraints are breached in total for the optimized progression of the design variables.

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

0 50 100 150 200 250 300

Mas

s fr

acti

on

in p

rod

uct

str

eam

Time in minutes

C8 C10 C12 C14 C16 C18 C20

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Figure 7: Temporal progression of the design variable after minimizing the product changeover time.

Figure 8: Temporal progression of the product flow's composition after minimizing the product changeover time.

Figure 9: Breach of the product specification; 0: Concentration within specification 1: Concentration outside of specification; all:

Sum across all components.

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100 120 140 160 180 200

Val

ue

of

des

ign

var

iab

le

Time in minutes

R/D 1

QR 1 [MW]

R/D 2

QR 2 x 10 [MW]

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

0 20 40 60 80 100 120 140 160 180 200

Mas

s fr

acti

on

in p

rod

uct

str

eam

Time in minutes

C8 C10 C12 C14 C16 C18 C20

0

1

2

3

4

0 20 40 60 80 100 120 140 160 180 200

Act

ive

con

stra

ints

Time in minutes

C8 C10 C12 C14

C16 C18 C20 Alle

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As we can see in figure 9, the product specification is not reached for 3 minutes when changing

over to the optimum operating parameters. In this period, the concentration of the C18 fatty acid

undercuts the bottom limit of 4%. To prevent such effects, the optimization problem can be

formulated differently, for example. Whether such short-time constraint breaches are of relevance

and have to be considered must be decided for each case.

Also, the determined scenario of the minimum product changeover time is not a global optimum.

Calculation of the gradients of the target function (= product changeover time) with respect to the

design variables is done numerically with a difference quotient. Selection of the increment when

forming the difference quotient therefore has a significant influence on the local minimum

determined with the SQP method. However, it is often not decisive to determine the

mathematically correct minimum of the target function. For operation, the optimization calculation

already pays off if the product changeover time is reduced.

Global optimum and Process Simulation Cup

If the optimization is performed with additional points of the design variables, the product

changeover time can be further reduced, but the optimization problem becomes more complex.

Ramps can also be omitted altogether with the OTS mode (Operator Training System) and instead,

the control valves for the reflux (R/D 1 and R/D 2) and the steam feed (QR 1 and QR 2) temporally

freely adjusted.

To what extent can the product changeover time be reduced even further, and how large is the

potential, if more than one jump of the design variables is permitted? These questions are

answered in the Process Simulation Cup 2015. The objective is to find the global minimum of the

product changeover time for the stated process. At http://www.process-simulation-cup.com/

students can submit their solution suggestions for the jumps of the design variables, and will

immediately receive the product changeover time calculated with these.

Successful implementation in practice

The product changeover time and the potential for its reduction vary for each system and each raw

material product scenario. It is also necessary to weigh up how detailed the dynamic process model

has to be, for example with reference to geometric data. In addition, the simulation results should

be compared to real system data to validate the process model. In practice, the timescales

determined through dynamic optimization are generally not simply transferred. Much rather,

changeover criteria such as the temperature values of certain bottoms are used by CC-DYNAMICS

for implementation. Optimum operating schemes for the system operators are generated based on

these new changeover criteria.

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Infraserv GmbH & Co. Knapsack KG in cooperation with Chemstations offers the corresponding

services for the described implementation. For example, the product changeover times for 12 raw

material product scenarios were minimized for a customer in the oleochemical industry, and the

raw material and energy input reduced thanks to the additionally gained production time.

Conclusion

CHEMCAD provides all tools for stationary and for dynamic simulation and optimization of

processes in one package. Scenarios can be calculated quickly and easily, and improvement

suggestions developed through complete integration of the tools for process optimization (Process

Optimizer), for apparatus dimensioning (Sizing Tool), and for dynamic simulation (CC-DYNAMICS).

System owners and operators can obtain support and advice in all phases from Infraserv GmbH &

Co. Knapsack KG, which has already helped numerous customers to significantly reduce the

operating costs in production.

Are you interested in further publications, tutorials, seminars or other solutions with CHEMCAD? Then please contact us: Mail: support@chemstations.eu Phone: +49 (0)30 20 200 600 www.chemstations.eu Authors:

Jan Schöneberger

Moritz Wendt