MORE efficient evaporation in viscose production · 2017-02-27 · MORE efficient evaporation in viscose production Christian Jasch, Gerhard ... The spinbath recovery circle Na 2

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MORE efficient evaporation in viscose production

Christian Jasch, Gerhard Seyfriedsberger, Bernhard Voglauer and Thomas Röder

Final Consortium Meeting DECHEMA Frankfurt 15th Feb. 2017

The LENZING AG and its contribution to project MORE

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• World market leader for cellulose fibers

– Sales 2015: EUR 1,977 mn

– Fiber sales volumes: 965,000 t

– Staff: 6,127

• Almost 80 years of experience in cellulose fiber production

• Headquartered in Lenzing, Upper Austria

• Global production network with major production sites in Indonesia, China, USA and EU

The Lenzing Group

Final Consortium Meeting • Frankfurt

327/02/2017

Produced from the raw material wood

Fields of application: Textile, fashion, hygiene, medical and technical applications

Our core market:wood-based cellulose fibers


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Lenzing/Austria 2015

297.000 t Dissolving Pulp

339.000 t Cellulose Fibers

World‘s largest fully integrated dissolving wood pulp and viscose

fiber production

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The viscose process

From pulp to fibers


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The spinbath recovery circle

Na2SO4

Spinning

Recovery

Heat Recovery

Bottom tank Degassing

FilterEvaporator

ZnSO4

CS2

H2S

H2SO4 H2O 300t/h

100t/h steam

CalcinationNa2SO4*10H2O

H2O

Fiber Viscose(NaOH+CS2+Cell)

H2SO4 high concentr.

Na2SO4 low concentr.

Crystallizer

H2SO4 low concentr.

Na2SO4 high concentr.


To maintain a certain concentration and to extract the co-product Na2SO4

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The spinbath evaporator

Vapour steam HE

Live steam HE

Evaporation chambers 2

Evaporation chambers 1

Cooling tower

Condenser

live steam

Spinbath inflow

Spinbath outflow

Vapour steam

Vapour steam

Vapour condensate Live steam condensate


Multi stage Evaporators with live steam as main heat source Cooling through cooling tower or surface condenser Main control values: spinbath circulation, cooling temp. and temp. after live

steam HE

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MORE Objectives

MORE objective: Reduce the energy consumption of the evaporation section by 1 %

Case 1: Improvement of a single evaporator operation

Case 2: Improvement of the whole evaporator section operation by optimisation of the evaporator load allocation

Case 3: Optimisation of the evaporator cleaning cycles


Main energy source in the viscose fiberproduction = Heat in the form of steam

Spinbath evaporation process step has thehighest steam consumption

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SSC of a single evaporator depends on:• Capacity (operating point)• Fouling• External conditions (ambient temp.)

SSC visualisation over capacitySSC

[t/

t]

0

~0,4

17 19 21 23 25 27 29 31

Spe

c. s

team

co

nsu

mp

tio

n [

t/ t

]

Evaporation Capacity [t/h]

dotted line : circulation flow change solid line: heating temp. change cooling Temp. Heating Temp.blue = low small = highred = high big = low

Δ=0.01

Case 1: Single evaporator operation

• Energy efficiency of a single evaporator = REI specific steam consumption (SSC)

• SSC Τ𝑡 𝑡 = Τ𝑙𝑖𝑣𝑒 𝑠𝑡𝑒𝑎𝑚 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑒𝑣𝑎𝑝𝑜𝑟𝑎𝑡𝑜𝑟 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦

• REI visualisation


SSC optimum = most efficient operation at:

• High heating temperatures• Low circulation flows• Low cooling temperatures

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0

5

10

15

20

25

30

-15,0 -10,0 -5,0 0,0 5,0 10,0 15,0 20,0

Co

olin

gTe

mp

. [°C

]

Ambient Temp. [°C]

fixpoint

old coolingtemp. limit

new coolingtemp. limit

Bonus:• REI visualisation indicated lower SSC for lower cooling temp.• Lower temperature limit set to prevent icing of the cooling tower• Development of a new cooling temperature control through further tests and

calculations allowing lower cooling water temperatures lower SSC

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MORE results for case 1:

Identified a new SSC operation optimum through REI visualisation

Implementation of a new evaporator performance control in the DCS

2,5 % savings in evaporator steam consumption

Equals 310.000 €/a or 1.0 Mio Nm³ naturals gas/a

Implementation of a new cooling tower controll in the DCS


Equals 65.000 €/a or 0.2 Mio Nm³ natural gas/a



12Final Consortium Meeting • Frankfurt

Case 2: Evaporator load allocation

• The evaporator section = more than 25 evaporators on several spinbath cycles

• Capacity, efficiency, fouling state and connectivity varies for each evaporator

• Before MORE: Operator performs load allocation solely based on his experience

• After MORE: Decision support system (DSS) to indicate the most energy efficient load allocation

EV2EV1 EV4EV3

Cycle 1

Cycle 2

Target-Capacity

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10Cycle 3

Cycle 4

Cycle 5

10

10

10

EV1

EV1

EV2

EV2

EV2 EV3

EV3

EV4

EV4

EV8EV7 EV9 .…

EV7

EV7

EV8

EV8

EV9

EV9

EV9

EV9

EV5

EV6EV5

EV5

EV5

EV6

EV6

EV6

t/h

t/h

t/h

t/h

t/h

….

No load Partial load Full load



EV Modells

• Linear databased models as a function of control variables for every EV

• Least square fit of the model parameters to historical data

MATLAB Optimizer

• Mixed integer non linear program

• Production constrains and fouling state

• PI visualisation of evaporator section

• Displaying actual state and optimisation proposal

PI Dashboard

• Load allocation with lowest steam consumption

DSS




Identified linear databased evaporator models

Fully functional prototype DSS for the load allocation with PI-Dashboard

Prototype testing still going on

Expected impact

1 % savings in evaporator steam consumption


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• Impurities in the spinbath cause fouling of the evaporators

• Fouling effects especially the HE and increases the SSC of the evaporators

• Evaporators receive cleanings to get rid of the fouling

• Before MORE: Cleaning schedule based on availability and supervisor experience

• After MORE: DSS to indicate for every evaporator the most resource efficient

Case 3: Evaporator cleaning cycles


time

Sp

ec. ste

am

co

nsu

mpt.

Cycle time 1

Cycle time 2

Cleaning cycle time

time

Sp

ec. ste

am

co

nsu

mpt.

Cleaning 1

Cleaning 2

Cleaning 3

Cleaning type

time

Sp

ec. ste

am

co

nsu

mpt.

Cleaning sequence

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• SSC depends on operating point Identification of fouling/cleaning effect not possible

• Implementation of a reference run setting in the evaporator DCS

Fixed operating point near max. capacity for 2 h

Performed before, after cleaning and once a week

Identification of SSC increase caused by fouling and cleaning effects



3/31/16 5/20/16 7/9/16 8/28/16 10/17/16

SSC

[t/

t]

Δ=0,005 Cleaning type 1

Cleaning type 2

Avg. SSC increase

= 1,0 kg/(t*d)

cleaning

SSC

[t/

t]

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• The economic viability of a cleaning cycle = REI Normalised average cost per time (NACT)

• NACT Τ€ 𝑑 = Τ(𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑠𝑡𝑠 + 𝑐𝑙𝑒𝑎𝑛𝑖𝑛𝑔 𝑐𝑜𝑠𝑡𝑠) 𝑐𝑦𝑐𝑙𝑒 𝑡𝑖𝑚𝑒

• REI visualisation

0 5 10 15 20 25 30 35 40

No

rmal

ise

d a

vera

ge c

ost

s p

er t

ime

[€/d

]

Cleaning cycle time [d]

OptimumHistorical

Conclusion = Clean more often

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Measurement to identify and display fouling behaviour and cleaning effects

Database of fouling behaviour and cleaning effects for number of

evaporators Data collection going on

Prototype DSS (Excel) for optimal cleaning scheduling

Expected impact:



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Project MORE Resume

Already 3% in energy savings through improved resource efficiency within

MORE

Equals 1,2 Mio Nm³ natural gas/a or 375.000 €/a

Further 1,8 % more energy savings expected in the next year (225.000 €/a)

Because of REIs better understanding of the evaporation process

New tools and methods learned through the cooperation with our

partners


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Thank you for your attention!


Special thanks to:

Documents

MORE efficient evaporation in viscose production · 2017-02-27 · MORE efficient evaporation in viscose production Christian Jasch, Gerhard ... The spinbath recovery circle Na 2