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1
Industrial Experienceat Lenzing AG and Petronor
Bernhard Voglauer
(Lenzing AG)
José Luis Pitarch
(UVA)
SPIRE Workshop
Jan. 27th 2016
2
Outline
SPIRE Workshop• Brussels27/01/2016
• Introduction Lenzing AG
• Overview Lenzing Case
• MORE Experiences at Lenzing
• Overview Petronor Case
• MORE Experiences at Petronor
• Summary
3
The Lenzing Group 2014
– Sales: EUR 1,864.2 mn (2013: 1,908.9 mn)
– Export share: 92.3% (2013: 90.8%)
– Fiber sales volumes: 960.000 tons (2013: 890,000 tons)
– Staff: 6.356 (2013: 6,675)
– Listed at Vienna Stock Exchange, Prime Market (ATX)
– Major shareholders:
• B&C Privatstiftung >50%
• Oberbank AG >5%
4
Our core market:Man-made cellulose fibers
– Produced from the raw material wood
• Halfway position between natural and chemical fibers
• Natural wearing properties of natural fibers combined with the advantagesof synthetical fibers such as purity and consistent quality
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Lenzing/Austria2015
297.000 t Dissolving Pulp
337.000 t Cellulose Fibers
TENCEL®, Modal®, Lenzing Viscose®
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Overview Lenzing Case
SPIRE Workshop• Brussels27/01/2016
Case 1.1.: Continuousoptimization of actualenergy consumption
• Optimize operation by the right settings of theavailable manipulated variables!
• Define REIs to be optimised
• DCS based solution preferred
• Fully automated
• Realized savings
300k€/a = 1.000.000 m3 nat gas/a = 2700 t CO2/a
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Overview Lenzing Case
SPIRE Workshop• Brussels03/02/2016
• Definition of REIs
• Cleaning efficiency
• Modelling incl. fouling
• Optimizer Design
• Feasibility Study
• Savings: to be evaluated
Case 1.1.: Continuousoptimization of actualenergy consumption
Case 1.2.: Optimizationof cycle time
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Overview Lenzing Case
SPIRE Workshop• Brussels03/02/2016
• Optimize specific steam consumption in totalevaporator network (30 Evaporators in 8 loops)
• Modelling, process control strategy, Optimizer,Visualisation, PIMS Implementation
• Savings: to be evaluated
Case 1.1.: Continuousoptimization of actualenergy consumption
Case 1.2.: Optimizationof cycle time
Case 2.1.: Continuousoptimization of actualenergy consumption(network)
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Overview Lenzing Case
SPIRE Workshop• Brussels03/02/2016
Combination of all applied to the entire network:
• Which evaporator shall be operated at whichload in which loop best at optimized cycle time?
• Feasibility study
• Savings: to be evaluated
Case 1.1.: Continuousoptimization of actualenergy consumption
Case 1.2.: Optimizationof cycle time
Case 2.1.: Continuousoptimization of actualenergy consumption(network)
Case 2.2.: Optimizationof overall cycle cost ofevaporator network
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What shall be optimized?• Think about influences and impacts, feasibility?
• use REI methodology MORE REI – Guideline!
Examples
MORE Experiences
03/02/2016 SPIRE Workshop• Brussels
NEW!
NEW!
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Availability of information?• Suitable design of visualization MORE Visualization – Guideline!
• on suitable platform IntexcSuite, PIMS
• to the relevant people!
MORE Experiences
27/01/2016 SPIRE Workshop• Brussels
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Make the right decision!• decision support system
• Implementation of model based optimization methods UVA, TUDO
Examples:
static grey box model Evaporator 38: UVA
fouling model: UVA
Optimization of one cycle: UVA
Feasibility study cycle optimization: UVA
static black box model Evaporator: TUDO
Optimization of actual steam consumption in evaporator network: TUDO
MORE Experiences
27/01/2016 SPIRE Workshop• Brussels
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• First principles based stationary mathematical model
27/01/2016 SPIRE Workshop• Brussels
Grey Nonlinear Model (UVa)
Spinbath C-P-T equilibriums.
Heat transfer to ambient.
Psychrometric conditions. Energy and massbalances.
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• Instantaneous RTO:
• Already implemented
Results for line EV.40
27/01/2016 SPIRE Workshop• Brussels
• Cleaning prediction:
SOLUTION: BIG CLEANING
Cleaning in 24 days, 16 h, Update control each 6hSolver time < 1 min (Intel® Core™ i7-4510 CPU)
0,2
0,22
0,24
0,26
0,28
0,3
0 100 200 300 400 500 600 700 800
Time (h)
Measured
Optimized
SOLUTION:Maximum spinbath temperatureMinimum circulating flow
Unitary cost (€/h)
Specific steam consumption
Specific steam consumption
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• Largest site in Spain: production of 11 Mi T/yearBilbao
• 945 fixedemployees
• 6200 inducedjobs
Petronor: Oil refinery
27/01/2016 SPIRE Workshop• Brussels
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Overview Petronor case
27/01/2016 SPIRE Workshop• Brussels
MOTIVATION
Hydrogen H2 is an expensive utility, very important in the refinery global economicbalance.
H2 requirements have experienced a steady increase in recent years:
o heavier fuels are processed;
o more strict environmental regulations;
H2 production capacity is a bottleneck for oil processing capacity.
H2 consumption can experience frequent significant changes (feedstock usuallychanges every 2-3 days)
GOAL: INCREASE EFFICIENCY IN H2
USE IN THE REFINERY
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MORE Experiences
27/01/2016 SPIRE Workshop• Brussels
Definition and computation of REIs
REI 1. Intensity
REI 2. Efficiency
REI 3. Relative Efficiency (compared to best case)
o Reference is obtained by solving an optimization problem with a material balancemodel of the H2 network.
REI 4. Operational Efficiency (secondary REI with operational purposes)
REI 5. Controllability Efficiency (secondary REI with operational purposes)
o Use of REIs for Network on-line Decision Support purposes
ͳ�௦௨ܫܧ �௧�ூ௧௦௧௬
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ͳ�ுଶ�ே௧ܫܧ�ூ௧௦௧௬
=∑ ݎݑ ݎ�ଶܪ� ݑ ௗ௨�௧௦ �ሺ ଷ/ℎ)
∑ ݕ ݎ ݎ ݎ� ݏݏ ௦௨ �௧௦ �ሺ ଷ/ℎ)
͵ܫܧ �ுଶ�ே௧ோா௬
=∑ ܯ�� ݑ � ݑݍ ݎ ݐܯ� ݎ � ݎݑ ݎ�ଶܪ� ݑ �ሺ Ǥሻௗ௨�௧௦
∑ ܣ�� ݑݐ ܯ� ݐ ݎ � ݎݑ ݎ�ଶܪ� ݑ ௗ௨�௧௦
1827/01/2016 SPIRE Workshop• Brussels
Optimal management of H2 network
Producer units: STEAM-REFORMING FURNACESH2 byproduct: CATALYTIC PLATFORMERSConsumer units: HDS, HDT
99.9%
96%
75%
Which plants must produce H2 and their Production rates?
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MORE Experiences
27/01/2016 SPIRE Workshop• Brussels
CHALLENGES: an accurate plant state estimation is difficult.
Great variability for H2 consumption in HDT hydrotreating plants:
Lack of on-line measurements for H2 purity and gas molecular weight MW.
Error prone Orifice-Plate Differential Pressure flowmeter. Drift error due to:
Great influence of variability in light ends composition over total gas MW
o Uncertainty unless both H2 purity and gas MW are measured.
o PRECISE ONLINE COMPUTATION OF REI’s WAS NOT POSSIBLE
o EXPECTED BENEFITS around 2-4% in opportunity for profit inoperation due to frequent changes in scenarios
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Data reconciliation is used to provide consistent estimates: Simplified solubility model to determine the item (MP/LP purges)
Disregard flowmeters with gross errors persistent in time.
Estimate H2 consumption in reactors according to FI/AI measurements.
Real-time optimisation tests to compute REI 3 (outline for the moment)
MORE Experiences
27/01/2016 SPIRE Workshop• Brussels
20,00
30,00
40,00
50,00
60,00
70,00
80,00
90,00
100,00
(%)
REI 1 - Intensity
HD3 - pure H2cons/Hydrocarbon
Min non-pure H2 / Actual non-pure H2
0,90
0,95
1,00
1,05
(%1
)
REI3 - Relative Efficiency
NET - Min non pure H2 prod/Actual non pure H2 prod
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• A What-If analysis regarding REIs is available insimulation
MORE Experiences
27/01/2016 SPIRE Workshop• Brussels03/02/2016
decision variables u
Optimization(SNOPT/IPOPT)
SimulationEcosimPro
Process
J cost funtion
g constrains
Data Adquisition(SCADA + Excel )
Data Treatment(EcosimPro/
Excel)
SimulationEcosimPro
Sequential (EcosimPro) /Simultaneous (GAMS)
REIs for off-lineDecision Support
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• A DMC controller has been commissioned and is in operation– Deals with the main source of inefficiency in the H2 network operation
– Complementary to the RTO approach
– Built upon other MPCs managing the operation in each consumer plant
MORE Experiences
27/01/2016 SPIRE Workshop• Brussels
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Summary
27/01/2016 SPIRE Workshop• Brussels
• SUITABLE RESOURCE EFFICIENCY INDICATORS have been defined.
• SUITABLE CLOSED-LOOP SOLUTIONS have been developed and are alreadyonline and working!
• DECISION SUPPORT SYSTEMS have been designed and are in development.
• SAVINGS ARE ACHIEVED: LENZING: around 1.5 MioNm3/y of natural gas and300 k€/y, PETRONOR: around 1.5% high-purity H2 consumption)
… and more are expected!
• MORE IDEAS and EXCELLENT NETWORK OF PARTNERS responsible forachievements
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End
27/01/2016 SPIRE Workshop• Brussels