Innovative tools,
methods and indicators
for optimizing
the resource efficiency
in process industry
“This project has received funding from the European Union’s Seventh Programme for research, technological
development and demonstration under grant agreement No 604140”
Methodology for modelling in industry to couple energy and material flows
The CoSMo Company, CIRCE, TU Dortmund
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and Optimization
Brussels 27th January 2016Speaker : Guilhem Raffray
2
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
1. Development of Key Resources
Indicators (KRI).
2. Definition of Key Performance
Attributes (KPA) for each sector
addressing Quality, Safety, Costs.
3. Evaluation of the resource efficiency
potential of the global process by
performing audits and diagnosis over
the sub-processes and equipment.
4. Modelling and simulation of the
identified highest resource-efficiency
improvement points to establish the
Critical Process Parameters (CPP),
(residence time, Tº of the raw
materials, cooling water flow rate..)
and understand their relation with
the KPA. This step will allow the
identification of the variables for the
monitoring (VM) and control (VC)
of the CPP.
METHODOLOGY
3
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
1. Development of Key Resources
Indicators (KRI).
2. Definition of Key Performance
Attributes (KPA) for each sector
addressing Quality, Safety, Costs.
3. Evaluation of the resource efficiency
potential of the global process by
performing audits and diagnosis over
the sub-processes and equipment.
4. Modelling and simulation of the
identified highest resource-efficiency
improvement points to establish the
Critical Process Parameters (CPP),
(residence time, Tº of the raw
materials, cooling water flow rate..)
and understand their relation with
the KPA. This step will allow the
identification of the variables for the
monitoring (VM) and control (VC)
of the CPP.
METHODOLOGY
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Overview of the
methodology
4
Methodology6
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
TUDO TECNALIA, COSMO,
CIRCE
Process
ModelsMeta-models
Sensitivity
AnalysisOptimization
Optimized
CPPs
TUDO, CIRCE,
COSMO
COSMO, (TUDO)
CPPs
Build process and utility models
Couple the models
Lists degrees of freedom (operating parameters)
Lists the KPAs
Build surrogate models
Explore the design space with sensitivity analysis
Optimize the process regarding the KRIs using the CPPs
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Detailed Plant models
Modelling of the plant in different scenarios
7
Methodology8
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Objectives:
Comprehensive characterization of the systems
Multiple operation scenarios
TUDO TECNALIA, COSMO,
CIRCE
Process
ModelsMeta-models
Sensitivity
AnalysisOptimization
Optimized
CPPs
TUDO, CIRCE,
COSMO
COSMO, (TUDO)
CPPs
Petrogal`s Refinery10
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Consists of 4 subsystems → 2 are considered in detail
Process plant – Crude distillation and fractionation section
Utility system
Crude Distillation&
Fractionation(CD)
Crude
Utility SystemElectricity
Fluidized Catalytic Cracking
(FCC)
Hydrocracking(HC)
LPG (C3+C4)
Imported VGO
UCO
Natural Gas
Natural Gas Fuel Gas
Electricity
Fuel Gas
Compressed Air
Steam
Propylene
Hydrogen
HydrogenNetwork
AtmosphericResidue
Propane
Butane
Jet Fuel
Naphtha Comp.
Gasoline Comp.
Gasoil Comp.
Fueloil Comp
CC-E60B
CC-E60A
Train B
Train A
Crude
CC-E1B
CC-E1ACC-E2
CC-E3
CC-E5A
CC-E5BCC-V26BDesalter
CC-V26ADesalter
CC-E4A
CC-E4B
CC-E6A
CC-E6B
CC-V6Pre-Flash Drum
CC-E62B
CC-E62A
CC-E8B
CC-E8A
aprrox. 250°C
CC-H1AFired Heater
CC-H1BFired heater
CC-V2HGO
Stripper
CC-V5Overhead Receiver
Water
Steam
Kerosene
Amospheric residue
360-370°C
CC-V3LGO
Stripper
CC-V4KeroseneStripper
Steam
Steam
CC-E13Kerosene Product
Air Cooler
Kerosene- MEROX- Storage
- LGO/Diesel Hydrotreater
CC-E9
Naphtha PA
4
14
15
24
26
27
33
35
36
43
48 LGO PA
CC-E37D
CC-V19Naphtha Splitter
CC-V7Desalting Water
Surge Drum
CC-V13Straight Run Stabilizer
Charge Drum
HGO Product
Naphtha PA
Naphtha Product
HGO PA
CC-E12LGO Product
Air Cooler
LGO- HGO- Storage
- Diesel Hydrotreater
CC-E28D
CC-V14Debutanizer
CC-E61B
CC-E61A
CC-E63
CC-E10HGO Product
Air Cooler
HGO- Storage- Diesel Hydrotreater
CC-E7B
CC-E7A
Steam3.5 bar
CC-V15
CC-E29
CC-E43
CC-E27
Heavy Naphthato:
- ISOMAX, etc.
Heavy Naphthato Storage
CC-E35
CC-E36
CC-V20
CC-E40
CC-V21
CC-E38
CC-E39
Medium Naphthato Storage
Chemical Naphthato Storage
CC-V22Deisopentanizer
CC-E41
CC-E42
CC-V23
CC-E44
CC-E45
Isopentanesto MEROX
CC-E46
CC-E47
Light gasoline to MEROX unit
CC-C1
P-51
Atmospheric residue to VDU
C3+C4 to De-ethanizer
CC-V1Crude tower
CC-E49
Steam out
Steam in
Petrogal Refinery
Process Model
11
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Flowsheet of Petrogal`s crude tower & fractionation
Pre-heatingFractionation
Crude tower
13
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Aspen HYSYS® implementation
Subflowsheet
Pre-heating
Fractionation
sectionSubflowsheet
Crude tower
Petrogal RefineryProcess Model
Petrogal Refinery
Utility system model
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SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Aspen Plus® implementation
Steam generation
Steam AP 80.5 bar
Steam IP 24 bar
Steam MP 10.5 bar
Steam LP 3.5 bar
Plant-I
Plant-II
Plant-III
DCI´s Steam Cracker15
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Process Plant
Combustion Air
Propylene
Utility System
Internal Fuel Gas
Ethylene
Fuel-Oil
Steam, Electricity
(Natural Gas)
LPG´s(Ethane/Propane/Butane)
Py-gas
Naphtha/Field Condensate
Combustion Air
Consists of 2 subsystems
Process plant
Utility system
DCI´s Steam Cracker
Process Model
16
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Flowsheet of DCI´s steam cracker plant
TLE
FPH
ECO
HTC-1
DSSH
HPSSH
HPSSH
HTC-2
Naphtha FeedC-2001
Primary FractionationC-2401
E-2401
I
C-2402
E-2402
II
C-2403
E-2403
III
C-2406
E-2404
IV
C-2408
E-2405
V
E-3002
E-3001 E-3003
E-3004
C-3001
E-3005
C-3101Deethanizer I
E-3101 A/B
C-3104Deethanizer II
E-3101 C/D
SLL
E-3103
Ethylene
Propylene Propylene
C-5001Depropanizer
E-3104CW
C-3204Acetylene
Hydrogenation
C-2901Dryer
with Activated Alumina
E-3308
Methane +H2
Ethylene
C-3401Demethanizer
Methane
E-3401
C-3801C2 Splitter
E-3801
E-4001
Ethaneto Furnace F 1011-3
E-5002
C-5002
Propylene
E-5001
SLL
C-5501C3-Stripper
E-5504
C-5503
Propylene
H2, Methane, C2s to Cracked Gas Compressor
E-5503
SLL
P-138
E-5601 A
SLL
C-5601C3 Splitter I
C-5603
E-5601 B
SLL
C-5602C3 Splitter II
C-5604
E-5604
E-5605
CW
CW
PropaneFuel Gas
E-5602
Propylene
C4+ Fraction
E-2002
Water/Gasoline
Gasoline
Condensed Hydrocarbons
Water
Ethylene
TLE
FPH
ECO
HTC-1
DSSH
HPSSH
HPSSH
HTC-2
Quench Oil
TLE
FPH
ECO
HTC-1
DSSH
HPSSH
HPSSH
HTC-2
F 1001-9
F 1010/14
F 1011-13
Ethane-Recyclefrom C2-Splitter
C-3801
11x
Naphtha
furnaces
3x Ethane
recycle
furnaces
Transfer Line Exchangers (TLEs)
Primary fractionation
Compression
Separation
section
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SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Naphtha
furnaces
Ethane recycle
furnaces
Transfer Line Exchangers (TLEs)
Primary fractionation
Compression
Separation section
Aspen Plus® implementation
DCI´s Steam CrackerProcess Model
DCI´s Steam Cracker
Steam network model
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SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Aspen Plus® implementation
SHH line
SM line
SLL lineSL line
SH line
Fertinagro
Process Model
19
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Aspen Plus® implementation (simplified)
Detailed granulator model in Matlab®
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Metamodeling
Generation of fast and robust models based in complex models (or in historical data)
22
Methodology23
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Objectives:
Reducing time of simulation
Increase robustness
Consideration of interaction effects
TUDO TECNALIA, COSMO,
CIRCE
Process
ModelsMeta-models
Sensitivity
AnalysisOptimization
Optimized
CPPs
TUDO, CIRCE,
COSMO
COSMO, (TUDO)
CPPs
Finding a better solution24
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Simulation of
the plant
ASPEN©
MATLAB©…
Varying
Process
parametersDegrees of
freedom
Varying KPA Key Performance
Attributes
Varying KRI Key Resource
Indicators
Process parameters:
Key Performance
Attributes:
Key Resource
Indicators:
Changes in operating conditions are considered in the
design space (range of variation)
Limitations and constraints that must be met when looking
for a better solution
Evaluation indicators that enable to sort the solutions (from
the worst to the best one).
Finding a better solution25
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Design space
PP1
PP2
KPA1
KPA2
Ra
nge o
f va
ria
tion
of
PP2
Range of variation
of PP1
KRI1
KRI2
Out of the design space not simulated
Not comply with constraints discarded (outlier)
Constrained
domain
Valid alternative
Valid and better alternative (minimizes KRIs)
Why a Surrogate?
A lot of alternatives
A lot of simulations
Use of Surrogate to save time
How to create a Surrogate?
Surrogate modelling26
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Design space
coverage
PP1
PP2 Advantage of Kriging technique
Solid Mass Flow (kg/h) Water Mass Flow (kg/h)
Error
Count
Random exploration of the design space:
Assess potential KRI improvement
Ex: FERTINAGRO
Current Exergy = 24 kJ/kJ
Minimum Exergy (model) = 18 kJ/kJ
Potential reduction = 25%
Sensitivity analysis:
Model quality and Critical Process Parameters identification
Insight from the surrogate27
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Sensitivity analysis
Determination of CPP’s
28
Methodology29
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Objectives:
Reducing the number of process parameters by identifying
CPPs
Consideration of interaction effects
TUDO TECNALIA, COSMO,
CIRCE
Process
ModelsMeta-models
Sensitivity
AnalysisOptimization
Optimized
CPPs
TUDO, CIRCE,
COSMO
COSMO, (TUDO)
CPPs
Sensitivity Analysis30
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Global Sensitivity
Analysis Tool
Sampling
Input:
- Operating parameters
- Standard value
- Minimum value
- Maximum value
KRIs from
Meta-models
Output:
- Total sensitivity
indices
- First-order
sensitivity indices
- Interaction effects
Sensitivity
calculation
0
0,1
0,2
0,3
0,4
0,5S_Ti
S_i
Interaction
Measuring the influence of operating parameters on
the KRIs
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Plant optimization
33
Methodology34
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Objectives:
Finding optimal values of CPPs for maximizing/minimizing
KRIs
Consideration of interaction effects
TUDO TECNALIA, COSMO,
CIRCE
Process
ModelsMeta-models
Sensitivity
AnalysisOptimization
Optimized
CPPs
TUDO, CIRCE,
COSMO
COSMO, (TUDO)
CPPs
35
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Increased resource & energy efficiency
Optimization
Optimization
Critical ProcessParameters(CPPs)
KPA-Calculator
Meta-models
KRIs from Meta-
models
KRI1
CPP1Allowed KPA-range
36
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Optimization
KRI-Objectives:
Headline indicators
Material efficiency [kg/FU]
Direct primary energy consumption [J/FU]
Gross water use [m³/FU]
Net water use [m³/FU]
Resource Exergy indicator (materials, energy and water) [J/FU]
More or less
conventional
indicators
Novel “aggregated” indicator
• Conventional indicators as benchmark
• Aggregated resource exergy indicator
Is it a superior indicator for the different industrial sectors?
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016
Impacts of TOP-REF
Expected outcome on energy intensive industries, SPIRE sectors and SPIRE roadmap
37
TOPREF impacts the following KA:
1. K.A. 2.3: Process monitoring, control and optimization: Couple the
materials and energy flows for a better and global optimization
2. This will drive industry to a K.A. 2.4: More efficient systems and
equipment.
3. The mix of model methodology and tools with the exergy concept provide
K.A. 2.5 New energy and resource management concepts
(including industrial symbiosis)
All in all, provide tools for better decision making and
improve the resiliency and competitiveness of the
industry.
SPIRE roadmap and impacts38
SPIRE Workshop on Resource Efficiency Monitoring, Assessment and OptimizationBrussels January 27th , 2016