Innovative tools, Methodology for modelling in industry to ...

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


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


Overview of the

methodology

4

Methodology6


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


Detailed Plant models

Modelling of the plant in different scenarios

7

Methodology8


Objectives:

Comprehensive characterization of the systems

Multiple operation scenarios


CIRCE

Process

ModelsMeta-models

Sensitivity


Optimized

CPPs

TUDO, CIRCE,

COSMO

COSMO, (TUDO)

CPPs

Petrogal`s Refinery10


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


Flowsheet of Petrogal`s crude tower & fractionation

Pre-heatingFractionation

Crude tower

13


Aspen HYSYS® implementation

Subflowsheet

Pre-heating

Fractionation

sectionSubflowsheet

Crude tower

Petrogal RefineryProcess Model

Petrogal Refinery

Utility system model

14


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


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


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

17


Naphtha

furnaces

Ethane recycle

furnaces

Transfer Line Exchangers (TLEs)

Primary fractionation

Compression

Separation section


DCI´s Steam CrackerProcess Model

DCI´s Steam Cracker

Steam network model

18



SHH line

SM line

SLL lineSL line

SH line

Fertinagro

Process Model

19


Aspen Plus® implementation (simplified)

Detailed granulator model in Matlab®


Metamodeling

Generation of fast and robust models based in complex models (or in historical data)

22

Methodology23


Objectives:

Reducing time of simulation

Increase robustness

Consideration of interaction effects


CIRCE

Process

ModelsMeta-models

Sensitivity


Optimized

CPPs

TUDO, CIRCE,

COSMO

COSMO, (TUDO)

CPPs

Finding a better solution24


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


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


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



Sensitivity analysis

Determination of CPP’s

28

Methodology29


Objectives:

Reducing the number of process parameters by identifying

CPPs



CIRCE

Process

ModelsMeta-models

Sensitivity


Optimized

CPPs

TUDO, CIRCE,

COSMO

COSMO, (TUDO)

CPPs

Sensitivity Analysis30


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


Plant optimization

33

Methodology34


Objectives:

Finding optimal values of CPPs for maximizing/minimizing

KRIs



CIRCE

Process

ModelsMeta-models

Sensitivity


Optimized

CPPs

TUDO, CIRCE,

COSMO

COSMO, (TUDO)

CPPs

35


Increased resource & energy efficiency

Optimization

Optimization

Critical ProcessParameters(CPPs)

KPA-Calculator

Meta-models

KRIs from Meta-

models

KRI1

CPP1Allowed KPA-range

36


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?


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


“This project has received funding from the European Union’s Seventh Programme

for research, technological development and demonstration under grant agreement No 604140”

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