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Data-driven Models in the Chemical Process Industry. Bram Jansen and Olaf Abel BASF Antwerpen N.V. STI – Automation Services Workshop Honorary Doctorate Degree Prof. Dr. Lennart Ljung Leuven, 12./13.10.2004. Agenda. Introduction to BASF and its Antwerp site

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data driven models in the chemical process industry

Data-driven Modelsin the Chemical Process Industry

Bram Jansen and Olaf AbelBASF Antwerpen N.V.STI – Automation Services

Workshop Honorary Doctorate Degree Prof. Dr. Lennart Ljung Leuven, 12./13.10.2004

agenda
Agenda
  • Introduction to BASF and its Antwerp site
  • Drivers and challenges in chemical process industry
  • Opportunities
  • Case Studies
  • Reflection and conclusion
basf the chemical company
BASF – The Chemical Company
  • The world‘s leading chemical company
  • Product segments: plastics, chemicals, performance products, agriculture & nutrition products, oil & gas
  • Successful due to global presence, Verbund, active portfolio management
  • Number of employees (30.6.2004): 85.124
  • Financial results (2003):
    • Sales: 33.361 Mio €
    • EBIT: 2.658 Mio €
    • Cashflow: 4.878 Mio €
basf antwerpen n v some key data
BASF Antwerpen N.V.Some key data
  • Located in most northerly part of the Antwerp port
  • Largest chemical production site in Belgium,total surface: 598 ha, approximately 65% developed
  • 3.700 BASF employees and 1.400 from associates companies
  • Financial results (2003):
    • Sales: 3.666 Mio €
    • EBIT: 383 Mio €
    • Cashflow: 468 Mio €
  • Energy consumption (2003): 10,3 TWh (3,5 % of Belgium)
integrated production site verbund 54 plants in 4 production sectors
Integrated Production Site (Verbund)54 plants in 4 production sectors

Plastics and

Fibers(46,0%)

Chemicals and

other products(35,3%)

Fertilizers and Inorganics(9,7%)

Performance

Products(8,9%)

BASF-products

polyurethane raw materials, polystyrene, ethylbenzene, styrene, Styrolux®, ABS,caprolactam

ethylene, propylene,benzene, PIB, amines.

nitric acid, sulphuric acid,ammonia, compound andsimple fertilizers

ethylene oxide, glycol, acrylic acid, superabsorbers

Applications

household appliances, toys, sports equipment, insulating and packing material, seats,car parts, carpets, nylon, …

crop-protecting, pharmaceutical products, additives for fuels and lubricants, ...

fertilizers

anti freeze, detergents,PET bottles, diapers, …

automation services central competence center for operations
Automation Services – Central competence center for Operations

Enterprise Resource Planning,Business IT, SAP/R3

BASF IT Services N.V.

ERP

Advanced Process Control, Real-time Optimization

APC, RTO

Process InformationManagement Systems

PIMS, LIMS

Automation Services

Process Control

DCS, PLC

Measure, Communicate

Field Devices & Infrastructure

Operate

Plant

Operations

agenda1
Agenda
  • Introduction to BASF and its Antwerp site
  • Drivers and challenges in chemical process industry
  • Opportunities
  • Case Studies
  • Reflection and conclusion
the chemical industry in europe
The Chemical Industry in Europe
  • Total GDP grows with approximately 2%.
  • Chemical market growth below average GDP growth.
  • Most investments go to Middle or Far East, in particular China.
  • High dependence on external influences (feedstock prices).
  • Increasing competition.

The existing plant capacityhas to be used optimally!

achieving optimality

The interesting quantities are often difficult to measure.

  • The influence of the DoF on the OF is not obvious.
  • The used problem formulation isplant-specific andtime-dependent.
Achieving optimality
  • Possible objective functions (OF):
    • throughput, conversion, …
    • raw materials, energy, …
    • product specification
  • Degrees of freedom (DoF)(controller setpoints)
  • Constraints:
    • product quantities
    • product qualities
    • safety

Optimal operation is only achievable by applying process models!

different types of models
Different types of models

Possible requirements:

  • Dynamic
  • Nonlinear behaviour
  • Resolution in time and space
  • Extrapolation capabilities
  • Property distributions

First principles:

  • Based on balance and phenomenological equations
  • Process knowledge required
  • Comparatively expensive

Data driven:

  • Identified using process data
  • Comparatively inexpensive
agenda2
Agenda
  • Introduction to BASF and its Antwerp site
  • Drivers and challenges in chemical process industry
  • Opportunities
  • Case Studies
  • Reflection and conclusion
data collection

server

PC

PC

PC

collector

router

DCS

Data collection
  • PIMS: Plant Information Management System
  • Long term data historian(> 5 years online data)
  • Link between process controland office/engineering world
  • Site infrastructure(> 90.000 tags covered)
  • Similar technology availablefor laboratory data: LIMS(currently 5.000 sample points)
model structures and idenfication techniques

Steady state

Dynamic

Models

  • ARX, ARMAX, OE, BJ, …
  • Finite Impulse Response
  • State Space
  • Least Squares
  • Subspace identification
  • Prediction error methods
  • Numerical optimization

Identification

Model structures andidenfication techniques
  • Multiple Linear Regression
  • PCA and PLS
  • Neural Networks
  • Least Squares
  • Backpropagation
  • Numerical optimization
software tools used within basf
Software tools used within BASF
  • SAS – JMP
  • IPCOS – Presto
  • Mathworks – MATLAB System Identification Toolbox
  • Tailored applications
general methodology to develop data driven models
General methodology to developdata-driven models
  • Integration of knowledge
  • linear / non linear
  • static / dynamic
  • Experiences
  • Feasibility
  • Requirements
  • Noise
  • Disturbances
  • Maintenance

model structure selection and parameter estimation

Fine tuningand confidence interval

Pre- processing raw data

Online application

Preliminary investigation

  • Validation
  • Interpolation
  • Outlier detection
  • Analysis
  • Sensitivity analysis
agenda3
Agenda
  • Introduction to BASF and its Antwerp site
  • Drivers and challenges in chemical process industry
  • Opportunities
  • Case Studies
  • Reflection and conclusion
case 1 polymer production

QI

QI

Case 1: Polymer production
  • Process with inherent time lags and dead times
  • Production of 2 different grades
  • Relative viscosity (polymer / solvent) is main product specification

laboratory analysis

Reaction

Granulation

Extraction

Drying

Monomer

Polymer

15 h

18 h

soft sensor for relative viscosity

η = f(T,p,m)

Soft sensor for relative viscosity
  • Relative viscosity is measured twice per day with high precision (0.01)
  • (laboratory / offline) analysis time is 4h
  • Potential in avoiding off-spec production:
    • fast detection of new product specification during grade change
    • fast reaction on disturbances influencing relative viscosity
    • reduction in number of (expensive) offline analysis

continuously and easily measurable variables

predictionof relativeviscosity

model

engineering and implementation
Engineering and implementation

UNILAB(Siemens)

PHD(Honeywell)

Digital Control System

EXCEL(MS)

JMP(SAS)

PRESTO(IPCOS)

results comparison model analysis

Relative Viscosity after drying

laboratoryanalysis

soft sensor

18 months

Results: comparison model / analysis
  • 2 models (low/high grade)after granulation
  • 2 models (low/high grade)after drying
  • Multiple LinearRegression (MLR)
  • 13 - 17 input parameters(reaction and granulation section)
results grade change

model based sensor

labo analysis

Relative viscosity

19.2.2003

25.2.2003

28.2.2003

22.2.2003

Relative viscosityis provided continuouslywith high precision!

Results: grade change
  • RMSE < 0.009
  • 80% of errors < 0.01
  • 98% of errors < 0.02
case 2 steamcracker
Case 2: Steamcracker
  • Largest plant at BASF Antwerpen N.V.
  • Consumes appr. 2,6 Mio. t/a of feedstock (mainly naphtha)

Capacity:

  • C2: 800.000 t/a
  • C3: 500.000 t/a
  • C4: 300.000 t/a
  • Benzene, TX
operational environment of the steamcracker

C

H

2

4

C

C

H

H

2

2

4

4

Mass fraction

C

H

3

6

C

C

H

H

3

3

6

6

H

2

H

H

2

2

Cross over temperature

4

4

H

H

Classical application area formodel-based optimization and multivariable predictive control!

2

2

900 ºC

900 ºC

C

C

850 ºC

850 ºC

Mass fraction

Residence time

Residence time

Operational environment of the steamcracker
  • Many operational degrees of freedom
  • A lot of changing operating conditions
    • feedstock qualities,
    • feedstock and product prices,
    • furnace decoke
model predictive control

Hold plant at optimal operating point:

setpoint

setpoint

(from optimizer)

(from optimizer)

  • Enforce constraints!
  • Prevent interaction among SISO control loops!
  • Minimize necessary control action!

controlled variable

controlled variable

(predicted)

(predicted)

measured

measured

manipulated

manipulated

variable

variable

time

time

control horizon

control horizon

prediction horizon

prediction horizon

Model Predictive Control
  • Multivariable
  • Rigorous incorporation of constraints
  • (Limited) optimization capabilities
engineering and implementation1
Engineering and implementation
  • Process data collected by executing step tests
  • PRBNS input
  • Mainly FIR models(linear application)
  • Identification basedon LS algorithms
size of the controller

Process Part

MV

FF

CV

Naphtha or LPG furnace

9

7

34

Ethane furnace

10

11

36

Cold side

36

20

82

Depropanizer

5

14

11

Propylene fractionator

11

29

9

Feedmaximizer

143

137

448

Size of the controller
results c 2 splitter separation ethane ethylene
Ethylene loss in bottom ethaneResults: C2 splitter (separation ethane/ethylene)

Feed to C2 splitter

Ethane pollution intop ethylene product

results c 3 splitter two colums separation propane propylene
Results: C3 splitter – two colums(separation propane/propylene)

Propane in top propylene product(1. and 2. column)

Combinedfeed to both columns

Propylene loss in bottom propane (1. and 2. column)

agenda4
Agenda
  • Introduction to BASF and its Antwerp site
  • Drivers and challenges in chemical process industry
  • Opportunities
  • Case Studies
  • Reflection and conclusion
reflection with academic research
Reflection with academic research
  • Chemical processes are nonlinear and time-variant.
  • Nevertheless, the examples are all dealing with linear technology.
  • Within the domain of linear techniques, the simple methods are used.
  • Theoretical properties are neglected in most cases.
  • Only some attempts exist to useadvanced technologies(polymer processes,batch processes, …).
  • The (economical) justificationfor using those technologieshas to be provided.
conclusions
Conclusions
  • Data-driven models help to improve the operation of chemical processes and to drive them towards optimality.
  • The necessary infrastructure (PIMS, LIMS) is far developed.
  • The number of applications of data-driven models is growing.
  • In most cases, linear technology is applied.
  • The time might have come to do the next step.
  • Contiuous exchange between academia and industry is permanently required in order to identify appropriate application areas for current research activities.