Transcript Document 7779809

IPCOS‘ R&D interests
Jobert Ludlage
IPCOS
Jobert Ludlage
Bosscheweg 135b
5282 WV Boxtel, Netherlands
http://www.ipcos.com
Tel:
+31 411 613 500
Fax:
+31 411 616 710
Bert Pluymers
Technologielaan 11-0101
3001 Heverlee, Belgium
http://www.ipcos.com
Tel:
+32 1639 7695
Fax:
+32 1639 3080
E-mail: [email protected]
E-mail: [email protected]
Overview
• IPCOS and its markets
• IPCOS products
• R&D philosophy
• Master model concept
• Serving niche markets
• IPCOS Integrated Solution Platform in R&D projects
• Configurable Engine
IPCOS
Boxtel
Leuven
Advanced process control
and optimisation
Chemical
Processing &
refining
Power
Production
Products and Services
Niche Markets
Glass
Manufacturing
Oil
Production
IPCOS’ main products
Planning and
Scheduling
MIS
or
Plantwide
Computer
In-Line
Optimization
Pathfinder
Supervisory
Control
System
Process
Computer
INCAsuite
Property Estimation
DCS
Regulatory Control
System
PRESTO
RAPID
Safety Systems
Process
R&D in cooperation
Technology developments are done in cooperation with universities,
supplier companies and end-user companies:
• Finished projects: Impact, Incoop, polyProms, Regla, softForce, syncPro, synopsys
– Imperial College, KUL, NTNU, RWTH, TUD, TUE, University of Thessaloniki,
University of Rome
– LABOR, MDC, PSE, TNO
– BASF, Bayer, Borealis, Danisco, DOW, DSM, Kemira, LMS, Purac, Rexam,
Roquette, Shell
• Running projects: proMATCH, Crypto, Batch, Distillation
– Imperial College, KUL, NTNU, RWTH, TUD, TUE,
– Cybernetica, PSE
– Bayer, BASF, Cytec, Purac, Schott, Statoil
Master Model Concept I
Demand Operation
driven
driven
• Dynamic flexible process operation
Long term planning
Market expectations
Long term contracts
Process operation
PROCESS
Short term schedule
?
Market opportunities
(sales and purchase)
• Dynamic non-linear process models required
How to obtain and maintain models valid
and accurate over the whole (dynamic)
operating range of the process unit ??
State Estimation
model
inferential
model
Master
model
Master model: Detailed rigorous/hybrid model
Major problem: model-reduction (promatch)
DYNOPT
model
MPC
model
Master Model Concept II
• Functional architecture
market conditions
Hybrid model
Models, and future
prediction (slow)
Dynamic
Optimization
Estimation
measurements
Time Scale
Separation
Models, and future
prediction (fast)
optimal reference
trajectories
Model
Predictive
Control
control setpoints
Plant (including base control)
Remarks:
MPC should evolve from linear to non-linear model predictive control
IMPACT, INCOOP, polyProms, promatch
Technology serving niche markets
• IPCOS is addressing new niche markets where traditional MPC
approach is not viable
• Economically (ROI)
• Technologically
• Missing Process Know how
Batch Processes
Glass
Manufacturing
Crystallization
Processes
Distillation
Columns
Dedicated modeling and control solutions are developed
Solutions fit into broadly applicable approach towards NLMPC
IPCOS Integrated Software Platform
IISP:
• OPC based communication
• Extremely open architecture
• Flexible
IPCOS pc
Potential common software
environment for the project
INCAView
User Interface
Scheduler
INCAEngine
INCATest
MPC Controller
IPCOS
OPC Server
Model
(gPROMS)
Observer
(matlab)
INCACalc
Exchange
Advantages:
Online (plant pc)
• Standardization of project environment:
• Exchange of technology and tools
between partners in the project
• Identical setup for on-line as off-line:
• Easy exchange of off-line tested new
features to the on-line environment
• Replay possibilities of on-line situations
Softnet DataServer
Watchdog
Offline (simulation)
Alternate
OPC Server
PLC/SCADA
Process
(gPROMS)
Process
File2DataServer
INCAEngine flexible MPC
Configurable engine used for control of the
temperature profile (polyPROMS, batch)
Problem
Configuration
Engine
Configuration
Tuning & configuration
User interface
connection
Advantages:
• Enables tailoring the controller to specific
classes of problems.
• Only one source to maintain.
OPC
Model
Static
optimization
QP stat
Tuning
Pre-processing
Model
Post-processing
Dynamic
optimization
QP dyn
Import process data
matlab
OPC
Non-linear prediction
Matlab interface Block 2
Export process data
Dynamic CV setpoint
reference
Matlab interface Block 1
Scheduler
INCAEngine
Non-linear prediction generates:
1. Prediction based on optimal future MV’s of the previous sample instant
2. Generates state space models for dynamic and steady state optimization.
P a s t h o r iz o n
P r e d ic t io n ( o r f u t u r e ) h o r iz o n
c o n s t r a in t
MV
5 .2
u
5 .1
5
A p p r o x i m a t e d y n a m ic m o d e l s
A
b a s e d o n n o n - lin e a r p r e d ic t io n :
B C D E
F
G
H
I
J
CV
4 6 9 .4
y
S e t p o in t
N o n l in e a r p r e d ic t io n
4 6 9 .2
O p t im is e d o u t p u t
t-4 0 0
t-2 0 0
t
t+ 2 0 0
t+ 4 0 0
t+ 6 0 0