Transcript No Slide Title

TUSTP 2003
Intelligent Control
of
Compact Separation System
by
Vasudevan Sampath
May 20, 2003
Overview
 Objectives
 Literature Review
 Compact Separation System
 Review of Control System Development
 Fuzzy Logic System
 Artificial Neural Network System
 Future Plans
Objectives
 Conduct a detailed study on advanced control
systems like fuzzy logic, neural network etc. and
study their suitability for compact separation system.
Develop an intelligent control strategy for compact
separation system and conduct dynamic simulation
and experimental investigation on the developed
strategy.
Literature Review
Control System Studies:




Wang (2000) : Dynamic Simulation, Experimental
Investigation and Control System Design of GLCC
Dorf & Bishop (1998): Modern Control Systems
Grimble (1994): Robust Industrial Control
Friedland (1996): Advanced Control System Design
Literature Review
Fuzzy Logic and Neural Networks:





McNeill and Thro (1994): Fuzzy Logic
Leondes (1999): Fuzzy Theory Systems –
Techniques and Applications
Terano, Asai and Sugeno (1994): Applied Fuzzy
Systems
Passino and Yurkovich (1998): Fuzzy Control
Reznik (1997): Fuzzy Controllers
Compact Separation System 1
LC-Level Control
Oil Rich
Clean
Oil
LC
PC-Pressure Control
WCC-Water cut Control
Pipe Type
Separator
PC
Clean Gas
FC-Feed Control
WCC
LC
PDC-Press. Diff. Control
GLCC (Scrubber)
Pump
Manifold
Slug Damper
WCC
Water Rich
GLLCC (3-phase)
FC
PDC
PRC
LLCC
PRC
PDC
Hydrocyclones
Clean Water
Compact Separation System 2
LC-Level Control
Gas Stream
PC-Pressure Control
Clean Gas
PC
WCC-Water cut Control
Clean
LC
FC-Feed Control
PDC-Press. Diff. Control
Oil
Pipe Type
Separator
GLCC (Scrubber)
WCC
Pump
Manifold
Slug Damper
WC
FC
Liquid Stream
PDC
PRC
LLCC
PRC
PDC
LC
GLCC
Hydrocyclones
Clean Water
7
8
9
10
11
12
13
14
15
16
17
18
19
X
X
X
X
X
X
X
X
GLCC Variable Area
Inlet Control
X
X
X
GLCC Dual Inlet Control
X
X
DownStream ON/OFF
Pump Control
X
X
X
Intelligent Control Artificial Neural Network
X
X
X
Modern Control Fuzzy Logic Control
Predictive Control of GLCC
using Slug Detection
X
X
Robust Control With
Gain Scheduling
Flow Rate Control with LCV
and GCV
X
X
X
GVF Control System
Liquid Level Control with LCV
and Pressure Control with GCV
X
Watercut Control System
Pressure Control with GCV
X
GLCC Optimal and Adaptive
Control - Moving Set point
Hybrid LCV and GCV Level Control
Remote Powerless GLCC Operation
Remote GLCC Operation With Power
Well Testing (Recombined Flow)
Bulk Separation (Separator Stand Alone)
DownStream Surge Tank Control
Separation of Wet Gas (raw Gas Lift)
Separation of Low-Medium GOR
(Liquid Dominated)
Separator subjected to Severe Slugging
Integrated Separation systems
- 2 Stage GLCCs
GLCC with Liquid Hydrocyclones
GLCC Upstream of pumps
GLCC with Conventional Separators
Subsea Application
Downhole Applications
Non-Petroleum Application
- Liquid Metering
Non-Petroleum Application
- Gas Metering
FREE-WATER Knockout with LLCC
GLCC/LLCC Integrated System Control
GLCC for Environmental Applications
Liquid Level Control with GCV Only
1
2
3
4
5
6
Liquid Level Control with LCV Only
No.
APPLICATION
CLASSES
Passive Control System
CONTROL STRATEGIES
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Control System Development Stages
 1st Stage: Frequency –response design methods for scalar
systems by Nyquist, Bode
 2nd Stage: The state-space approach to optimal control and
filtering theory
 3rd Stage: Multivariable systems by frequency-domain
design methods (MIMO)
 4th Stage: Robust design procedures - H design
philosophy
 5th Stage: Advanced techniques – Fuzzy Logic, Neural
Networks, Artificial Intelligence.
Adaptive Versus Robust Control
 Adaptive Control – Estimates parameters and
calculates the control accordingly. Involves online
design computations, difficult to implement.
 Robust Control – This allows for uncertainty in the
design of a fixed controller, thus, producing a robust
scheme, which is insensitive to parameter variations or
disturbances. H robust control philosophy provides
optimal approach to improve robustness of a controlled
system.
Limitations of Conventional Controllers
 Plant non-linearity: Nonlinear models are computationally
intensive and have complex stability problems.
 Plant uncertainty: A plant does not have accurate models
due to uncertainty and lack of perfect knowledge.
 Uncertainty in measurements: Uncertain measurements do
not necessarily have stochastic noise models.
 Temporal behavior: Plants, Controllers, environments and
their constraints vary with time. Time delays are difficult to
model.
Fuzzy Logic Control
How are you going to park a car ?
You have to switch
to reverse, then push
an accelerator for 3
minutes
and
46
seconds and keep a
speed of 15mph and
move 5m back after
that try………..
Crisp man
It’s eeeeassy……!
Just move slowly
back and avoid any
obstacles.
Fuzzy man
Benefits of Fuzzy Logic Controller
 Can cover much wider range of operating conditions than PID
and can operate with noise and disturbance.
 Developing a fuzzy logic controller is cheaper than developing
a model-based controller.
 Fuzzy controllers are customizable. Since it is easier to
understand and modify their rules.
Operation of Conventional Controller
Input
PID
Controller
Feedback Signal
Output
PLANT
Operation of Fuzzy Logic Controller
Reference
Input r(t)
Inference
mechanism
Rule-base
Input u(t)
PLANT
Output
Fuzzy Controller Operation
Choosing Inputs
Measuring Inputs
Scaling Inputs
Fuzzification
Fuzzy Processing
Input
scaling
factors
Inputs
membership
functions
Fuzzy
rules
Defuzzification
Outputs
Membership
functions
Scaling Outputs
Outputs
Scaling
factors
PLANT
Neural Network Process Control Loop
Input
Sensing
System
Neural Network
Analysis System
Output
Plant Operating System
Neural Network
Decision System
Basic Artificial Neural Network
Basic Artificial Neural Network
Feed forward ANN – a,b
Feed back ANN - c
Advantages of Neural Network
 Simultaneous use of large number of relatively simple
processors, instead of using very powerful central
processor.
 Parallel computation enables short response times for
tasks that involve real time simultaneous processing of
several signals.
 Each processor is an adaptable non linear device.
Neuro Fuzzy Systems
 Neural Networks are good at recognizing patterns, not good at
explaining how they reach that decision
 Fuzzy logic are good at explaining their decision but they cannot
automatically acquire the rules they use to make those decisions
 Central hybrid system which can combine the benefits of both are
used for intelligent systems
 Complex domain like process control applications require such hybrid
systems to perform the required tasks intelligently
 In theory neural network and fuzzy systems are equivalent in that they
are convertible, yet in practice each has its own advantages and
disadvantages
Applications
Fuzzy Logic and Neural Network applications to compact separation
system:
 Dedicated control system for each component, like GLCC or LLCC
 Sensor fusion – improvement in reliability and robustness of sensors
 Supervisory control – intelligent control system with diagnostics
capabilities.
Future Plans
1. Develop dedicated control systems for each component using
neural network or adaptive control system.
2. Develop sensor fusion modules using neural networks to improve
the quality of measured signal.
3. Develop intelligent supervisory control system for overall
control, monitoring and diagnostics of the process.