TUSTP 2003 by Vasudevan Sampath by Vasudevan Sampath May 20, 2003 Intelligent Control of Compact...
-
Upload
frederick-brown -
Category
Documents
-
view
221 -
download
2
Transcript of TUSTP 2003 by Vasudevan Sampath by Vasudevan Sampath May 20, 2003 Intelligent Control of Compact...
TUSTP 2003TUSTP 2003TUSTP 2003TUSTP 2003
byVasudevan SampathVasudevan Sampath
byVasudevan SampathVasudevan Sampath
May 20, 2003May 20, 2003
Intelligent Control of
Compact Separation System
Intelligent Control of
Compact Separation System
Overview Overview
Objectives
Literature Review
Compact Separation System
Review of Control System Development
Fuzzy Logic System
Artificial Neural Network System
Future Plans
ObjectivesObjectives 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 ReviewLiterature 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
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
Literature ReviewLiterature Review
Compact Separation System 1Compact Separation System 1
Clean
Water Rich
Oil Rich
GLLCC (3-phase)
Pipe Type Separator
GLCC (Scrubber)
LC
PC
LC
WCC
WCC FC
Pump
Clean OilOil
Water Rich
Oil Rich
GLLCC (3-phase)
Pipe Type Separator
GLCC (Scrubber)
ManifoldSlug Damper
LCLC
PCPCClean GasClean GasLCLC
WCCWCC
WCCWCC
Hydrocyclones
LLCCPRC
PRC
Hydrocyclones
LLCC
FCFC PRCPDC
PDC
PumpPump
Clean Water
LC-Level Control
PC-Pressure Control
WCC-Water cut Control
FC-Feed Control
PDC-Press. Diff. Control
Compact Separation System 2Compact Separation System 2
PCPC
LCLC
Clean Gas
LCLC
Hydrocyclones
LLCCPRC
PRC
Hydrocyclones
LLCC
FCWC PRCPDC
PDC
PumpPump
WCCWCC
GLCC (Scrubber) Pipe Type Separator
Clean
OilOil
ManifoldSlug Damper
GLCC
Liquid Stream
Gas Stream
Clean Water
LC-Level Control
PC-Pressure Control
WCC-Water cut Control
FC-Feed Control
PDC-Press. Diff. Control
No.
APPLICATION CLASSES
Passiv
e C
on
tro
l S
yste
m
Liq
uid
Level C
on
tro
l w
ith
LC
V O
nly
Liq
uid
Level C
on
tro
l w
ith
GC
V O
nly
Hyb
rid
LC
V a
nd
GC
V L
evel C
on
tro
l
Pre
ssu
re C
on
tro
l w
ith
GC
V
Liq
uid
Level C
on
tro
l w
ith
LC
V
an
d P
ressu
re C
on
tro
l w
ith
GC
V
Flo
w R
ate
Co
ntr
ol w
ith
LC
Van
d G
CV
Pre
dic
tive C
on
tro
l o
f G
LC
C
usin
g S
lug
Dete
cti
on
GL
CC
O
pti
mal an
d A
dap
tive
Co
ntr
ol -
Mo
vin
g S
et
po
int
Wate
rcu
t C
on
tro
l S
yste
m
GV
F C
on
tro
l S
yste
m
Ro
bu
st
Co
ntr
ol W
ith
G
ain
Sch
ed
ulin
g
Mo
dern
Co
ntr
ol -
Fu
zzy L
og
ic C
on
tro
l
Inte
llig
en
t C
on
tro
l -
Art
ific
ial N
eu
ral N
etw
ork
Do
wn
Str
eam
ON
/OF
F
Pu
mp
Co
ntr
ol
GL
CC
Du
al In
let
Co
ntr
ol
GL
CC
Vari
ab
le A
rea
Inle
t C
on
tro
l
1 Remote Powerless GLCC Operation X2 Remote GLCC Operation With Power X X X X X X X X X X X3 Well Testing (Recombined Flow) X X X X X X X X X X X X4 Bulk Separation (Separator Stand Alone) X X X X X X X X X X5 DownStream Surge Tank Control X X6 Separation of Wet Gas (raw Gas Lift) X X X X X X X X X X X
7Separation of Low-Medium GOR (Liquid Dominated)
X X X X X X X X
8 Separator subjected to Severe Slugging X X X X X
9Integrated Separation systems - 2 Stage GLCCs
X X X X X X X X
10 GLCC with Liquid Hydrocyclones X X X X X X X X X11 GLCC Upstream of pumps X X X X X X X X12 GLCC with Conventional Separators X X X X X X X X X X X13 Subsea Application X X X X X X X X X14 Downhole Applications X X X X X
15Non-Petroleum Application - Liquid Metering
X X X X X
16Non-Petroleum Application - Gas Metering
X X X X X X
17 FREE-WATER Knockout with LLCC X X X X X X X X X18 GLCC/LLCC Integrated System Control X X X X X X X X X X X X X X X19 GLCC for Environmental Applications X X X X X X
CONTROL STRATEGIES
Control System Development StagesControl 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 ControlAdaptive 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 ControllersLimitations 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 ControlFuzzy Logic Control
Crisp man Fuzzy man
How are you going to park a car ?
It’s eeeeassy……!
Just move slowly back and avoid any obstacles.
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………..
Benefits of Fuzzy Logic ControllerBenefits 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 ControllerOperation of Conventional Controller
PID Controller
PLANTInput Output
Feedback Signal
Operation of Fuzzy Logic ControllerOperation of Fuzzy Logic Controller
OutputF
uzz
ific
atio
n
Def
uzzi
fica
tionInference
mechanism
Rule-base
PLANT
Reference Input r(t)
Input u(t)
Fuzzy Controller OperationFuzzy Controller Operation
Choosing Inputs
Measuring Inputs
Scaling Inputs
Fuzzification
Fuzzy Processing
Defuzzification
Scaling Outputs
PLANT
Inputscalingfactors
Inputs membership functions
Fuzzy rules
OutputsMembership functions
Outputs Scaling factors
Neural Network Process Control LoopNeural Network Process Control Loop
Sensing System
Neural Network Analysis System
Neural Network Decision System
Plant Operating SystemInput Output
Basic Artificial Neural NetworkBasic Artificial Neural Network
Basic Artificial Neural NetworkBasic Artificial Neural Network
Feed forward ANN – a,b
Feed back ANN - c
Advantages of Neural NetworkAdvantages 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 SystemsNeuro 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
ApplicationsApplications
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 PlansFuture 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.