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

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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.