1 1 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Vinicius de Oliveira Control and Automation Engineering.

Slides:

Advertisements

Similar presentations

Model-based PID tuning methods Two degree of freedom controllers

Advertisements

Chapter 9 PID Tuning Methods.

Implementation of MPC in a deethanizer at the Kårstø Gas plant

1 Plantwide control Control structure design for complete chemical plants Sigurd Skogestad Department of Chemical Engineering Norwegian University of Science.

PROCESS SYSTEMS ENGINEERING GROUP Førsteamanuensis Tore Haug-Warberg Professor Heinz Preisig Professor Sigurd Skogestad Førsteamanuensis Nadi Skjøndal-Bar.

Forget Laplace Transforms….  Industrial process control involves a lot more than just Laplace transforms and loop tuning  Combination of both theory.

Specialization project 2012 Temperature control of an unstable chemical reactor By Ola Sæterli Hjetland Supervisors: Sigurd Skogestad, Krister Forsman.

Chemical Process Controls: PID control, part II Tuning

Stabilization of Desired Flow Regimes in Pipelines

CHE 185 – PROCESS CONTROL AND DYNAMICS CONTROL LOOP ANALYSIS.

Plantwide process control with focus on selecting economic controlled variables («self- optimizing control») Sigurd Skogestad, NTNU 2014.

Chapter 8 Performance of P-only, PI and PID Controllers.

1 Operation of heat pump cycles Jørgen Bauck Jensen & Sigurd Skogestad Department of Chemical Engineering Norwegian University of Science and Technology.

PID Tuning and Controllability Sigurd Skogestad NTNU, Trondheim, Norway.

1 Feedback: The simple and best solution. Applications to self-optimizing control and stabilization of new operating regimes Sigurd Skogestad Department.

Industrial Process Modelling and Control Ton Backx Emeritaatsviering Joos Vandewalle.

Specialization project Project title “Practical modeling and PI-control of level processes” Student Ingrid Didriksen Supervisor Krister Forsman.

ERT 210/4 Process Control & Dynamics

1 Coordinator MPC for maximization of plant throughput Elvira Marie B. Aske* &, Stig Strand & and Sigurd Skogestad* * Department of Chemical Engineering,

1 Modelling, Operation and Control of an LNG Plant Jens Strandberg & Sigurd Skogestad Department of Chemical Engineering, Norwegian University of Science.

First African Control Conference, Cape Town, 04 December 2003

Book Adaptive control -astrom and witten mark

PSE and PROCESS CONTROL

Department of Chemical Engineering,

Simple rules for PID tuning Sigurd Skogestad NTNU, Trondheim, Norway.

Controller Design (to determine controller settings for P, PI or PID controllers) Based on Transient Response Criteria Chapter 12.

Public PhD defence Control Solutions for Multiphase Flow Linear and nonlinear approaches to anti-slug control PhD candidate: Esmaeil Jahanshahi Supervisors:

Model Reference Adaptive Control (MRAC). MRAS The Model-Reference Adaptive system (MRAS) was originally proposed to solve a problem in which the performance.

PID Controller Design and

1 A Plantwide Control Procedure Applied to the HDA Process Antonio Araújo and Sigurd Skogestad Department of Chemical Engineering Norwegian University.

1 Anti-slug control on a small-scale two-phase loop Heidi Sivertsen and Sigurd Skogestad Departement of Chemical Engineering, Norwegian University of Science.

1 Feedback: The simple and best solution. Applications to self-optimizing control and stabilization of new operating regimes Sigurd Skogestad Department.

1 Active constraint regions for economically optimal operation of distillation columns Sigurd Skogestad and Magnus G. Jacobsen Department of Chemical Engineering.

1 Decentralized control Sigurd Skogestad Department of Chemical Engineering Norwegian University of Science and Tecnology (NTNU) Trondheim, Norway.

1 Self-optimizing control From key performance indicators to control of biological systems Sigurd Skogestad Department of Chemical Engineering Norwegian.

Anti-Slug Control Experiments Using Nonlinear Observers

Control limitations for unstable plants

1 Feedback: The simple and best solution. Applications to self-optimizing control and stabilization of new operating regimes Sigurd Skogestad Department.

1 Feedback: The simple and best solution. Applications to self-optimizing control and stabilization of new operating regimes Sigurd Skogestad Department.

1 Feedback Applications to self-optimizing control and stabilization of new operating regimes Sigurd Skogestad Department of Chemical Engineering Norwegian.

Stabilizing control and controllability:

Control Structure Design: New Developments and Future Directions Vinay Kariwala and Sigurd Skogestad Department of Chemical Engineering NTNU, Trondheim,

1 Self-optimizing control From key performance indicators to control of biological systems Sigurd Skogestad Department of Chemical Engineering Norwegian.

1 Effect of Input Rate Limitation on Controllability Presented at AIChE Annual Meeting in Austin, Texas November 7 th, 2002 Espen Storkaas & Sigurd Skogestad.

1 Combination of Measurements as Controlled Variables for Self-optimizing Control Vidar Alstad † and Sigurd Skogestad Department of Chemical Engineering,

1 Visit at Perstorp Daniel Greiner Edvardsen (Master student) Johannes Jäschke (PhD) April 14th, 2010.

Process control group of Sigurd Skogestad, Departmenmt of Chemical Engineering, NTNU, Trondheim,

Control strategies for optimal operation of complete plants Plantwide control - With focus on selecting economic controlled variables Sigurd Skogestad,

1 1 Sigurd Skogestad | Closed-loop model identification and PID/PI tuning for robust anti-slug control Closed-loop model identification and PID/PI tuning.

1 Sammenligning av lineære og ulineære metoder for robust Anti-slug regulering Slug (liquid) buildup Two-phase pipe flow (liquid and vapor) Sigurd Skogestad.

Coordinator MPC with focus on maximizing throughput

EEN-E1040 Measurement and Control of Energy Systems Control I: Control, processes, PID controllers and PID tuning Nov 3rd 2016 If not marked otherwise,

Probably© the smoothest PID tuning rules in the world: Lower limit on controller gain for acceptable disturbance rejection Sigurd Skogestad Department.

Comparison of nonlinear model-based controllers and gain-scheduled Internal Model Control based on identified model Esmaeil Jahanshahi and Sigurd Skogestad.

The thematic content of the series:

Feedback: The simple and best solution

Stabilization of Desired Flow Regimes in Pipelines

Active Control of Gas Lift Wells Simulation and Experimental Series

Feedback: The simple and best solution

Christoph J. Backi and Sigurd Skogestad

Controllability of a Granulation Process

Implementation of MPC in a deethanizer at the Kårstø Gas plant

Implementation of MPC in a deethanizer at the Kårstø Gas plant

Presented at AIChE Annual Meeting in Indianapolis, USA

Simplified First Principle Model for Severe

Presented at AIChE Annual Meeting in Indianapolis, USA

Operation and Control of Divided Wall (Petlyuk) Column

Performance and Robustness of the Smith Predictor Controller

Espen Storkaas and Sigurd Skogestad

PID control of unstable chemical reactor

Presentation transcript:

1 1 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Vinicius de Oliveira Control and Automation Engineering degree, (Brazil, ). Master thesis at EPFL ( )  First time I met Sigurd (He gave a talk on self-optimizing control) Started PhD August Expect to finish this fall!

2 2 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization What I am doing right now? 1.Control and optimization of energy storage systems 2. Robust and adaptive anti-slug control 3.Self-optimizing control for batch processes 4.Football, chess, skiing

3 3 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization What is next? From October 2015: R&D Engineer at Kelda Drilling Controls Developing robust and adaptive control for managed pressure drilling

4 4 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization An autonomous approach for driving systems towards their limit: an intelligent adaptive anti-slug control system for production maximization Vinicius de Oliveira Johannes Jäschke Sigurd Skogestad Department of Chemical Engineering, NTNU, Trondheim, Norway Two-phase pipe flow (liquid and vapor) Slug (liquid) buildup

5 5 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Outline 1.Explaning the slugging problem 2.Overview of the proposed method o The autonomous supervisor layer o The robust and adaptive control layer 3.Results o How does the method perform in practice? o How does it handle major disturbances? o What if we use a bad baseline controller?

6 6 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization The big picture

7 7 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization The slug cycle

8 8 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization The slug cycle (video) Experiments performed by the Multiphase Laboratory, NTNU

9 9 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization p1p1 p2p2 z Slug cycle (stable limit cycle)

10 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Problems caused by severe slugging Large disturbances in the separators –Causing poor separation performance –Can cause total plant shutdown  production losses! –Increase flaring. Large and rapid variation in compressor load Limits production capacity (increase pressure in pipeline)

11 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization How to avoid slugging?

12 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Avoid slugging: 1. Design change to avoid slugging p1p1 p2p2 z Expensive

13 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Minimize effect of slugging: 2. Build large slug-catcher Most common strategy in practice p1p1 p2p2 z Expensive

14 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization p1p1 p2p2 z Avoid slugging: Close valve (but increases pressure) Problematic for aging fields  increased pressure limits production No slugging when valve is closed

15 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Avoid slugging: ”Active” feedback control

16 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Anti slug control: Full-scale offshore experiments at Hod-Vallhall field (Havre,1999)

17 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Problems with current anti-slug control systems Tend to become unstable (oscillating) after some time –Inflow conditions change –Require frequent retuning by an expert  costly Ideal operating point (pressure set-point) is unknown –If pressure setpoint is too high  production is reduced –If pressure setpoint is too low  system may become unstable

18 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Motivation We want to increase valve opening But larger openings = worse controllability The lager the valve opening  the more difficult it is to stabilize the system –Controller gets more sensitive to uncertainties –Process gain is reduced

19 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Our proposed autonomous control system Setpoint change is key for the adaptation to work well Robust adaptive control Plant Autonomous supervisor Periodically checks the stability of the system Reduces setpoint if control loop is working fine

20 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization How does it work?

21 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Adaptive control based on adaptive augmentation Relies on state-of-the-art output feedback adaptive control techniques  Very successful in the aerospace industry

22 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Adaptive control design

23 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Adaptive control design Feedback term to improve transient dynamics

24 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization How does it perform in practice?

25 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization : Esmaeil Jahanshahi, PhD-work supported by Siemens Experimental mini-rig 3m its dynamical behavior is quite similar to that of much larger rigs water+air mixture

26 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Experimental Results Baseline controller tuned for Z=30% Linearized mechanistic or simple empirical models can be used Note: our models agree very well with experiments

27 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Autonomous supervisor and adaptive LTR controller Safely operates at very large valve openings

28 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Autonomous supervisor and adaptive LTR controller Adaptation gains

29 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Oops, Big disturbance! Emulates a ‘gas-to-oil’ ratio change over 60%

30 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Large change in the operating conditions Supervisor quickly detects major disturbance Moves to safer operating point Adaptive control stabilizes under new operating conditions

31 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization What happens if the baseline controller is poorly tuned?

32 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Poorly tuned PI control as baseline: Adaptation is OFF

33 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Poorly tuned PI control as baseline: Adaptation is ON 1.Supervisor quickly detects major disturbance Desired closed-loop performance is recovered!

34 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Comparison CaseMean valve opening ISE Bad baseline + adaptation OFF38,45 %6,2 Bad baseline + adaptation ON50,42%0,76 Good baseline + adaptation ON53,23%0,64 Large is good Small is good

35 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Take home message Our 2-layered anti-slug control system works very well in practice The interaction between the two layers create a very nice synergy: Setpoint changes triggered by the supervisor makes the adaptation work well A well functioning adaptive control makes it possible to safely operate at large valve openings, thus maximizing production

36 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Take home message Expected benefits Stable and safe operation in a wide range of conditions Reduced need for control tuning Reduced workload on operators Increased production

37 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Thank you for your attention ` “A robust adaptive control system is key for reliable autonomous operation”

38 Vinicius de Oliveira | an intelligent adaptive anti-slug control system for production maximization Olga simulation `