© ABB Group June 20, 2012 | Slide 1

Animp Ati

Advanced control and optimization of a Complex Combined Cycle Plant

© ABB Group June 20, 2012 | Slide 2

Presentation Agenda

� Advanced Process Control: brief introduction

� Advanced Process Control at an IGCC unit

� Brief process introduction

� Implementation Results

� Syngas balancing

� MW output control

� Gasifiers conversion control

© ABB Group June 20, 2012 | Slide 3

APC Technology: brief introduction

© ABB Group June 20, 2012 | Slide 4

Introduction to APC - Automation Hierarchy

OPTIMIZATION

ADVANCED PROCESS CONTROL

ADVANCED REGULATORY CONTROL

BASIC CONTROL

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How APC Improves Performance (1)Time domain view

actual constraint

before MPC

Reduce

Variance

Shift

Target

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How to improve control performance

� Use process models � Coordinated unit control instead of local control� Continuous control execution vs. human direction of local

setpoints� Multiple objectives, priority hierarchy

© ABB Group June 20, 2012 | Slide 7

Introduction to APC Technology

� Use of ABB Multivariable Controller OptimizeIT Predict & Control

� Predict & Control is an Observer Based Model Predictive Controller

� It uses State-Space technology to describe models and control process

� Any model is defined by a discrete time state-space process model description:

� x(k)=Ax(k-1)+B uu(k-1)+B dd(k-1)+w(k-1)

� y(k)=Cx(k) + h(k)

A, B, C=dynamics, x=state vector, u=controller outp ut, d=feedforward, w=process noise, y=measured process variable, h=measurement noise

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Introduction to MPC – The Models (2)

� State space tech is transparent to the user

� Graphical representation from the model identification tool of Predict and Control, how each MV (column) affects each PV (row)

� Multiple alternative models are presented to compare dynamics and steady state gain

Classical vs. Modern Formulation of MPCClassical formulation

�Simple Model identification

�Model with many parameters

�Need workaround to handle integrals (e.g. level control)

�Only Exit disturbance

Modern Formulation

�Well known theory (Kalman Filter)

�Simple model representation

�Easy model for integral process (level control)

�Input and output disturbance

© ABB Group June 20, 2012 | Slide 9

�Source: http://en.wikipedia.org/wiki/Model_predictive_control

http://www.abb.com/industries/seitp410/f908918ee650587cc1257027006c2dc4.aspx?productLanguage=us&country=US

http://www.aspentech.com/publication_files/he_jan2006_state_space_controller.pdf

© ABB Group June 20, 2012 | Slide 10

Implementation Results

TAR

EE tot

Syngas

Post Firing1

P

GT1

TV1

Expander

Gasifier1

HRSG1

MASTER CONTROLLER

TV2

HRSG2

GT2

Post Firing2

Gasifier2

TAR

�Honor Process constraints (e.g. max temp SH, feed pump amperes, etc.)

�Keep CH4 content in gasifiers outlet at a setpoint

�Keep Syngas pressure in range at Syngas HP header

�Keep Total MW at a setpoint or, if not possible, maximize MW

�Maximize GT contribution vs. Postfiring

�Keep power integral over 15 mins horizon

�Avoid GT saturation issues – sudden drop of temperature

MASTER CONTROLLER OBJECTIVES

Syngas pressure

Pressure is minimized as Syngas production is limited – riding on the lo constraint allows for a slight increase in power export

MASTER CONTROLLER – RESULTS (1/3)

APC ON

MASTER CONTROLLER RESULTS (2/3)

Net MW export

Syngas pressure

APC ONAPC OFFAPC ON

PV – Blue

Limits - Red

MASTER CONTROLLER RESULTS (3/2)

APC OFF 528.3 MW

APC ON 531.0 MW

∆∆∆∆ Power + 2.7 MW

MASTER RESULTS – MW PRODUCTION RAMP

MW: 528.5 to 531.7

∆= 3.2 MW

APC ONAPC OFFPlant startup

MASTER CONTROLLER DISPLAYS (1/2)

MASTER CONTROLLER DCS (2/2)

Methane control - over one week

CH4 Control APC ON

GASIFIERS – CH4 CONTROL CH4 (1/4)

GASIFIERS – CH4 COMPOSITION CONTROL (2/4)

Gasifier Methane control - over 5 days

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Integration with DCS

OPC Server

Firewall

APC I

Runtime

Client – Remote Desktop

DCS

DCS NET

APC LAN

Optional

VPN Remote Access

APC INTEGRATION

DCS Consoles

APC II

Offline

APC Scope

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