Modeling, Control, Simulation and Diagnosis of Complex Industrial and Energy Systems

Modeling, Control, Simulation and Diagnosis of Complex Industrial and Energy Systems

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Modeling, Control, Simulation and Diagnosis of Complex Industrial and Energy Systems

This book focuses on the modeling, control, simulation, and diagnosis of complex industrial systems. Emphasis is placed on the real-time monitoring and control of process plants and energy systems and on the application of innovative approaches ranging from the predictive control of a gasoline engine, through fuzzy inference applied to quality control in the paper industry and up to innovative load shedding and demand management in national electrical grids.

This book will be of interest to practitioners within the automation field, particularly those focused on process control and energy systems. It will also be of interest to academics and seeking an overview of current approaches in this field or looking for detailed treatment of any of the issues covered by the individual chapters. More than forty authors from countries around the world have contributed to the production of this unique book.

Hard copy, 292 pages.

Table of Contents

List of Figures

List of Tables

 

1 Remote Supervision Center for Enel Combined Cycle Plants

Introduction

Location of the center

Architecture

Functions

Performance control

Heat rate evaluation

Maximum power forecast

Plant status and status monitor

Plant start-up: technical and economical evaluation

Power unbalance calculation

Diagnostics

Automatic reporting

Heat rate losses

Start-up evaluation

Energy unbalance

Gas turbine output temperatures and humming and acceleration phenomena

Gas turbine compressor filters status

Computerized events register

Acknowledgments

References

 

2 Pickling Line Modeling for Advanced Process Monitoring and Automation

Introduction

Pickling of carbon steel

Pickling of stainless steel

Management and control of pickling processes

Advances in pickling line automation

Architecture of control software

Pickling lines components and configuration

Main components of pickling lines

Pickling lines configuration

Electrolytic pickling lines

Pickling line model

Equations describing the recirculation tank

Equations describing the working tank

The pickling model

Electrolytic pickling model

Additional notes on the pickling line model

Model implementation

Conclusion

Acknowledgments

References

 

3 Modeling, Simulation and Predictive Control of a Gasoline Engine

Introduction

Mean value engine model

Air supply system

Engine

Vehicle model

Validation

Control design

Design of a static regulator

Model of the driver

Design of a dynamic controller with MPC

Simulation results

Conclusion

Acknowledgments

References

 

4 Dynamic Principal Component Analysis Applied to the Monitoring of a Diesel Hydrotreating Unit

Introduction

Hydrotreating Unit Model

Hydrotreating (HDT) unit

HDT unit modeling

Principal Components Analysis (PCA)

Monitoring system: development and results

Operational conditions

DPCA: definition of the number of delays

DPCA: training

A hybrid procedure combining DPCA and classification

Results: validation and test

Conclusion

Acknowledgments

References

 

5 A Simulation Study of the Flue Gas Path Control System in a Coal-Fired Power Plant

Introduction

The plant model

Structure of the plant

Unit modeling

The control system model

General remarks

Control system architecture

Continuous-time controllers

Logic controllers

Improvement of the control strategy

Improvement of the critical logic control behavior

Selected simulation results

Load dispatching

Transition from FGD inserted to FGD bypassed

Conclusion

References

 

6 Automatic Diagnosis of Valve Stiction by Means of a Qualitative Shape Analysis Technique

Introduction

Valve stiction

Automatic detection of stiction

Techniques based on PV-OP—brief review

Techniques based on qualitative description formalism

The Yamashita stiction detection technique

Application on simulated data

Noise-free data

Adding noise

Varying setpoints

First conclusions about the technique

Application to plant data

Results

Sampling time

Observation window

Noise level

Other phenomena observed in the plant data

Conclusion

References

 

7 Monitoring and Controlling Processes with Complex Dynamics Using Soft Sensors

Introduction

Case study 1: freeze-drying of pharmaceuticals

Detailed and simplified models

Observers design

Feedback temperature control

Case study 2: catalytic combustion of lean mixtures

Case study 3: SCR unit for NOx 154

Conclusions

Acknowledgments

Nomenclature

References

 

8 Estimation of a Ternary Distillation Column via a Tailored Data Assimilation Mechanism

Introduction

Estimation problem

Data assimilation mechanism

Estimation design

The Non-linear Geometric Estimator (NGE)

The Extended Kalman Filter (EKF) with reduced data injection

Conclusion

References

Table of Contents xiii

 

9 A Prediction Error-Based Method for the Performance Monitoring of Model Predictive Controllers

Introduction

Problem statement

Process, model, and state estimator

Steady-state target calculation

Dynamic optimization

Method

Preliminary definitions of prediction error

Motivating example

Prediction error-based diagnosis

Case studies

Extensive simulations

An industrial example

Conclusion

Acknowledgments

References

 

10 An Intelligent/Smart Framework for Real-Time Process Monitoring and Supervision

Introduction

Integrated framework

Trend analysis and preprocessing

Outlier detection

Noise reduction

Fault detection and identification

Self-Organizing, Self-Clustering Network (SOSCN)

Case study

Conclusion

 

11 Quality Monitoring Through a Dynamic Neural Software Sensor

Introduction

Background

Problem statement

The process

Software sensor

Software sensor design

Basic structure

Neural software sensor formulation

Industrial application

Data acquisition

Input selection

Results and discussion

Conclusion

References

 

12 Wind Generation and Flexible Electric Load Management Issues for System Operation in Crete

Introduction

Green Electricity Availability Barometer Service (GEA BASE)

A Control Center tool

Demand nodes model

Generation nodes model

Implementation

Formulation of the knowledge base

Inference derivation process

Architecture of an expert system

Incorporation of the GEA BASE tool into GIS and digital database for the Crete Power System

Conclusion

References

 

13 A Fuzzy Inference System Applied to Quality Control in the Paper Industry

Introduction

Problem description

Experimental setup

The quality control system

The image processing phase

Defect detection through a clustering algorithm

Defect evaluation through a fuzzy inference system

Numerical results

Conclusion and future work

References

 

14 Innovative Load Shedding and Demand Side Management Enhancements to Improve the Security of a National Electrical System

Introduction

Demand side management and demand response services

Automatic meter reading system and enhancement required by Demand Response (DR) services

Potential vulnerability of communication technologies for demand control services

Current activity in CESI RICERCA

Conclusion

Acknowledgments

References

Index



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