Advanced control and supervision of mineral processing plants

Advances in Industrial Control

Other titles published in this series:

Digital Controller Implementation

and Fragility

Robert S.H. Istepanian and James F.

Whidborne (Eds.)

Optimisation of Industrial Processes

at Supervisory Level

Doris Sáez, Aldo Cipriano and Andrzej W.

Ordys

Robust Control of Diesel Ship Propulsion

Nikolaos Xiros

Hydraulic Servo-systems

Mohieddine Jelali and Andreas Kroll

Model-based Fault Diagnosis in Dynamic

Systems Using Identification Techniques

Silvio Simani, Cesare Fantuzzi and Ron J.

Patton

Strategies for Feedback Linearisation

Freddy Garces, Victor M. Becerra,

Chandrasekhar Kambhampati and

Kevin Warwick

Robust Autonomous Guidance

Alberto Isidori, Lorenzo Marconi and

Andrea Serrani

Dynamic Modelling of Gas Turbines

Gennady G. Kulikov and Haydn A.

Thompson (Eds.)

Control of Fuel Cell Power Systems

Jay T. Pukrushpan, Anna G. Stefanopoulou

and Huei Peng

Fuzzy Logic, Identification and Predictive

Control

Jairo Espinosa, Joos Vandewalle and

Vincent Wertz

Optimal Real-time Control of Sewer

Networks

Magdalene Marinaki and Markos

Papageorgiou

Process Modelling for Control

Benoît Codrons

Computational Intelligence in Time Series

Forecasting

Ajoy K. Palit and Dobrivoje Popovic

Modelling and Control of Mini-Flying

Machines

Pedro Castillo, Rogelio Lozano and

Alejandro Dzul

Ship Motion Control

Tristan Perez

Hard Disk Drive Servo Systems (2nd Ed.)

Ben M. Chen, Tong H. Lee, Kemao Peng

and Venkatakrishnan Venkataramanan

Measurement, Control, and

Communication Using IEEE 1588

John C. Eidson

Piezoelectric Transducers for Vibration

Control and Damping

S.O. Reza Moheimani and Andrew J.

Fleming

Manufacturing Systems Control Design

Stjepan Bogdan, Frank L. Lewis, Zdenko

Kovačić and José Mireles Jr.

Windup in Control

Peter Hippe

Nonlinear H2/H∞ Constrained Feedback

Control

Murad Abu-Khalaf, Jie Huang and

Frank L. Lewis

Practical Grey-box Process Identification

Torsten Bohlin

Control of Traffic Systems in Buildings

Sandor Markon, Hajime Kita, Hiroshi Kise

and Thomas Bartz-Beielstein

Wind Turbine Control Systems

Fernando D. Bianchi, Hernán De Battista

and Ricardo J. Mantz

Advanced Fuzzy Logic Technologies in

Industrial Applications

Ying Bai, Hanqi Zhuang and Dali Wang

(Eds.)

Practical PID Control

Antonio Visioli

(continued after Index)

Daniel Sbárbaro · René del Villar

Editors

Advanced Control

and Supervision of Mineral

Processing Plants

123

Daniel Sbárbaro, Prof.

Universidad de Concepción

Departamento de Ingeniería Eléctrica

Casilla 160-C, Correo 3

Concepción

Chile

[email protected]

René del Villar, Prof.

Université Laval

Département de Génie des Mines,

de la Métallurgie et des Matériaux

Pavillon Adrien-Pouliot 1065

venue de la Médecine

Québec, QC G1V 0A6

Canada

[email protected]

ISSN 1430-9491

ISBN 978-1-84996-105-9 e-ISBN 978-1-84996-106-6

DOI 10.1007/978-1-84996-106-6

Springer London Dordrecht Heidelberg New York

British Library Cataloguing in Publication Data

A catalogue record for this book is available from the British Library

Library of Congress Control Number: 2010932025

© Springer-Verlag London Limited 2010

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as

permitted under the Copyright, Designs and Patents Act 1988, this publication may only be re￾produced, stored or transmitted, in any form or by any means, with the prior permission in writing of

the publishers, or in the case of reprographic reproduction in accordance with the terms of licences

issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms

should be sent to the publishers.

The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of

a specific statement, that such names are exempt from the relevant laws and regulations and therefore

free for general use.

The publisher makes no representation, express or implied, with regard to the accuracy of the infor￾mation contained in this book and cannot accept any legal responsibility or liability for any errors or

omissions that may be made.

Cover design: eStudioCalamar, Figueres/Berlin

Printed on acid-free paper

Springer is part of Springer Science+Business Media (www.springer.com)

A

Advances in Industrial Control

Series Editors

Professor Michael J. Grimble, Professor of Industrial Systems and Director

Professor Michael A. Johnson, Professor (Emeritus) of Control Systems and Deputy Director

Industrial Control Centre

Department of Electronic and Electrical Engineering

University of Strathclyde

Graham Hills Building

50 George Street

Glasgow G1 1QE

United Kingdom

Series Advisory Board

Professor E.F. Camacho

Escuela Superior de Ingenieros

Universidad de Sevilla

Camino de los Descubrimientos s/n

41092 Sevilla

Spain

Professor S. Engell

Lehrstuhl für Anlagensteuerungstechnik

Fachbereich Chemietechnik

Universität Dortmund

44221 Dortmund

Germany

Professor G. Goodwin

Department of Electrical and Computer Engineering

The University of Newcastle

Callaghan

NSW 2308

Australia

Professor T.J. Harris

Department of Chemical Engineering

Queen’s University

Kingston, Ontario

K7L 3N6

Canada

Professor T.H. Lee

Department of Electrical and Computer Engineering

National University of Singapore

4 Engineering Drive 3

Singapore 117576

Professor (Emeritus) O.P. Malik

Department of Electrical and Computer Engineering

University of Calgary

2500, University Drive, NW

Calgary, Alberta

T2N 1N4

Canada

Professor K.-F. Man

Electronic Engineering Department

City University of Hong Kong

Tat Chee Avenue

Kowloon

Hong Kong

Professor G. Olsson

Department of Industrial Electrical Engineering and Automation

Lund Institute of Technology

Box 118

S-221 00 Lund

Sweden

Professor A. Ray

Department of Mechanical Engineering

Pennsylvania State University

0329 Reber Building

University Park

PA 16802

USA

Professor D.E. Seborg

Chemical Engineering

3335 Engineering II

University of California Santa Barbara

Santa Barbara

CA 93106

USA

Doctor K.K. Tan

Department of Electrical and Computer Engineering

National University of Singapore

4 Engineering Drive 3

Singapore 117576

Professor I. Yamamoto

Department of Mechanical Systems and Environmental Engineering

The University of Kitakyushu

Faculty of Environmental Engineering

1-1, Hibikino,Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135

Japan

vii

Series Editors’ Foreword

The series Advances in Industrial Control aims to report and encourage

technology transfer in control engineering. The rapid development of control

technology has an impact on all areas of the control discipline. New theory, new

controllers, actuators, sensors, new industrial processes, computer methods, new

applications, new philosophies…, new challenges. Much of this development

work resides in industrial reports, feasibility study papers and the reports of

advanced collaborative projects. The series offers an opportunity for researchers

to present an extended exposition of such new work in all aspects of industrial

control for wider and rapid dissemination.

In control conferences around the globe, there is much activity today on all

aspects of control in networks; computer networks, communication networks,

wireless networks, sensor networks, and so on. Another highly visible topic is

control in the field of vehicles; automobile control, engine control, control of

autonomous vehicles on land, at sea and in the air, and cooperative control and

formation control of convoys, fleets and swarms of autonomous vehicles are all

areas of current interest. Although such topics are exciting state-of-the-art

technological developments, it is too easy to overlook the continuing need for

control engineering to optimize or at least to improve the processes of heavy or

primary industries. Some of these industries, like energy production and

petroleum refining, are quite mature users of advanced control technology, but

others in this economically important category have only recently automated their

process units and are now ready to exploit the additional benefits that more

advanced control systems can bring. A case in point is the mining and mineral

processing industry. Automation of this industry’s processes is widespread but

the control used is simple, open-loop, or closed-loop using rule-based and

classical PID control. The process lines are large-scale and mechanically

vigorous, so the control emphasis is on achieving continuous operation (with as

little downtime as possible) and efficient extraction performance. However, some

control engineers are now beginning to realize that it is possible to do better, and

when the end product is a commodity like gold, copper, zinc, cobalt or another

valuable mineral, it is easy to see that enhanced extraction performance quickly

brings an economic return but there are some important communication and

training barriers to overcome:

viii Series Editors’ Foreword

• a need for a general awareness in the industry of the benefits that

more advanced control systems can bring;

• a need for engineering specialists in the industry to be conversant

with control concepts and ideas; and

• a reciprocal need for engineers, researchers and academics from the

control community and related research groupings to understand the

processes, the technology, the process objectives, and the technical

language of the mining and mineral processing industries.

This Advances in Industrial Control monograph, Advanced Control and

Supervision of Mineral Processing Plants, edited by Professors René del Villar

(Canada) and Daniel Sbárbaro (Chile) should be seminal in initiating the dialogue

needed to understand the measurement and control of processes in the industry.

The monograph covers the types of process variables to be measured, novel

measurement methods, the status of industrial control products currently used, the

objectives of control and what the control community can contribute to enhance

the performance of mineral processing units. The Editors (who also contribute to

the book as authors) have brought together a strong international authorial team

who illuminate vividly the engineering needed for these control system

developments.

The monograph comprises seven chapters. The opening Chapter 1 gives the

reader an introductory overview of the general process structure for mineral

processing, namely: mining, size reduction, classification, concentration, and

final product handling leading to transportation or smelting. The next three

chapters offer a thorough look at the process parameters and the process qualities

that can be measured, or that the industry would like to measure. Three novel

state-of-the-art technologies for measurement are described: models and data

reconciliation, image processing from machine vision, and soft sensors.

Measurement is followed by process simulation, and in Chapter 5 a MATLAB®-

based library of simulation modules for the units and equipment of the mineral

processing line is described. This chapter also has case study examples showing

how the library is used. While the case study of Chapter 5 is a grinding circuit,

that used in Chapter 6 for an in-depth control study is a flotation column. In this

chapter there is an interesting table showing 18 previously-published control

solutions for one aspect of the column control. The methods used were as

follows: PID (7), MPC (5), fuzzy logic (3), expert system (2) and nonlinear model

reference (1), but the table also shows that the industrial controllers used in

practice were either an expert system, fuzzy logic or PID controller. The final

chapter surveys current industrial control products used in the mineral processing

industry and these are mainly general process industry control packages used on

mineral processing applications with just a few products specifically developed

for the industry.

The Editors and contributing authors are to be congratulated on producing

such a useful contribution to the industrial control literature. The monograph will

be required reading for any control engineer working in the mineral processing

industry. The chapters are self-contained – state-of-the-art but with good survey

and historical perspective sections. Other readers likely to enjoy and benefit from

this book include mineral processing engineering specialists, related process

Series Editor’s Foreword ix

communities, especially those seeking new and challenging topics to apply

advanced measurement and control methods will also find the monograph an

inspirational read. This is an exemplary addition to the Advances in Industrial

Control series.

Industrial Control Centre M.J. Grimble

Glasgow M.A. Johnson

Scotland, UK

2009

industry engineers and industrial control system engineers. Academics,

researchers and research students working in the control and process control

Preface

In 2008 the idea of writing a book on issues of automatic control of mineral pro￾cesses was suggested by Professor Michael Johnson and discussed between three

Chilean academics working in this field, each belonging to a different University

(Daniel Sbarbaro from Universidad de Concepci´on, Guillermo Gonzalez from Uni￾versidad de Chile and Aldo Cipriano from Pontificia Universidad Cat´olica de Chile).

During the 12th IFAC International Symposium on automation in Mining, Mineral

and Metal Processing organized by Universit´e Laval in Qu´ebec City (August 2007)

the decision to integrate this automation group of researchers was adopted. As such,

four new professors, Daniel Hodouin, Ren´e del Villar, Andr´e Desbiens and Carl

Duchesne, each contributing with his particular field of expertise, joined the en￾deavor.

This book provides an up-to-date review of modern automation developments at

industrial level, highlighting the main role played by modeling, control theory and

measuring systems in the design and operation of effective supervisory strategies

for mineral processing processes. It describes how to use dynamic models of major

equipments in the design of automatic control, data reconciliation and soft sensing

schemes. Through examples, it illustrates modern design tools for integrating simu￾lation and control system design for comminution (crushing, grinding) circuits and

flotation columns. It fully covers the design of soft sensors based on standard sin￾gle point measurements and those based on more complex measurements, such as

digital images. It surveys the main issues concerning steady-state and dynamic data

reconciliation and their application to the design of instrumentation architectures

and fault diagnosis systems for the mineral processing processes. Considering that

most mineral processing plants have distributed control systems and information

management systems, this book also describes the current platforms and toolkits

available for implementing these advanced data processing and control systems.

Some applications to real mineral processing plants or laboratory/pilot scale set-ups

highlight the benefits obtained with the techniques described in the book.

The book will benefit engineers working in the mineral processing industry by

providing valuable tools and information about the use of modern software plat￾forms and techniques. It is also of interest to senior students of chemical, metallur￾xi

xii Preface

gical and electrical engineering looking for applications of control technology to

mineral processing plants. Control engineers and academics will find the industrial

application areas for the new control techniques of general interest.

The editors would like to thank the book’s contributors for the work accom￾plished, and the numerous students that, at each extreme of the American continent,

have contributed to the results presented by their professors (naming all of them

would be almost impossible). A special word of thanks is addressed to Prof. Michael

Johnson and Aislinn Bunning from Springer, whose encouragement and technical

support was instrumental to the conclusion of the book.

Qu´ebec and Concepci´on, Rene del Villar ´

July 2009 Daniel Sbarbaro

Contents

List of Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii

1 Introduction ................................................ 1

Daniel Sbarbaro and Ren´e del Villar

1.1 Introduction . ............................................. 1

1.2 A Concentration Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.1 Crushing Circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.2 Grinding Circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.3 Separation Circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.4 Dewatering Circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.3 Main Functions of Automation Systems in Mineral Processing

Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3.1 Basic Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3.2 Advanced Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4 Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.5 Historical Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.6 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2 Process Observers and Data Reconciliation Using Mass and Energy

Balance Equations........................................... 15

Daniel Hodouin

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.2 Process Variables and Operating Regimes . . . . . . . . . . . . . . . . . . . . . 20

2.3 Models and Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.3.1 The Dynamic Linear Mass Balance Equation . . . . . . . . . . 23

2.3.2 The Linear Stationary and Steady-state Cases . . . . . . . . . . 24

2.3.3 The Bilinear Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.3.4 Multi-linear Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.3.5 Additional Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.3.6 Summary of Stationary Conservation Equations . . . . . . . 28

xiii

xiv Contents

2.4 Sensors, Measurement Errors and Observation Equations . . . . . . . . 28

2.4.1 Statistical Properties of Measurements and

Measurement Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.4.2 Measurement Errors for Particulate Materials . . . . . . . . . . 30

2.5 Observation Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.6 General Principles of Stationary and Steady-state Data

Reconciliation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.6.1 Observability and Redundancy . . . . . . . . . . . . . . . . . . . . . . 35

2.6.2 General Principles for State Estimate Calculation . . . . . . . 38

2.7 The Linear Cases: Steady-state, Stationary and Node Imbalance

Data Reconciliation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.7.1 The Steady-state Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.7.2 The Stationary Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.7.3 The Node Imbalance and Two-step Methods for

Bilinear Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

2.8 The Non-linear Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

2.8.1 An Example of Substitution Methods: Mass and Heat

Balance of a Thermal Exchanger . . . . . . . . . . . . . . . . . . . . . 47

2.8.2 An Example of Hierarchical Methods: BILMAT™

Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

2.9 Performance of Data Reconciliation Methods . . . . . . . . . . . . . . . . . . 52

2.10 An Overview of Dynamic Reconciliation Methods . . . . . . . . . . . . . 55

2.10.1 Phenomenological Causal Models . . . . . . . . . . . . . . . . . . . . 57

2.10.2 Empirical Causal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

2.10.3 Sub-models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

2.10.4 Reconciliation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

2.10.5 An Example of Dynamic Reconciliation for a

Simulated Flotation Circuit . . . . . . . . . . . . . . . . . . . . . . . . . 64

2.11 Instrumentation Strategy Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

2.12 Fault Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

2.13 Coupling Data Reconciliation with Process Control and

Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

2.14 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

3 Multivariate Image Analysis in Mineral Processing ............... 85

Carl Duchesne

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

3.2 Background on Latent Variable Methods . . . . . . . . . . . . . . . . . . . . . . 88

3.2.1 Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . 88

3.2.2 Projection to Latent Structures (PLS) . . . . . . . . . . . . . . . . . 89

3.2.3 Statistics and Diagnostic Tools Used With Latent

Variable Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

3.3 Nature of Multivariate Digital Images . . . . . . . . . . . . . . . . . . . . . . . . 92