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    Process

    Plant Operational Excellence

    through Advanced Process Control

    By

    Michael Schaffer, Robert Sloan,

    Glenn Dobby and Andrea Festa

    Adv anced Process Contro l Area

    MinnovEX Technologies Inc.

    Toronto, Canada

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    Agenda

    Definitions Where are we today

    Data

    Technology

    Integration

    Case Studies Grinding

    Flotation

    Dewatering

    Where are we going

    Short term Long term

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    Definitions

    Operational Excellence : the consistent application ofoperational best practice leading to increased quality coupled

    with improved performance.

    Advanced Process Control :

    Advanced Control

    Strategic as opposed to tactical

    Concept development of a Global

    model (Theoretical, Phenomenological,

    Empirical, Stochastic, Heuristic)

    Field Instrumentation

    PLCs/Controllers

    DCS / SCADA

    MIS

    APC

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    Introduction

    In 1995 APC in the mineral processing industry moved from the

    development phase into a practical industrial solution.

    Now, in 2004 we can confidently state that advanced process

    control has reached a mainstream level, achieving over 99%

    availability with greater than 95% utilization and installed in

    many of the major operating facilities in the world.

    This success is due not only to the advances at the APC levelbut even more importantly at the regulatory, network and

    hardware levels.

    With the addition of new instrumentation, operations has been

    able to move from a qualitative to a quantitative environment

    facilitating a better understanding of the circuit state.Additionally, data driven and fundamental models have been

    successfully applied as soft sensors and predictors.

    The next step will be the morphing of these models into true

    controllers.

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    Introduction (cont.)

    The South American industry has positioned itself at the

    vanguard of this control revolution. Recognising the potential

    benefits and while managing risk, testing and implementing the

    solutions.

    Each advanced control solution has 3 components:

    Data

    Technology

    Integration

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    The Data

    The data us allows to make informed decisions. The data can be

    measured directly or inferred.

    Direct measurements are always better. Measurement instruments

    are evolving fast enough to bring the industry closer to a fully defined

    system and causing competition at a price level.

    Examples: froth vision systems and cyclone overflow particle sizeanalysers.

    Some characteristics of the operation on-line cannot be measured

    and it is necessary to use average or typical values that will be

    corrected through a sophisticated error model or filter. For example

    a Neural Network model.

    Research continues at both the micro and macro level, developing

    and testing several techniques.

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    The DataExamples of these technologies :

    Froth Vision Systems :

    Originally : fibre optics, CCD cameras, separatelighting : USD20,000/camera + lights

    Now : Wireless or Ethernet camera withincorporated lighting : USD4500/camera includinglights

    Particle Size :

    Originally : Physical contact : USD120,000

    Now : Camera based, full distribution : USD40,000

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    The Data

    Many key characteristics of the process can still not be measuredon-line resulting in compromises.

    An example being ore hardness in a mill.

    At present the industry adopts average or typical values in themodels with variability manifesting itself as an unmeasureddisturbance that is corrected or filtered using an error model orfilter (I.e. Kalmann filter or a Neural Network model).

    300 m

    800 m

    0 200 SPI

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    The Data As the systems are better defined more models can be leveraged and

    their extensibility exploited. Until that time many of the current systems (both human and

    automated) rely on inferred data and this explains the success of

    expert system technology that leverages a heuristic model.

    Inferred data represent variables that are not directly measured but arerather indicated by some secondary or tertiary means. These

    parameters are then predicted using various models some moresophisticated than others.

    An example is load in a mill currently inferred by either bearing

    pressure or weight (through load cells)

    In summary it is essential that the type and quality of the data used tomake control decisions must be carefully considered. A well-designed solution actively participates in ensuring that the basis foractions are well supported

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    The Technology

    Selection of the appropriate technology is critical.This applies both to the decision of whichfundamental approach to adopt as well as whichproduct to purchase.

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    The Technology - Platform

    The selection of a platform is critical whenconsidering the long-term sustainability and

    maintainability of the advanced control solution.

    Special consideration must be given to the

    resources available to administer the system. The operation must avoid a solution that

    becomes obsolete without a clear path to

    modernization such as custom-coded systems.

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    The Technology - Platform

    The selection of the platform has as much to do with the concept

    of commercial and technical risk as it does with functionality.

    Clients must consider the long-term sustainability of the systems

    due to the fast pace of progress within the Advanced Control field.

    Many offerings have come and gone over the years it is difficult

    to build a strong business model focusing on a single industry. Long term success has so far been the domain of vendors that

    build multi industry platforms and count on integrators to

    customise for a specific applications.

    Such a broad based strategy ensures sufficient cash flow tosupport the research and development programs necessary to

    meet the demands of industry and fully exploit the capabilities of

    the systems as they become available.

    One example is Gensym which still remains at the forefront of

    this field after 16 years building expert system platforms.

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    The Technology - Platform Advanced Process Control solutions are a unique blend of control,

    process and operations.

    Users accessibility, intuitive diagnostic tools, self-documentation

    and recording features are critical to this solutions as they grow

    with the operation.

    The ability and ease of interfacing with external environments isalso an important consideration

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    Integration

    Integration is considered the most important component in the

    successful deployment of an advanced process control

    solution.

    Regardless of the strength of the data or the technology being

    deployed, if the package is not configured and tuned properly

    and the solution is not accepted into the culture of theorganization it will fail.

    Advanced process control empowers the operator to optimize

    the plant.

    Each implementation offers its own challenges and must be

    considered on its own merits. Success can be accomplished

    with the appropriate methodology and education program.

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    Comminution circuits

    MET Solution Case Studies

    SAG Mill Expert Systems

    Advanced algorithms have been implemented where the expertsystem monitors the relationship between power and pressure

    pushing the operation to the sweet spot by considering current,

    past and future performance. Through logic the system self

    regulates, adapting to changes in ore type and properties.

    Ball Mill Expert Systems

    Neural networks are a robust, data driven modeling technique thatleverages real-time plant data to continuously test and evaluate

    itself, Neural nets are deployed to model the cyclone and predict itsperformance on a real-time basis.

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    Comminution circuits

    A few considerations when using Neural networks are:

    Re-training: to ensure that they represent the current

    situation

    Data preprocessing to make informed decisions

    Extrapolation is not recommended

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    Froth flotation

    MET Solution Case Studies

    Froth Flotation Vision Systems

    New vision-based technology for flotation cells coupled withheuristic flotation expert systems has permitted higher levels ofautomation and control.

    The solution METCAM FS have simplified the application of the

    technology through innovations such as integrated light andcamera systems and wireless options eliminating one of thehigher cost components of an installation.

    Parameters monitored:

    Froth Velocity (most important)

    Bubble size

    Froth stability

    Color

    Froth typing

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    Froth flotation

    MET Solution Case Studies

    MET Flotation Stabilizer (METFS)

    It is a flow-surge flotation expert system.

    It is currently employed at facilities in Chile and Argentina to

    eliminate the need to manually deal with flow surges by

    anticipating the onset of a surge before it actually arrives at

    the circuit. Once a surge is detected, METFS moves the supervisory

    control to standby and aggressively handles the surge by

    controlling the instrumentation related.

    METFS will return the control to the supervisory control once it

    is satisfied that the surge is under control.

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    Froth flotation

    MET Solution Case Studies

    Circulating Loads and the MET Solution - Cleaner Circuit

    The METCLEANER advisory expert system for a producer in Chileanticipates and controls both internal and external circulating

    loads of a cleaner flotation circuit.

    Once circulating loads are controlled the cleaner circuit can

    achieve maximum performance for that given circuit and feed.

    METCLEANER consists of soft sensors that will warn the operators

    and suggest corrective actions for maintaining a stable cleaner

    circuit .

    METCLEANERhas been implemented without the use of cameras on

    the first cleaner and cleaner scavengers however we believe it willbe advantageous to install cameras for a better control.

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    Froth flotation

    MET Solution Case Studies

    Open Channel Flow Measurements and the MET Solution

    Open channel flow measurement, consists of a relatively

    inexpensive ultrasonic level sensor and provides a continuousfeedback of the concentrate production. This method captures the

    entire cell or row output so it is very reliable .

    The open channel flow measurement of concentrate slurry has

    been incorporated into the logic for the control of rougher flotation

    at an Argentina operation.

    The increased response speed and consistency of the expert has

    improved stability in the rougher concentrate production in this

    operation.

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    Froth flotation

    MET Solution Case Studies

    Rule for controlling Air and Level

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    Dewatering Facilities

    The expert system monitors the plant process water tank levels and

    thickener overflow capture pond levels.

    The expert system automatically starts / stops "tranque" waterpumps to maintain adequate levels in the plant. Also, the expert

    system can avoid overflows of the thickener capture ponds that

    would otherwise lead to the loss of valuable process water.

    Similar applications include CCDs which are increasing in

    prominence.

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    Mini Expert Systems

    Once the expert system platform has been purchased andinstalled, and an expert system for one area of the plant has beenintegrated and tested, further sub-modules can be developed

    and run from the same platform.

    These are opportunities where a new expert system would be

    difficult to justify but applications that can clearly benefit frommore intelligent control.

    For example a water consumption expert system has been

    expanded to control freshwater valves to the concentrator relative

    to KPIs for freshwater consumption (per tonne processed). This

    aids the site by avoiding excessive water consumption.

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    A Look at the Near Future

    Increased use of models with more, better and

    reliable measurements of process variables for

    soft sensing and prediction.

    Increasingly holistic approaches considering

    multi unit interactions and global optimisation

    through the operation.

    Increasing capabilities to understand rootcauses and address the problem source.

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    Further Out

    Conversion of the models into

    controllers.

    Incorporation of economic objectivefunctions becomes possible optimising

    the business drivers in the operation and

    using those to define the process

    requirements.

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    THE END