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INTELIGENCIA OPERACIONAL

ANALISIS MULTIVARIABLE Y

MONITOREO DINAMICO DEL PROCESO DEMOLIENDA

Jose Sanzana, Luis Yacher

CONTAC INGENIEROS LTDA.

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Large number of process variables

Complex cause-effect relationships

High co-linearity and redundancy

Different (time based) Operational Modes:drift, noise, start-up, set point changes,

disturbances, etc.

Modern Plants have large data

bases for Real Time and Historical

information about process variables

and equipment vital signs.

How to proactively use the hidden knowledge that exists in the plant or

equipment historical information?

How to extract the behavioral patterns from the empirical data?

Why Multivariate Analysis?

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Why multivariate patterns?, a simple example for a centrifugal pump

in the figure it is shown a plot of different operating points and the yellow lines showing upper andlower limits for Flow and Pressure.

flow is OK

pressure is OK

BUT Flow vs. Pressure deviatesfrom the pattern,

something is wrong with this pump,an impeller problem?, a driveproblem?, etc.

99

8.......

.

.

. ...

flow

pressure For the circled point:

Most probably, the failure will be later reflected in a deviation from the pressure or flow limits, if so, thepattern analysis gives an early alert of the problem, or.

The problem will be never detected, until it is too late ???

Individual variables are OK, but the multivariable relationship is signaling a deviation from thedesired pattern.

Relevant information is embedded in the relationships between the process variables or equipmentvital signs.


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In this example, a XY plot is enough to visualize an outlier, but:

What if a process or machine has 15 relevant variables or vital

signs?

Moreover, are the 15 variables independent?, also: what is the

relative influence of each of them?

What if there are many processes or machines to supervise?

Multivariate pattern models reducethe number of relevant variables, by the identification of the

independent (orthogonal) factors that represents a certain behavior. This reduction means that the

supervision can be made using much fewer variables for each process or machine.

Characterization indexes can be defined for each pattern, real time calculation of those indexes gives an

overall measurement of the equipment (or process) perfomance.

Patterns can be defined depending on the GOAL, being: a quality pattern, a throughput pattern, an

equipment failure risk pattern, etc.


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To identify patterns for groups of related process variables or equipment vital signs, patterns that

represent a particular process or equipment objective or target: quality, throughput, failure risk,

environmental risk, etc.

Same as above but relating the pattern to one or more process or equipment indexes, like soft

sensors, ex.: analytical values between Lab analysis, wear estimation, emission estimations, etc.

To identify the most influencing variables or vital signs for a specific operating point, giving an

insight for root cause analysis as well as the definition of control actions.

To use the pattern as a reference to detect in advance the risk of deviations, in quality, throughput,

emissions, failure or shut down risks, etc.

To use the pattern for the online estimation of critical process variables and equipment status and

vital signs.

To use the pattern model indexes aggregated measurements of the process or machine status for

quality, safety, efficiency, throughput, etc.,

To condense in one or few indexes the information about the variability of ten's of process variables

and vital signs, enabling powerful supervision and control applications development

9

99

9

999


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Get historical data related with thedesired goal

Build a multivariate pattern modelrelated with the desired goal

Identify the variability factors

Identify the weight and relativeinfluence of the VFs

Determine the Pattern ModelIndexes

Step 1: Off-Line Analysis

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DCS

PI System

Plant DataHistorian

Model

Engine

OR

OPC

Svr.

On-Line Visualization

of Model Indexes

SAP

Maximoothers

Alerts to MaintenanceSystems

Indexes valuesfeedback to ControlSystems

Same for Soft sensorestimators

Etc.

Step 2: On-Line Deployment

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VF_1

VF_2

VF_3

Pressure [psi]

Power [W]

Temperature [F]

Weight [Kg]

Current [A]

Flow [L/seg]Pressure [psi]

Power [W]

Temperature [F]

VF_1

VF_2

VF_1

VF_2

VF_1

VF_2

VF_1

VF_2

Linear Transform

(variable reduction) Mtodo:

Normalization of the empirical data

Maximun variability directions are determined

VF_1= p11 Press + p12 Pow + p13 Temp + p14 Weight + p15 Current + p16 Flow



VF_1

VF_2

Objetives:

Variable quantity reduction

To evaluate if the operation point belongs to apattern.

Time

Distance toEllipses Center

T2 Index

Theoretical Foundation: VFA

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Pressure [psi]

Power [W]

Temperature [F]

LSx_1

LSx_2

Flow [m3s]

Weight [Kg]

Current [A]

LSy_1

LSy_2

W1

W2

T

T

X T P E

Y U Q F

= +

= +

Set ofinput

variables

X

Set ofoutputvariables

Y

A reduction procedure,similar to the onedescribed for VF, isperformed.

Procedure is:

Fast: suitable for online use.

Reliable: cross correlationsare removed.

[ ]Y W X=

p1p2

q2

q1

Theoretical Foundation: PLS

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Increase of Mill Tonnage


The Problem:One Mill with less tonnage than others

The Solution: Find the most influence factors over the mill power and tonnage

Trend of tonnage mill 245 TPH Trend of tonnage respect to others mills.

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A Multivariate PLS model for the power was created, finding witch are themost influence variables over the process

Variable Peso Peso Absoluto %

Agua Fresca FIC1104-2 -0.28 0.28 23.83

Agua Dilucin FIC1164-05 -0.28 0.28 23.83Agua Colectores FIC1104-3 -0.2 0.2 17.1Nivel Cajn LIC1164-01 0.15 0.15 13.05

Tonelaje WIC1044-1 -0.14 0.14 11.68Agua Recuperada FIC1104-1 -0.11 0.11 9.73Presin Ciclones PI1174-01 0.01 0.01 0.79

The PLS models for the power

shows that we must to act over theFresh, Collector and dilution waterfor increase the mill power

Flujo

Normal

(LPM)

Flujo Corregido

con SCAN

(LPM)

Agua Colectores 814 643Agua Fresca 303 241Agua Diluci 455 4510

Doing this changes the milltonnage increase in 15 TPH byover 6 hours.

Regression coefficient of PLS Models :


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WithoutRecomendations

WithRecomendations

1.9 TM

96 TMMINERAL TONAGGE

TONNAGE

Production Increase

Cu Price US$ 2.55 / Lb

US$ 2 751

US$ 2 158

US$ 593Production Cost (US$ 0.55/Lb)

Earning ?

Net Earning


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On-Line Deployment

To the light of theexcellent obtained

results it was decided toimplement the modelson-line, at the presenttime this system give agroup of

recommendations to theoperator to optimize thequality variables of thesystem (tonnage, power,

%#+65, etc.)

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Hotelling

The fault could bedetected the 2 of

J uly

mainteniment Abnormaloperation fault

Analysed variables: Temperature,

Amperage, Pressure, Feed water,Power, etc.

Early Fault Detection

The 7 of July one of the main pumps of theplant had a critical fault that it meant a

considerable diminution of the productionduring 18 hours.

The VFA analysis of the data shows that 6days before the fault, multivarible indicators

showed the imminence of this one,identifying the root cause in addition.

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A model was built for the centrifugalpumps (many).

Model represents normal operating

conditions.

Deviation from the pattern is measuredby two Index T2and SPE.

A simple index condenses theinformation of 10 pump operationrelated variables

On-Line Deployment

Early alert for Pump malfunctioning canbe derived from the T2 and SPE Index

A detailed view for each Pump isavailable for root cause analysis.

NormalOperation

Abnormal

Operation NormalOperation

Mainteniment

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Compressor Operation Model Mapping

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ANALISIS MULTIVARIABLE Y

MONITOREO DINAMICO DEL PROCESO DEMOLIENDA

Jose Sanzana, Luis Yacher

CONTAC INGENIEROS LTDA.Fono: 56-2-270.09.00

Avda. Amrico Vespucio 315, uoa, [email protected], [email protected]

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