Lpm 3_2 Introduction

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cpmPlus Loop Performance Manager 3.2

Introduction to LPM

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Presentation Outline

Introduction / Motivation

cpmPlus LPM Features Tuning

Control Performance Monitoring

Supporting Utilities

cpmPlus LPM Plant-wide Disturbance Analysis

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cpmPlus Loop Performance Manager

1. Introduction/Motivation1. Introduction/Motivation

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Why Loop Performance Monitoring?

“Does my plant run optimally?” If not, how much can be accounted to

the process automation, especially the control loops?”

We should use available measurement data instead of just storing it.

Normal operation does not necessarily mean optimal operation

Loop optimization saves money without new capital investments

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Real world performance is suboptimal!

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An investment that has to pay off!

Typical control loop as a $25,000 asset Half of it is lost

50 % well tuned 25 % uneffective control 25 % decrease performance

Half time of good performance = 6 months 2 – 4 hours to investigate and improve control

performance Typical process contains 2000 – 4000 control loops Only few people with appropriate know-how Average process engineer in charge of 400 control

loops 25 % of 4000 loops do harm, this means…

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Analysts start to get the message

Quotes: “…while process equipment is an integral part of AM [asset management] programs, control

loops … often don’t receive the same attention.”

“Performance of control loops … degrades slowly over time with little fanfare.”

“Without properly tuned control loops to minimize variability, … substantial benefits are lost.”

“… even a slight degradation in process control can result in millions of dollars in lost profitability.”

“Identifying the high-payback control loops requires evaluating all control loops, which would be an insurmountable task without the aid of control loop performance monitoring and analysis software.”

“When first installed, advanced process control typically provides substantial benefits. Sustaining those benefits due to changing conditions, however, has been a problem.”

“… it’s a good time to ensure control systems are part of your AM efforts.”

Recent issue:

“Include control loops in asset management”

Les A. Kane, Editor

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Benefits of Tuning and Auditing

Maintains control system at its peak

Loop Tuning

Enables the plant engineers to reach loops optimum performance with significant time savings (vs. manual tuning)

Loop Auditing

Provides timely indication of equipment/automation/process problems. In this way it easy to keep the loop at their , allowing to stay at the optimal performance

Also, it provides stable foundation for multivariable/advanced control

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cpmPlus Loop Performance Manager – What is it?Loop Tuning Challenge

Optimal PID Tuning is critical to efficient process operation Loop Tuning is a time consuming activity Typically, only expert engineers can perform Tuning

Solution LPM Tuning makes definition of optimal PID parameters an easy,

reliable & manageable task

Loop Auditing Challenge

Loop optimization is frustrating, because after few months all results seem lost due to the process variability

Plant engineers have to look at hundreds of signals and among them detect possible problems

Solution Once Loop Optimization is performed, LPM Auditing monitors loops and

allows the process engineer to immediately address problem areas

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Cost of bad control

High

LowTime

Dream

Co

st

High

LowTime

Reality

Co

st

High

LowTime

Realistic dreamwith Auditing

Co

stLoop Tuning Execution

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2. LPM Features2. LPM Features

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LPM Tuning – Workflow Which step to tune a Loop?

Configure

Collect

Model Tune

Log

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LPM Features – Data Collection Configure database

by loops

Simultaneous data collection for multiple loops

OPC connectivity

Direct connection for Infi90/Symphony

Data collections stored as object on navigation tree for future retrieval

Possibility to exploit auditing automatic data collection for tuning purposes

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LPM Tuning - Identification BASIC for not experts and

ADVANCED with fully scalable complexity for expert control engineers

BASIC

ADVANCED

Manual or Automatic structure selection by best fit

Parameters specified - up to 4th order

Identification also with Process in Close Loop

Validation

Model simulated with another data set

Evaluation

Ideal step response

Bode diagram

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LPM Tuning

5 Tuning methods available Time domain analysis

Frequency analysis

Support many vendor specific PID controller types

Ability to model, tune, and analyze Feedforward control loops. Considers feedback tuning.

Special treatment of Cascade control loops

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LPM Advanced Tuning Features

New Tuning values can be assessed on model different from the ones used to obtain the tuning set (Simulate Mode)

Data pre-processing functionalities

Advanced Feedforward Loop Tuning Management

HTML-based and information-richer Tuning Logs

Advanced Cascade Loop Tuning Management

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LPM Tuning – Advantage State of the art Tuning Algorithm, but with

user-friendly tool to make Advanced Control Theory accessible to every Plant Engineers

Ready for every DCS OPC connection

Calculated PID parameters (Kp, Ti, Td ) with the definition of your DCS

Identification also with Loop in normal Close Loop Mode

Not only basic PIDs, but also FeedForward and Cascade Loop

Control Tuning becomes easy, fast, profitable

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Performance Assessment: Tuning vs. Auditing

Tuning - Design stage Assessment stage

Reasonable design

Slightly tight design

?

Is this good control?

If not: why?

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Control loop monitoring – non-invasive!

indices

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LPM Auditing - General concept

based on available signals only(SetPoint, PV, CO)

available information can be incorporated

performance indices, measures

inference engine

suggest remedies

know howInfo

Hypothesis, Diagnosis

know how

I1, I2, I3, …

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Kinds of Performance Indices in LPM

Basic statistics

Data Validity

Control loop modes

Tuning Performance indices

Oscillation indices

Valve indices

Measurement“PV”

Measurement“PV”

Target“SP”

Target“SP”Controller

Output “CO”

ControllerOutput “CO”

Nonlinearity indices Property indices Housekeeping Special indices Continuous indices

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Kinds of Diagnoses in LPM

Performance indicesPerformance indices

Auditing RulesAuditing Rules+ Maintenance DiagnosesMaintenance Diagnoses

Indices plus know-how organized in a Root-Cause analysis elaborate Maintenance Suggestions

Diagnoses dealt with problems in: Tuning, Actuators and Sensors, External disturbance

Diagnoses

Tuning Problem

Loop Oscillatory

SetPoint oscillatory

Significant external disturbance

Significant non-linearity

Valve stiction

Valve leakage or zero error

Valve size incorrect

Excessive valve action

Data unrealiable

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Overall Performance

Acceptable performance indexHarris index

Acceptable setpoint crossings indexSetpoint crossing index (not for Level Control)

Variability randomOscillation index of control error

Controller output within rangeSaturation index

Loop automaticAutomatic mode index

Acceptable cascade trackingCascade tracking index (if in cascade)

Acceptable response speedACF to horizon index

Acceptable performance indexHarris index

Acceptable setpoint crossings indexSetpoint crossing index (not for Level Control)

Variability randomOscillation index of control error

Controller output within rangeSaturation index

Loop automaticAutomatic mode index

Acceptable cascade trackingCascade tracking index (if in cascade)

Acceptable response speedACF to horizon index

Acceptable Overall performanceAcceptable Overall performance

excellent

good

fair

poor

PRECONDITIONS

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Auditing workflow

Loop configurationAssign• TAG connection• Signal ranges• Loop Type

Auditing configurationAssign• Data collection schedule• Batch / continuous auditing

Loop category configurationAssign• Sampling rate• Batch duration

Report configurationAssign• Report layout

Configuration file

Configuration

Indices ReportsExcel, HTML

Diagnoses ReportExcel, HTML

Indices Trend PlotOutputPeriodical reports

Maintenance Operator•Repair device•Tuning

Process Engineer•Investigate Problem•Activate Maintenance Maintenance

Start auditing

DatabaseData collectionIndices calculation

Setpoint CO,PV

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Example oscillation investigation...

F

FC

static friction

cycling load

tight tuning

Diagnoses

Verify overall Performance

Detect oscillation

Decide among the 3 causes

Indices

Oscillation details (period, amplitude…)

Amount of problem for every causes

Trend plot for every index

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LPM Auditing - KPI Reporting & Analysis

Reporting

Pre-defined report templates

Both numerical and chart-based assessment

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Advanced Auditing Features

Advanced Indices & Diagnosis trend facility (on multiple even non consecutive periods)

User-defined Indices

Enhanced KPI and Diagnosis set

Server Status Monitor to supervise all the auditing functions

“What Is Changed” report to immediately eye-catch recently developed events

Possibility to generate a “Detailed Loop” Report, with in-depth charts and numerical figures

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Detailed Report Time domain view

(PV,SP,CO)

Power spectrum view (PV)

Statistical view (PV, CE)

CE vs. CO, during oscillation becomes a ring. From the shape it is possible to detect stiction

Impulse response of Disturbance Rejection

Sensitivity study for Prediction horizon (good situation when lines is increasing with steps)

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And More …

Operation-Sensitive Reports: allow to monitor control loops according their operating region(s)

Examples: production campaign types, loads, …

Capability to extract and utilize for Tuning purposes data automatically collected during Auditing normal operation

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Bulk Database Import for quick DB Configuration

Allows to import tag configuration details from Excel spreadsheets

Results in Relevant Manpower Savings

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Infi90/AC800F Bulk Import Tool

Available as an add-on to standard LPM Functions

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LPM auditing - Everything also by Web

Facility to get and manage all LPM

information from any location in the net

From the LPM Home Page it is possible to navigate to …

… Reports Configuration …

… Reports Retrieval …

… Tuning Logs

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LPM Auditing: Advantages Automatic data-collection enable actual

continuous loop performance assessment rather than “sporadic sampling”, maximizing the chance to identify and correct insurgent production-related problems

Simple, straightforward diagnostic indications are made available for the basic user or for quick assessment

Diagnostic results are based on sophisticated indices which are able to provide explanations or in depth analysis for advanced user or when needed

Both Diagnosis and Indices are saved and stored in user-configurable Reports so to not require continuous attention from plant crew and to provide a comprehensive “plant history” track record

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3. Plantwide Disturbance 3. Plantwide Disturbance AnalysisAnalysis

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Plant-wide disturbance analysis - intro

Analysis process data off-line

Searches for data pattern in time (oscillations) and frequency (specra) to identify Oscillations

Interactions

Identifies most likely root-cause (with no info on plant topology/interconnections)

Integrated in LPM, could use auditing data or external data (e.g. plant historian)

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FC

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FC

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FC

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TI

TI

FC

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LC

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Internal Condenser

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TI

PI

TI

TC

Steam Steam

Co

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Internal Condenser

PDI

TC

TI

TI

TI

TI

PI

PDI

DecanterLC

TC

TI

TCSteam

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PDA Application – Case 1

Cascaded Distillation Columns:

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PDA Application – Case 1: Dataset Details

Primary cycle Column 1 level through column

2 distillate

Cause is LC2 valve movement problem

Many variables cycling together

Secondary cycle Top of column 1 (distillate

FC2 and temperatures)

Cause is FC2 valve movement problem

96 hours total data, sample time = 30 sec

Dataset window chosen

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PDA Application – Case 1: Clustering

Three main Clusters detected: Two Oscillation

Clusters

One PCA Cluster

A few tags have been added to clusters due to process considerations

Oscillation Clustering: manually added 1 related tag to grouping (primary cycle)

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Clusters

One PCA Cluster


Default grouping: secondary cycle, had to add ti2.pv and ti3.pv tags manually

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Clusters

One PCA Cluster


PCA cluster default grouping, manually added 2 related tags to grouping (primary cycle)

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FC

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FC

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TI

TI

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Internal Condenser

Co

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TI

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TI

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Steam Steam

Co

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Internal Condenser

PDI

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TI

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PDI

DecanterLC

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TCSteam

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PDA Application – Case 1: Main Clustered Disturbances

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Good default results for non-linearity analysis (primary cycle) (ranks LC2 as highest non-linearity)

PDA Application – Case 1: Root Cause Analysis

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FC

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FC

FC

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TI

TI

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Internal Condenser

Co

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TI

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TI

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Steam Steam

Co

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Internal Condenser

PDI

TC

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TI

TI

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PDI

DecanterLC

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TCSteam

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PDA Application – Case 1: Disturbance Propagation

Cluster 1Cluster 2

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LC

Liqu

id

LC

PC

Liqu

id

LC

FC

Liqu

id

Steam

LC

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Liqu

id

A B DC

SteamSteamSteam

7 PI

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PI

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Vapor Header

PC1

SP

PC

PDA Application – Case 2

Vaporizer System:

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8 Good results for non-linearity, clearly identifies LC2 as root cause


Ref. to: “Peak Performance: Root Cause Analysis of Plant-

wide Disturbances”, ABB Review 1/2007

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cpmPlus - LPM Conclusions

Tuning With LPM Process Engineers (also

non expert in control theory) can optimize Loop behavior

Benefits: increase process profit, more stable working condition, more safety operations

PDA Very valuable insight on process

corrrelations, oscillations and root causes with a few points and click

Could use your historian data (with reasonable data compression)

Complementary to tuning and auditing

Auditing Control Performance Monitoring is

non-invasive, simple to perform and very efficient

LPM detects automatically problem at the beginning of their occurrence

Performance monitoring nowadays answers the most important questions to help the plant personnel to pinpoint and remove problems

The right information to the right people

Lpm 3_2 Introduction

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Transcript of Lpm 3_2 Introduction