Field Experience in Property Estimation DeltaV Neural has been used in a variety of applications as...

Field Experience in Property Field Experience in Property EstimationEstimation

DeltaV Neural has been used in a variety of applications as a soft sensor for property estimation. Also, the estimated property may be used in closed loop control applications. In this short course we will present the features of DeltaV Neural, some of the implemented applications, and also some of the challenges and issues faced in developing a soft sensor. Dynamic simulation will be used to illustrate how a property estimator may be easily created from operating data.

PresentersPresenters

• Ashish Mehta

• Lou Heavner

• Nathan Camp

OverviewOverview

• Introduction – DeltaV APC and Soft sensors• DeltaV Neural features• Demo• Installation examples• Emerson services – Lou Heavner• Experiences with a real implementation – Nathan Camp• Q/A

DeltaV advanced control

• Embedded in DeltaV

• State-of-the-art technology

• Expands and improves process control tool set

• Available redundancy

• EASY to implement

• EASY to maintain

• EASY to justify

DeltaV Advanced ControlDeltaV Advanced ControlWhat’s Different?What’s Different?

Classic Advanced Control

DeltaV Function Block – Foundation DeltaV Function Block – Foundation Fieldbus ApproachFieldbus Approach

Function Blocks Support Mode

Function Block Inputs and Outputs Provide an Engineering Unit Value AND Status Standard Deviation is automatically calculated

What is a soft sensor?What is a soft sensor?

Plant DCS

& Historian

143.0 ppm

ANALYSISIn the lab,or automatic on a frequency

Samples

Results

At a fixed PeriodorDelayed

A model (generally nonlinear) of a process to predict a lab result or to fill in the gaps between sample points from an automatic sampling sensor.

S

T T

F

F Amp

T

Measurements Used In Constructing NN

Kappa Prediction For Outlet Stream

Example - Kappa AnalysisExample - Kappa Analysis

– Continuous Digester is a thermo chemical process - Time delay of + 4 hours

– On-line measurements of Kappa difficult - inaccurate, unreliable - 1 to 2 hours between off-line feedback analysis

Model Results

65

75

85

95

105

115101

110

119

128

137

146

155

164

173

182

191

200

Test Record Number

Actual

Predicted

Example - Model Results vs. LabExample - Model Results vs. Lab

Target ApplicationsTarget Applications Predict critical process measurements available only

through lab analysis (paper, food properties)

Continuous indication of measurements available only infrequently from sampled analyzer (gas chromatograph)

Provide real-time online predictions

Reduce process variability, improve control

Validate/backup sampled or continuous analyzers (mass spectrometer, stack analyzer).

TransferFunction

X2

X1X3

Wj1

Wj2

Wj3

Yj

1

-1

Non-linear Transfer Function

I W X

Ye

e

j ji ii

N

j

I

I

j

j

1

1

1

Neural Network is Built From NeuronsNeural Network is Built From Neurons

Three layer feed-forward Neural netThree layer feed-forward Neural net

X1

X2

Xi

XN

1 1

y

Output Layer

Hidden Layer

Input Layer

W11S1

h1

Wij

Sjhj

TdN

i1

i2

ii

iN

Delay to Address Dynamics

Tdi

Td2

Td1

Continuous indication for both: lab analysis and analyzer based measurements

Ease of use – integration, creation and commissioning

NN for the process engineer, not the Neural Guru

Adapt to process drifts and changes

Improve maintainability and reduce cost

‘If-then’ analysis of process change

Improve the bottom line, save some $$$

DeltaV Neural ObjectivesDeltaV Neural Objectives

DeltaV NeuralDeltaV Neural

– Practical means of creating virtual sensors for measurements that are only available through lab analysis today

– Easy to understand and use

– Data-based, cost effective

– General nonlinear approach

– Easy to update

Step 1a: Configure NN Function BlockStep 1a: Configure NN Function Block

Lab Analysis

Analyzer Measurement

References a maximum of 20 process measurements for analysis

Step 1b: Data CollectionStep 1b: Data Collection

SAM PLE

D ELAY

FOLLOW

OU T

FU TU R E

NN

#2

OU T

D ELAY

LE

#1

•Access data from anywhere within the system•Automatic assignment to historian

Step 2: Data selection and screeningStep 2: Data selection and screening

Step 3: Input Delays and SensitivityStep 3: Input Delays and Sensitivity

Step 3: Detail of Input Sensitivity Step 3: Detail of Input Sensitivity

Step 4: Network training Step 4: Network training

Number of hidden nodes automatically determined

Step 5: Model ValidationStep 5: Model Validation

Is the Model Good?

NN Block – Operator viewNN Block – Operator view

Lab Entry - Sample Value & TimeLab Entry - Sample Value & Time

Demo - Kamyr Digester Process Demo - Kamyr Digester Process

IT 1-1

FT 1-2

TT 1-7

TT 1-8

ST 1-4

Flash Tank

Heating Zone

Cooking Zone

Wash Zone

Main Blow

AY1-2

OutletDevice

Cold Blow

High Pressure Feeder

Chip Bin

Steaming Vessel

White Liquor

Kappa Analysis

HeatersHeater

FT 1-5

FT 1-6

TT 1-3 FT

1-3

Demo - Digester Kappa Prediction Demo - Digester Kappa Prediction

Soft Sensor

Inputs

StatisticalBias CorrectionLab results

Prediction

CV prediction

VOA estimatesshould be biasedwith Lab data

Use laboratory feedback tobias the soft sensor to keep it accurate.

On Line Error CorrectionOn Line Error Correction

Online Operation: Adaptive NN BlockOnline Operation: Adaptive NN Block

SAMPLE

DELAY

OUT

FOLLOW

FUTURE

CORR_FILTER

MODE CORR_LIM

0

I O O O

o

Feedforward Neural Net Model

OUT_SCALE

Delay

Limit Filter

+ CORR_BIAS

CORR_ENABLE

+INPUTS

Future PredictionFuture Prediction

• Trained Neural Network block automatically provides a predicted output into the future - ‘FUTURE’ along with OUT.

• Calculated by setting the input delays to zero - steady state solution for the given input values.

• Make immediate corrections for input changes.

• Perform ‘what-if’ analysis.

• Extremely valuable for processes with large delay time.

Automatic adaptation responseAutomatic adaptation response

Bias Value Changed

NN Out

Lab ValueFuture

Simple Control with DeltaV NeuralSimple Control with DeltaV Neural

DeltaV Neural Model output as PV of a PID controller

Regulatory Controls

Bleach ChemicalDosage Target

Bleach Chemical Flow Setpoint calc.

Kappa FactorControl

ChemicalStrength

ProductionRate

Operator Adjustment

Unbleached kappameasurement

KF Target

APC with DeltaV Neural?APC with DeltaV Neural?

Regulatory Controls



Kappa FactorControl

ChemicalStrength

ProductionRate

Operator target(DEK or brightness)

MPC


KF Target

Neural netInputs

Analyser or Lab test

APC with DeltaV NeuralAPC with DeltaV Neural

DeltaV Neural - Control Engineering’s 2001 DeltaV Neural - Control Engineering’s 2001 Editors Choice AwardEditors Choice Award

DeltaV NeuralReceives recognition for technological advancement, service to the industry, and impact on the control market.

March ’02 Issue of Control Engineering Magazine.

Creating Virtual Sensors with neural network technology has never been this easy!

DeltaV Neural - Control Magazine’s DeltaV Neural - Control Magazine’s Readers Choice AwardReaders Choice Award

Software, Neural Network

1. Emerson's DeltaV Neural

2. Pavilion Technologies

• Paper Machine Soft Sensors (Offline)– Developed a model for CONCORA (strength property) on a

medium liner board machine. – Developed a model for STFI (strength property) on a linerboard

machine. – Developed models for brightness and opacity on a fine paper

machine.

• The objective of the effort was to compare DeltaV Neural with other neural modeling tools. All of the applications were from models that were existing and had been operating for over a year. The results very closely correlated with each other.

Application: NuSoft TechnologiesApplication: NuSoft Technologies

HoleRefiners

TicklerRefiners

HOLE-HPDT

62AR129

pH

HOLEFLOW

FREE255

TICKLER-HPDTFREE355

M/cChest

WETAGENT

Stuff Box

CN219

Press

Dryer

Reel

TS-FLOW

HB-LEVEL

PIC203TH

WIRESPD

2HB1-CTRLSLICEOPEING

IN

COUCHVAC

CDSTMUSEPIC901RP-SETP

ARTONHBASISWTMOISTURE

Concora(Lab Delay)~ 45 mins

HDStorageTank

HDStorageTank

Application: Concora MeasurementApplication: Concora Measurement

62AR129HOLE-HPDTTickler-HPDTWETAGENTTS-FLOWPIC203TH2HB1-CNTRLCOUCHVACCDSTMUSEBASISWT

HOLEFLOWFREE255FREE355CN219HB-LEVELSLICEOPENINGWIRESPDPIC901RP-SETPARTONHMOISTURE

Concora

(Online) 5 mins


Application: Sasol AgriApplication: Sasol Agri

• 2 Phosphoric Acid Plants• 5 Evaporators on Each Plant• DeltaV/AMS/Devicenet MCC• Rosemount Hart Based Field

FIC1115-1

TIC1103

PIC1104

LIC1113

PC-J3404 AM

CONDENSORCONDENSOREVAPORATOREVAPORATOR

DensityDensity

TI1120

ACID STORAGEACID STORAGE

• Measure SG• Control Evap SG • Controlling retention in Evap • SG or Concentration ( 1.3 to

1.8)

RULESRULES


• Density Temp• Evap Vacuum • Heater Outlet Acid Temp• Heater Acid Inlet Temp• SG Lab Entry


Application: Georgia-Pacific Corp.Application: Georgia-Pacific Corp.

• Kamyr Digester Soda Loss Model (Offline)– Developed a model for soda loss in a Kamyr digester.

• The objective of the effort was to use DeltaV Neural to develop a model and properly identify the time delay between the dilution factor controlled variable and soda loss.

• Did a very good job of properly identifying the dead time.

• Was very easy to use compared to other tools available.

Applications: ErgonApplications: Ergon

• Refinery application – atmospheric crude column– SR Naphtha Endpoint– AGO Endpoint

• Refinery application – vacuum crude column– Wax Distillate 95% point

Naphtha

Kero

Hvy Kero

AGO

Resid to VAC Column

FC

FC

FCFC

FC

FC

FC

FC

TC

Crude

Fuel Gas

TCPredicted NA End Point

Predicted AGO End

Point

Column Temps & Yields

Column Temps & Yields

Applications: Ergon, Atm ColumnApplications: Ergon, Atm Column

TC

VGO

Wax Dist

Hvy Wax Dist

VAC Resid

FC

FC

LC

FC

FC

FC

FC

Atm Btms

Fuel Gas

TC

FC

VAC P/A

PC

TI

TI

Predicted Wax

Distillate 95% Point

Column Temps& Yields

Applications: Ergon, Vacuum ColumnApplications: Ergon, Vacuum Column

More ApplicationsMore Applications• Phosphoric Acid Concentrator

– Triple Effect Evaporator– Predict Acid Concentration (Density)

• Lime Kiln– Residual Carbonate

• Coffee Roaster– Aroma (Temperature Target)

• Brewing– Diacetyl

• Bleach Plant– Extracted Kappa– Brightness

• What business objectives are we looking to affect?– Quality– Throughput– Yield– Environmental– Energy– Uptime

Neural Applications: Hunting TipsNeural Applications: Hunting Tips

• Continuous or batch chemical processes where the dynamic response of variables is important

• Processes that are non-linear in nature• Processes with significant cycle times• Key parameter dependent on upstream variables

which are measured in real-time• Any parameter that is sampled and analyzed• Any parameter measured online by analytical

equipment that needs validation/backup



• Specific Gravity• Composition• NOx emmissions• SOx Emmissions• Melt index• Vapor pressure• Cloud point• Pour point• Particle Size

• pH• Kappa• Diacetyl• Concora• Viscosity• Octane Number• Cetane Number• Etc…


• Ashish Mehta

• Lou Heavner

• Nathan Camp

Approach to Quality ControlApproach to Quality Control

• Where Analyzers are available (and reliable) use them for Controlled Variables ( and Disturbance Variables ).

• Use intermediate measurements to estimate Quality when Analyzers are not functioning.

• Develop Virtual Sensors when Online Analyzers are not practical

Introduction to Quality EstimatorsIntroduction to Quality Estimators

• Small Process Models that provide an indication of stream Quality from Process measurements.

• Applications:– When an Analyzer is not available.

– When an Analyzer is unreliable or in maintenance.

– When an Analyzer response is dynamically slow due to Analyzer sample processing time (eg, GLCs).

– Process equipment between where the Quality is determined and where the stream is available for sampling.

Purpose of Quality EstimatorsPurpose of Quality Estimators

• To assist in operations achieving Quality Targets and Quality Constraints using Lab Results as the feedback mechanism.

• To improve the performance of closed loop Quality Control.– FeedBack or FeedForward Control– Model Predictive Constraint Control

• To give Real Time Optimization a means to predict the Qualities resulting from its (potential) adjustments.

Quality Estimator FormulationQuality Estimator Formulation

• GENERAL FORMULA ...

– Quality = f ( Temperature, Pressure, Flow ) + Calibration Constant

– Many Estimators are a function of pressure compensated temperature.

• Function may be a simple constant term:

– E.g. K * ( Temperature )

– Some estimators are complex nonlinear functions

• Functions based on first principles

• Functions based on empirical data

– Statistical techniques

– Artificial Neural Networks

Modeling & Analysis ApproachesModeling & Analysis Approaches

– First principles-based models

– Statistical Approaches

– Nonlinear Regression

– Neural Networks

First Principles-based ModelingFirst Principles-based Modeling Based on physical and chemical relationships

Examples: Kinetics, Fluid flow, Thermodynamics

Based on decades of experience Can be highly accurate when process is well

understood and relatively stable Requires in-depth knowledge of process Does not account for process behavior changes

over time• Sometimes available through combustion unit

manufacturer

Statistical ApproachesStatistical Approaches• Techniques such as: Data analysis/curve fitting Regression techniques Probability analysis

• Require lots of data• Require understanding of statistical techniques• Better for analysis than modeling

Neural Network-based ModelsNeural Network-based Models Fairly new in the marketplace Practical Minimal process knowledge is necessary Easy to apply to a variety of applications Training requires good data Easily re-trained to adapt to new conditions Do not extrapolate well

Emerson ServicesEmerson Services

• Feasibility Analysis• Feasibility Study• Project Execution• Model Support

Feasibility AnalysisFeasibility Analysis

• Sensitivity Analysis• Existing Customer Data• No Site Visit• Outputs:

– Best model identified– Recommendations to improve model

• Option: Benefit analysis

Offline Sensitivity AnalysisOffline Sensitivity Analysis

• Try DeltaV Neural on real plant data– Gather Plant Historical Data– Use all available measurements (up to 20)– Include Lab Data– Train and Verify

• Voila!– It’s that easy…

Feasibility StudyFeasibility Study

• Site visit– Process review– Data collection planning

• Sensitivity Analysis• Outputs:

– Identified model– Implementation proposal

Project ExecutionProject Execution

• Implement DeltaV Neural– Feasibility study– DeltaV Configuration– Online model development

• Setup• Training• Testing

– Verification• Short term• Long term plan

Model SupportModel Support

• Model Updating & Retraining• Consulting

– Troubleshooting– Accommodating process and I&C changes– Using model in control strategies

Emerson Value AdditionEmerson Value Addition

• Familiarity with DeltaV Neural• Process Expertise• Neural Net Modeling Expertise

Leads to:• Faster Implementation• Lower Risk• Appropriate Application

– Alternative approaches considered– Taking the next step to control

Oops!Oops!

• I thought I had a good model…– But it doesn’t look so good on new data

• I thought I had lots and lots of data…– But the model isn’t as good as advertised– How much data do I really need

• I thought for sure that this variable was critically important…– But DeltaV Neural ignored it

Practical ConsiderationsPractical Considerations

• Data is the key– Correct time-stamps– Raw snapshot data - no data compression– Sufficient variability– Data Density – clustering and voids

• Don’t confuse correlation and causality

Data RequirementsData Requirements• DeltaV Neural can capture dynamics…

– but time stamps must be accurate• Time delays should be constant or compensated

– Selection of time to steady-state is critical• Auto-correlation can lead to unusual results

Data RequirementsData Requirements• Quality of empirical data

– Use raw (snapshot) data, avoid filtering and averaging– There must be variability and it should span the range of expected operation– Minimal Data Clustering and Data Voids– Signal to noise ratio must be high– Correlation vs. causality

Data RequirementsData Requirements• Quantity of empirical data

– More is usually better

Data RequirementsData Requirements• Know the process

– Avoid redundant information– Ensure dominant affects are incorporated– Use calculated variables (first principles based inputs)– Understand process dynamics

Common QuestionsCommon Questions

• How many samples do I need?– Technically

• Complexity (number of inputs and time to SS vs sample interval)

• Train vs test split & verify unseen data

– Practically• > 100 is good rule of thumb

• Why was this variable deselected?– Redundant– No variability– Too much noise– Bad measurements– Bad timestamps– Correlated w/out causality

TroubleshootingTroubleshooting• Verify views

– Predicted & Actual vs Sample• Identify trends• Identify nature of error (bias, peak offset, etc)

– Predicted vs Actual• Identify clustering and voids• Identify outliers

• Analysis w/ Excel (Pre-processing)– Plot variables

• Vs Time• Vs Actual• From least to greatest

– Statistical checks• Max, Min, Delta (span)• Mean, Median, midpoint• Standard Deviation & 6 Sigma

Controlling Product QualityControlling Product Quality

• Direct Analyzer : product property measured by On-line Analyzer.

• Inferential : product property inferred from product state or another product property.– Utilizes easy to measure states or properties to infer properties

that are difficult or impossible to measure on-line.• E.g. Temperature and pressure of vapor leaving top tray of a column

indicating composition of top product

– Provide redundancy for online analyzers with poor availability/reliability

Direct Analyzer ControlDirect Analyzer Control

• Pros of Direct Analyzer Control– Accuracy, good repeatability– NIR now available e.g for on-line octane– Reduces lab, work– Faster results than lab

• Cons of Direct Analyzer Control– Expensive– High level of mechanical maintenance required to retain accuracy– Sample extraction– Often non-continuous read-out.

Inferential ControlInferential Control

• Pros of Inferential Control– Inexpensive - No capital cost.– Less mechanical maintenance.– Continuous read-out– Faster to implement from scratch.

• Cons of Inferential Control– Models often inaccurate, particularly if non-linear.– Potentially high maintenance if no On-line Analyzer available ( i.e.

monitoring and updating of correlations )– Generally, test runs must be done to develop accurate relationships– Often limited rangeability.

Developing New ModelsDeveloping New Models• Monitor Model Performance

– Trend vs Lab Analyses• Identify if error is random or persistent• Identify source of error

• Update Model as Required– Correlation with New Data

• Short term variance > Adjust Bias• Long term variance > Recalculate Correlation (New Model)

• Test New Model– Verify Against Old Data– Continue to Trend vs Lab Data


• Ashish Mehta

• Lou Heavner

• Nathan Camp

IntroductionIntroduction

• Neural Networks – When to use them and when not to– Selecting Inputs– Data Robustness– Offline Training– Overview of SFK’s Neural Networks– Problems, Solutions, Troubleshooting, and Tools

When to use and when not toWhen to use and when not to

• When not to use a Neural Network– Process Models or Equations are already well

established

Selecting InputsSelecting Inputs

• Use as many inputs as possible. Unimportant inputs may be ignored.

• Inputs should not be related.• Use calculated values instead of raw inputs if

relationships are known.• Inputs must vary over the range in which the

Neural will be used.• Unmeasured Disturbances can hurt.

Data RobustnessData Robustness

• Inputs must vary over a range. The NN output is not valid outside the range of training.

SFK’s Neural NetworksSFK’s Neural Networks

• Two Neural Networks were required– Extracted Kappa– D1 Brightness

• DeltaV sits on top of Foxboro I/A• Communications via OPC• NNs provide feedback to MPC (Model Predictive

Control) loops.

System ArchitectureSystem Architecture

Extracted Kappa NNExtracted Kappa NN

• Analyzer Provides Sample every 15min.

• NN Generates a Continuous Output for MPC


• Look at the inputs


• Evaluate the Inputs

• Should make sense

• Adjust the time delays if necessary


• Train the NN


• Check the validity of the predictions.• This can be an iterative process

Error Checking and OverridesError Checking and Overrides

• NN Provides Signal to MPC for Control

• Check for Errors to provide Overrides

Problems Commissioning DeligProblems Commissioning Delig

• Initially, we could not get a good fit.– A couple of inputs were dependent (co-linear) on

other inputs. Eliminated these inputs and replaced with others.

– Also introduced calculated inputs where possible.


• Neural output unstable for MPC– Due to noise from the inputs. Added extra blocks to

allow the NN inputs to be filtered separately.


• Neural Net Output went uncertain– Major cause was inputs going outside the trained

ranges.– Retrained Neural with larger set of data. Needed to

use PI-Datalink to pull data out and combine multiple time periods into one file.

– Offline training with this data provided a more robust Neural Net.


• Neural Net Output went uncertain– Built tools to pinpoint the problem.– Build error checking into the configuration to look for

range issues and take action if an input causes a problem.

Model Based ControlModel Based Control

• Sets the Kappa Factor Target – Injects a preset

amount of ClO2 per ton of pulp.

– Biased by incoming Unbleached Kappa

– Corrected via Model Regulatory Controls



Kappa FactorControl

ChemicalStrength

ProductionRate

Manual ControlCyberBLEACHAPC


KF Target

Ext Kappa Results AchievedExt Kappa Results Achieved

• Reduced Variability

2.50

3.00

3.50

4.00

4.50

5.00

After APC Before APC

Time Based View

Brightness NNBrightness NN

• After the learning curve on the Extracted Kappa Neural, we were ready to attempt the Brightness Neural.

• Several attempts were made at getting the Neural Net to fit.

Could Not Achieve a Good Fit Could Not Achieve a Good Fit

• Statistical Hint – If the pattern looks like a shotgun blast, it is a bad thing.

ProblemsProblems

• Large Variations in Dead Times.• Time Stamping of Lab Entries.• Repeatability of Lab Tests.• Data rangeability poor over training set• Unmeasured Disturbances – due to not having

input measurements for all necessary variables - greatly affect the brightness .

Brightness NN Plan 2Brightness NN Plan 2

• Develop Dynamic Estimator based on published data.

• Modify Lab Test to provide minor biases to the Estimator.

Trouble Shooting ToolsTrouble Shooting Tools

• Excel Spread Sheet using both PI Datalink and DeltaV Excel Addin to Pinpoint Problems

Trouble Shooting ToolsTrouble Shooting Tools

• Process History View will give a good indication of dynamics.

Off Line TrainingOff Line Training

• The expert mode allows sensitivity analysis from .dat files.

• Provides capability to combine data from multiple time frames.

• Data Manipulation can clean up noise and unwanted disturbances.

What Lessons Were Learned?What Lessons Were Learned?

• Careful up front design time will save a lot of time later.

• Use care in selecting which data to use in training the Neural Networks.

• Time Stamping is extremely important even on slow acting processes.

• A Neural is a good tool provided prerequisites are available.

Problems and SolutionsProblems and Solutions

• Neural Network may need different filtering than other processes– Use Second Input (AI or Pseudo AI) to provide

secondary filtering.

• Output will be invalid outside the trained range– Check valid ranges and program error handling

Problems and SolutionsProblems and Solutions

• Historian does not hold enough information to cover full sets of inputs.– Increase Historian Archive capabilities by increasing

the number of archives and/or size of archives– Use PI Datalink or other tools to save data into Excel

spreadsheets. Combine data and use off line training

SummarySummary

• Neural Networks are a very powerful tool.• The Extracted Kappa Neural Net and associated

MPC provide a good solution for our customer.• The Brightness Neural Net attempt shows that

the NN is not a magic solution for all cases. In this case, the addition of instrumentation would have allowed the Neural to work.

• Questions???


• Ashish Mehta

• Lou Heavner

• Nathan Camp

DeltaV Neural – preview into futureDeltaV Neural – preview into future• Data pre-processing tools:

– Statistical info like mean, std. deviation for data sets– Input filtering– Calculations/transforms (e.g., log, exp) on inputs– Improved metrics for sorting data into test/train segments

• Improve input time delay and correlation analysis – use expert user inputs

• Training Limit handling: – Allow user entry– Indicate outliers and limits– Online operation should indicate violated variable– Applicable limits shown during online

DeltaV Neural – preview into futureDeltaV Neural – preview into future• Adding new data set for retraining, both graphical and

file data• Indication of sensitivity after training a model• Residual analysis: graphical, statistical• Output filtering - essential when used in control • Allow DELAY value of up to 72 hours, currently

limited to TSS (max. 24 hours)• Clearer indication for Batch processes

– end of batch quality prediction– prediction of end of batch time

• Enhance ease of use

DeltaV APC and TDC – Using OPCDeltaV APC and TDC – Using OPC

OPC serveron AMNT

DeltaV Workstation

With OPC Server

Operator Station (US or GUS)

DeltaVController

Serial I/F Options

OPC I/F

ControllerPM APM HPPM

FTA

Highway Gateway

IOP Modules

DeltaV APC and ProvoxDeltaV APC and Provox

OPC serveron Chip

Any Provox Operator Console

DeltaVController

Serial I/F Options

OPC I/F

Provox Controller

IDI Intelligent

Device Interface

DeltaV Workstation

With OPC Server

SummarySummary

• The capability of DeltaV Neural as an effective soft sensor has been demonstrated

• Application examples / advanced features• Value addition by Emerson solutions group • Real-world challenges and improvements• Further information:

– [email protected]– [email protected]– [email protected]

mailto:[email protected]












Course # 7202DeltaV PredictPro

Implementation

Course # 7203DeltaV Neural

Implementation

Course # 7201DeltaV Advanced Controls

Overview

DeltaV Neural and other DeltaV Advanced Control ProductsDeltaV Neural and other DeltaV Advanced Control Products

Overview - Courses 7201, 7202, & 7203

• These courses, beginning with the 7201, overview all of the major DeltaV advanced control tools. Courses 7202, & 7203 each drill deeper into a specific advanced control product and its application.

• DeltaV advanced controls are unique in the process control industry, in that users do not need detailed knowledge of the underlying mathematical principles to successfully apply the DeltaV advanced controls technology.

Learning More About DeltaV Advanced Learning More About DeltaV Advanced ControlControl

• Book was inspired by DeltaV Advanced Control Products. This book was introduced at ISA2002 may also be ordered through ISA, Amazon.com or at EasyDeltaV.com/Bookstore

• The application sections include guided tours based on DeltaV Advanced Control Products

• CD provides an overview video for each section and examples. Copies

of the displays, modules, and HYSYS Cases are included on the CD.

Field Experience in Property Estimation DeltaV Neural has been used in a variety of applications as...

Documents

Transcript of Field Experience in Property Estimation DeltaV Neural has been used in a variety of applications as...